JP2023113550A - Communication device, information processing method, and program - Google Patents

Abstract

To improve the accuracy of measuring a distance between a plurality of devices.SOLUTION: A communication device is provided, including a radio communication part for receiving a signal from another communication device by radio, and a control part for estimating an extension signal matrix for minimizing a prescribed norm by repeated calculation using a regularization parameter being a positive minute amount, and estimating a reception time of a signal received by the radio communication part on the basis of the extension signal matrix to minimize the prescribed norm. The control part divides the repeated calculation into a plurality of stages for execution, sets a value of the regularization parameter to be used for repeated calculation after a second stage among the plurality of stages to a value of the regularization parameter or more to be used in repeated calculation of the preceding stage, and changes the value of the regularization parameter on the basis of a reception situation of a second signal in the repeated calculation after the second stages.SELECTED DRAWING: Figure 15

Description

The present invention relates to a communication device, an information processing method, and a program.

In recent years, a technique has been developed in which one device identifies the position of the other device according to the result of transmitting and receiving signals between the devices. As an example of position specifying technology, Patent Document 1 below discloses a technology in which a UWB receiver specifies an incident angle of a radio signal from a UWB transmitter by performing wireless communication in UWB (Ultra-Wide Band). ing.

WO2015/176776

However, in the technique described in Patent Document 1, although the incident angle of the radio signal is specified, improving the accuracy of measuring the distance between the UWB receiver and the UWB transmitter is difficult. There was room for further improvement.

That is, in the technology for measuring the distance between one device and the other device, it is desired to further improve the measurement accuracy of the distance between those devices.

Accordingly, the present invention has been made in view of the above problems, and an object of the present invention is to provide a mechanism capable of improving the accuracy of distance measurement between a plurality of devices.

In order to solve the above problems, according to an aspect of the present invention, a wireless communication unit that wirelessly receives a signal from another communication device, and the other communication device transmits a signal including pulses as a first signal. A correlation between a second signal, which is a signal corresponding to the first signal and the first signal, received by the wireless communication unit when the A data matrix, which is a matrix in which one or a plurality of correlation calculation results, which are results of correlation between the second signal and the first signal, are arranged at each of the predetermined times, is received at each of a plurality of set times. an extension mode matrix that is a matrix consisting of a plurality of elements representing the correlation calculation result when it is assumed that the The extended signal vector, which is a vector consisting of elements, is converted to a format including a matrix product of the extended signal matrix, which is a matrix in which one or more of the correlation calculation results are arranged, and the regularization parameter, which is a small positive amount a control unit that estimates the extended signal matrix that minimizes a predetermined norm by iterative calculation using the , wherein the control unit executes the iterative calculation in a plurality of stages, and uses the value of the regularization parameter used in the iterative calculation in the second and subsequent stages among the plurality of stages in the iterative calculation in the previous stage. A communication device is provided in which the value of the regularization parameter is set to be equal to or greater than the value of the regularization parameter, and the value of the regularization parameter is changed based on the reception status of the second signal in the iterative calculations after the second stage.

In order to solve the above problems, according to another aspect of the present invention, when another communication device transmits a signal including a pulse as a first signal, the first The correlation between the second signal, which is the signal corresponding to the signal of the above, and the first signal is taken at regular intervals, and the correlation between the second signal and the first signal in the radio communication unit is calculated as the A data matrix, which is a matrix in which one or more correlation calculation results obtained at specified time intervals are arranged, is a plurality of elements representing the correlation calculation results when it is assumed that a signal is received at each of a plurality of set times. and an extension signal vector, which is a vector consisting of a plurality of elements representing the presence or absence of a signal in the wireless communication unit at each set time, and the amplitude and phase of the signal, one or more The extended signal matrix, which is a matrix in which the results of the correlation calculation are arranged, is converted into a format including a matrix product of the extended signal, and the extended signal minimizes a predetermined norm by iterative calculation using a regularization parameter that is a small positive amount. estimating a matrix and estimating the time of reception of the second signal based on the extended signal matrix that minimizes the predetermined norm, wherein the estimating comprises performing the iterative calculation in multiple stages. Execute separately, setting the value of the regularization parameter used in the iterative calculation of the second stage and later among the plurality of stages to be equal to or greater than the value of the regularization parameter used in the iterative calculation of the previous stage, and the second stage and later changing the value of the regularization parameter based on the reception status of the second signal in the iterative calculation of .

Further, in order to solve the above problems, according to another aspect of the present invention, when another communication device transmits a signal including a pulse as a first signal, the computer receives by the wireless communication unit, A correlation between a second signal, which is a signal corresponding to the first signal, and the first signal is taken every prescribed time, and a correlation between the second signal and the first signal in the wireless communication unit is obtained. A data matrix, which is a matrix in which one or a plurality of correlation calculation results, which are the results of taking the correlation at each of the specified times, is arranged, represents the correlation calculation results when it is assumed that the signal is received at each of a plurality of set times. An extension mode matrix, which is a matrix consisting of a plurality of elements, and an extension signal vector, which is a vector consisting of a plurality of elements representing the presence or absence of a signal in the wireless communication unit at each set time, and the amplitude and phase of the signal, are combined into one Alternatively, convert to a format including a matrix product of the extended signal matrix, which is a matrix in which the results of the multiple correlation calculations are arranged, and minimize a predetermined norm by iterative calculation using a regularization parameter that is a small positive amount. estimating the extended signal matrix, and functioning as a control unit that estimates the reception time of the second signal based on the extended signal matrix that minimizes the predetermined norm, and causing the control unit to perform the iterative calculation is executed in a plurality of steps, and the value of the regularization parameter used in the iterative calculations in the second and subsequent steps among the plurality of steps is set to be equal to or greater than the value of the regularization parameter used in the iterative calculations in the previous step, and A program is provided for changing the value of the regularization parameter based on the reception status of the second signal in the iterative calculations of the second and subsequent stages.

As described above, according to the present invention, there is provided a mechanism capable of improving the accuracy of distance measurement between a plurality of devices.

It is a figure showing an example of composition of a system concerning one embodiment of the present invention. It is a figure showing an example of arrangement of a plurality of antennas provided in vehicles concerning this embodiment. It is a figure which shows an example of the position parameter of the portable device which concerns on this embodiment. It is a figure which shows an example of the position parameter of the portable device which concerns on this embodiment. FIG. 4 is a diagram showing an example of processing blocks for signal processing in the communication unit according to the embodiment; It is a graph which shows an example of CIR which concerns on this embodiment. FIG. 4 is a sequence diagram showing an example of the flow of distance measurement processing executed in the system according to the embodiment; FIG. 4 is a sequence diagram showing an example of the flow of angle estimation processing executed in the system according to the embodiment; It is a graph for explaining the technical problem of this embodiment. It is a graph for explaining the technical problem of this embodiment. It is a graph for explaining the technical problem of this embodiment. It is a graph for explaining the technical problem of this embodiment. FIG. 4 is a diagram for explaining a case where four antennas constitute a 2×2 planar array; It is a figure for demonstrating the relationship between y(k) and y[i]. 5 is a flow chart showing an example of control of a regularization parameter according to the embodiment; FIG. 4 is an explanatory diagram for explaining an outline of an extended signal matrix Y according to this embodiment; 7 is a flowchart showing an example of the flow of iterative calculation using a regularization parameter and a convergence determination value according to the embodiment; FIG. 11 is a flow chart showing an example of control of regularization parameters according to the second embodiment; FIG. 4 is a flow chart showing an example of the flow of location parameter estimation processing executed by the communication unit according to the present embodiment; FIG. 4 is a diagram for explaining a case where four antennas form a linear array;

Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings. In the present specification and drawings, constituent elements having substantially the same functional configuration are denoted by the same reference numerals, thereby omitting redundant description.

In addition, in this specification and drawings, elements having substantially the same functional configuration may be distinguished by attaching different letters after the same reference numerals. For example, a plurality of elements having substantially the same functional configuration are distinguished like radio communication units 210A, 210B and 210C as necessary. However, when there is no particular need to distinguish between a plurality of elements having substantially the same functional configuration, only the same reference numerals are used. For example, the wireless communication units 210A, 210B and 210C are simply referred to as the wireless communication unit 210 when there is no particular need to distinguish between them.

<<1. Configuration example >>
FIG. 1 is a diagram showing an example of the configuration of a system 1 according to one embodiment of the invention. As shown in FIG. 1, the system 1 according to this embodiment includes a mobile device 100 and a communication unit 200. As shown in FIG. A communication unit 200 in this embodiment is mounted on a vehicle 202 . A vehicle 202 is an example of a user's utilization target.

The present invention involves a communication device on the side of the person to be authenticated and a communication device on the side of the authenticator. In the example shown in FIG. 1, the portable device 100 is an example of the communication device on the side of the person to be authenticated, and the communication unit 200 is an example of the communication device on the side of the authenticator.

In system 1, when a user (for example, a driver of vehicle 202) carries portable device 100 and approaches vehicle 202, wireless communication for authentication is performed between portable device 100 and communication unit 200 mounted on vehicle 202. Communication takes place. Then, when the authentication is successful, the door locks of the vehicle 202 are unlocked, the engine is started, and the vehicle 202 becomes available for use by the user. System 1 is also referred to as a smart entry system. Each component will be described in order below.

(1) Portable device 100
Portable device 100 is configured as any device carried by a user. Optional devices include electronic keys, smartphones, wearables, and the like. As shown in FIG. 1, the portable device 100 includes a wireless communication section 110, a storage section 120, and a control section .

The wireless communication unit 110 has a function of performing wireless communication with the communication unit 200 mounted on the vehicle 202 . Wireless communication unit 110 receives wireless signals from communication unit 200 mounted on vehicle 202 and transmits wireless signals.

Wireless communication between the wireless communication unit 110 and the communication unit 200 is realized by signals using, for example, UWB (Ultra-Wide Band). In wireless communication of signals using UWB, if the impulse method is used, the propagation delay time of radio waves can be measured with high accuracy by using radio waves with extremely short pulse widths of nanoseconds or less. Time-based ranging can be performed with high accuracy. It should be noted that the propagation delay time is the time it takes for a radio wave to be received after it is transmitted. The wireless communication unit 110 is configured as a communication interface capable of UWB communication, for example.

Signals using UWB can be transmitted and received as, for example, ranging signals, angle estimation signals, and data signals. The rangefinding signal is a signal that is transmitted and received in rangefinding processing, which will be described later. The ranging signal may be configured in a frame format without a payload portion for storing data, or may be configured in a frame format with a payload portion. The angle estimation signal is a signal that is transmitted and received in angle estimation processing, which will be described later. The angle estimation signal may have the same configuration as the ranging signal. The data signal is preferably constructed in a frame format having a payload portion for storing data.

Here, wireless communication section 110 has at least one antenna 111 . The wireless communication unit 110 transmits and receives wireless signals via at least one antenna 111 .

The storage unit 120 has a function of storing various information for the operation of the portable device 100 . For example, the storage unit 120 stores a program for operating the portable device 100, an ID (identifier) for authentication, a password, an authentication algorithm, and the like. The storage unit 120 is configured by, for example, a storage medium such as a flash memory, and a processing device that executes recording/reproducing to/from the storage medium.

The control unit 130 has a function of executing processing in the portable device 100 . As an example, the control unit 130 controls the wireless communication unit 110 to communicate with the communication unit 200 of the vehicle 202 . The control unit 130 reads information from the storage unit 120 and writes information to the storage unit 120 . Control unit 130 also functions as an authentication control unit that controls authentication processing performed with communication unit 200 of vehicle 202 . The control unit 130 is configured by an electronic circuit such as a CPU (Central Processing Unit) and a microprocessor.

(2) Communication unit 200
Communication unit 200 is provided in association with vehicle 202 . Here, it is assumed that the communication unit 200 is mounted in the vehicle 202, such as being installed in the vehicle interior of the vehicle 202, or built into the vehicle 202 as a communication module. In addition, the vehicle 202 and the communication unit 200 may be configured separately, such as by providing the communication unit 200 in the parking lot of the vehicle 202 . In that case, the communication unit 200 can wirelessly transmit a control signal to the vehicle 202 based on the communication result with the portable device 100 to remotely control the vehicle 202 . As shown in FIG. 1, the communication unit 200 includes a plurality of wireless communication sections 210 (210A to 210D), a storage section 220, and a control section 230. FIG.

The wireless communication unit 210 has a function of performing wireless communication with the wireless communication unit 110 of the portable device 100 . The wireless communication unit 210 receives wireless signals from the mobile device 100 and transmits wireless signals to the mobile device 100 . The wireless communication unit 210 is configured as a communication interface capable of UWB communication, for example.

Here, each wireless communication unit 210 has an antenna 211 . Each wireless communication unit 210 transmits and receives wireless signals via an antenna 211 .

The storage section 220 has a function of storing various information for the operation of the communication unit 200 . For example, the storage unit 220 stores a program for operating the communication unit 200, an authentication algorithm, and the like. The storage unit 220 is configured by, for example, a storage medium such as a flash memory, and a processing device that executes recording/reproducing to/from the storage medium.

The control unit 230 has a function of controlling the overall operation of the communication unit 200 and onboard equipment mounted on the vehicle 202 . As an example, the control unit 230 controls the wireless communication unit 210 to communicate with the portable device 100 . The control unit 230 reads information from the storage unit 220 and writes information to the storage unit 220 . The control unit 230 also functions as an authentication control unit that controls authentication processing performed with the portable device 100 . The control unit 230 also functions as a door lock control unit that controls the door locks of the vehicle 202, and locks and unlocks the door locks. The control unit 230 also functions as an engine control unit that controls the engine of the vehicle 202, and starts/stops the engine. Note that the power source provided in the vehicle 202 may be a motor or the like in addition to the engine. The control unit 230 is configured as an electronic circuit such as an ECU (Electronic Control Unit).

<<2. Technical features >>
<2.1. Position parameter>
The communication unit 200 (specifically, the control unit 230) according to the present embodiment performs position parameter estimation processing for estimating a position parameter indicating the position where the portable device 100 exists. Various definitions of the positional parameters will be described below with reference to FIGS. 2 to 4. FIG.

FIG. 2 is a diagram showing an example of arrangement of a plurality of antennas 211 (radio communication units 210) provided in a vehicle 202 according to this embodiment. As shown in FIG. 2, four antennas 211 (211A-211D) are provided on the ceiling of the vehicle 202. As shown in FIG. Antenna 211A is provided on the front right side of vehicle 202 . Antenna 211B is provided on the front left side of vehicle 202 . Antenna 211C is provided on the rear right side of vehicle 202 . Antenna 211D is provided on the rear left side of vehicle 202 . Note that the distance between adjacent antennas 211 is set to be less than or equal to half the wavelength λ of the angle estimation signal, which will be described later. A local coordinate system of the communication unit 200 is set as a coordinate system based on the communication unit 200 . An example of the local coordinate system of the communication unit 200 has the center of the four antennas 211 as the origin, the longitudinal direction of the vehicle 202 as the X axis, the lateral direction of the vehicle 202 as the Y axis, and the vertical direction of the vehicle 202 as the Z axis. It is a coordinate system that Note that the X-axis is parallel to the axis connecting the front-to-rear antenna pairs (eg, antenna 211A and antenna 211C, and antenna 211B and antenna 211D). In addition, the Y-axis is parallel to the axis connecting the left and right antenna pairs (eg, antenna 211A and antenna 211B, and antenna 211C and antenna 211D).

Note that the arrangement shape of the four antennas 211 is not limited to a square, and may be a parallelogram, a trapezoid, a rectangle, or any other shape. Of course, the number of antennas 211 is not limited to four.

FIG. 3 is a diagram showing an example of position parameters of the portable device 100 according to this embodiment. Location parameters may include the distance R between the mobile device 100 and the communication unit 200 . A distance R shown in FIG. 3 is the distance from the origin of the local coordinate system of the communication unit 200 to the portable device 100 . The distance R is estimated based on the result of transmission/reception of a ranging signal, which will be described later, performed between one wireless communication unit 210 of the plurality of wireless communication units 210 and the portable device 100 . The distance R may be the distance from one wireless communication unit 210 that transmits and receives a ranging signal to be described later to the portable device 100 .

The position parameters also include the angle of the portable device 100 with respect to the communication unit 200, which consists of the angle α from the X-axis to the portable device 100 and the angle β from the Y-axis to the portable device 100 shown in FIG. obtain. The angles α and β are angles formed between the coordinate axes and a straight line connecting the origin of the first predetermined coordinate system and the portable device 100 . For example, the first predetermined coordinate system is the local coordinate system of communication unit 200 . The angle α is the angle between the straight line connecting the origin and the portable device 100 and the X axis. The angle β is the angle between the straight line connecting the origin and the portable device 100 and the Y axis.

FIG. 4 is a diagram showing an example of position parameters of the portable device 100 according to this embodiment. The location parameters may include coordinates of portable device 100 in a second predetermined coordinate system. A coordinate x on the X axis, a coordinate y on the Y axis, and a coordinate z on the Z axis of the portable device 100 shown in FIG. 4 are examples of such coordinates. That is, the second predetermined coordinate system may be the local coordinate system of communication unit 200 . Alternatively, the second predetermined coordinate system may be the global coordinate system.

<2.2. CIR>
(1) CIR Calculation Process The portable device 100 and the communication unit 200 perform communication for estimating position parameters in the position parameter estimation process. At that time, the portable device 100 and the communication unit 200 calculate CIR (Channel Impulse Response).

CIR is the response when an impulse is input to the system. The CIR in this embodiment is defined as, when the wireless communication unit of one of the portable device 100 and the communication unit 200 (hereinafter also referred to as the transmitting side) transmits a signal including a pulse as the first signal, the other (hereinafter referred to as the receiving side) is calculated based on a second signal, which is a signal corresponding to the first signal, received by the wireless communication unit. It can also be said that the CIR indicates the characteristics of the wireless communication channel between the portable device 100 and the communication unit 200 . In the following, the first signal is also referred to as the transmitted signal and the second signal is also referred to as the received signal.

As an example, the CIR may be a correlation calculation result, which is the result of taking the correlation between the transmission signal and the reception signal at regular intervals. The correlation here may be a sliding correlation, which is a process of obtaining the correlation between the transmission signal and the reception signal while shifting their relative positions in the time direction. The CIR includes a correlation value indicating the degree of correlation between the transmission signal and the reception signal as an element for each time with a specified time interval. The specified time is, for example, an interval at which the receiving side samples the received signal. Therefore, the elements that make up the CIR are also called sampling points. The correlation value may be a complex number with IQ components. The correlation value may also be a complex amplitude or phase. The correlation value may also be power, which is the sum of the squares of the complex I and Q components (or the square of the amplitude).

The CIR can also be regarded as a set whose elements are values at each time (hereinafter also referred to as CIR values). In that case, CIR is the time series variation of the CIR value. If the CIR is the correlation calculation result, the CIR value is the correlation value.

Note that the portable device 100 and the communication unit 200 acquire the time using a time counter. A time counter is a counter that counts (typically increments) a value (hereinafter also referred to as a count value) indicating an elapsed time at a predetermined time interval (hereinafter also referred to as a count cycle). The current time is calculated based on the count value counted by the time counter, the count period, and the count start time. Matching the count period and the count start time between different devices is also referred to as synchronization. On the other hand, the fact that at least one of the counting period and the counting start time differs between different devices is also referred to as non-synchronization or asynchronization. The portable device 100 and the communication unit 200 may be synchronous or asynchronous. Also, each of the plurality of wireless communication units 210 may be synchronous with each other or may be asynchronous with each other. The specified time for calculating the CIR may be an integral multiple of the count period of the time counter. In the following description, it is assumed that the portable device 100 and each of the plurality of wireless communication units 210 are synchronized with each other, unless otherwise specified.

The CIR calculation process when the transmitting side is the portable device 100 and the receiving side is the communication unit 200 will be described in detail below with reference to FIGS. 5 and 6. FIG.

FIG. 5 is a diagram showing an example of processing blocks for signal processing in the communication unit 200 according to this embodiment. As shown in FIG. 5, the communication unit 200 includes an oscillator 212, a multiplier 213, a 90-degree phase shifter 214, a multiplier 215, an LPF (Low Pass Filter) 216, an LPF 217, a correlator 218, and an integrator 219. .

Oscillator 212 generates a signal having the same frequency as the frequency of the carrier that carries the transmission signal, and outputs the generated signal to multiplier 213 and 90-degree phase shifter 214 .

Multiplier 213 multiplies the received signal received by antenna 211 and the signal output from oscillator 212 and outputs the multiplied result to LPF 216 . The LPF 216 outputs to the correlator 218 signals having frequencies lower than the frequency of the carrier that carries the transmission signal among the input signals. The signal input to correlator 218 is the I component (that is, the real part) of the components corresponding to the envelope of the received signal.

90-degree phase shifter 214 delays the phase of the input signal by 90 degrees and outputs the delayed signal to multiplier 215 . Multiplier 215 multiplies the received signal received by antenna 211 and the signal output from 90-degree phase shifter 214 and outputs the multiplied result to LPF 217 . LPF 217 outputs to correlator 218 signals having frequencies lower than the frequency of the carrier that carries the transmission signal among the input signals. The signal input to the correlator 218 is the Q component (ie imaginary part) of the components corresponding to the envelope of the received signal.

The correlator 218 calculates the CIR by taking the sliding correlation between the received signal composed of the I component and the Q component output from the LPF 216 and the LPF 217 and the reference signal. Note that the reference signal here is the same signal as the transmission signal before being multiplied by the carrier wave.

Integrator 219 integrates the CIR output from correlator 218 and outputs the result.

Here, the transmitting side can transmit a signal including a preamble including a plurality of one or more preamble symbols as a transmission signal. A preamble is a known sequence between transmission and reception. A preamble is typically placed at the beginning of a transmission signal. A preamble symbol is a pulse sequence containing one or more pulses. A pulse sequence is a set of multiple pulses separated in the time direction. Preamble symbols are subject to accumulation by accumulation 219 . That is, the correlator 218 performs sliding correlation between each part corresponding to a plurality of preamble symbols contained in the received signal and the preamble symbol contained in the transmitted signal (that is, the reference signal), thereby obtaining Calculate the CIR of Then, the integrator 219 integrates the CIR of each preamble symbol for one or more preambles included in the preamble, and outputs the CIR after integration.

(2) Example of CIR An example of CIR output from the integrator 219 is shown in FIG. FIG. 6 is a graph showing an example of CIR according to this embodiment. The CIR shown in FIG. 6 is the CIR when the time at which the transmission side transmits the transmission signal is assumed to be the count start time of the time counter. Such a CIR is also called a delay profile. The horizontal axis of this graph is the delay time. The delay time is the elapsed time from the time when the transmission side transmits the transmission signal. The vertical axis of this graph is the absolute value of the CIR value (for example, power value). In the following description, CIR refers to a delay profile.

The shape of the CIR, more specifically the shape of the time series variation of the CIR values, is also referred to as the CIR waveform. Typically, in CIR, a set of elements between zero crossing points corresponds to one pulse. A zero-crossing point is an element whose value becomes zero. However, this is not the case in noisy environments. For example, a set of elements between the intersections of the reference level and the time-series variation of the CIR value may be regarded as corresponding to one pulse. The CIR shown in FIG. 6 includes a set 21 of elements corresponding to a certain pulse and a set 22 of elements corresponding to other pulses.

A set 21 corresponds, for example, to signals (eg, pulses) arriving at the receiver via the fastpath. Fastpath refers to the shortest path between transmission and reception. Fastpath refers to a straight path between transmit and receive in an unobstructed environment. A set 22 corresponds to, for example, signals (eg, pulses) arriving at the receiver through paths other than the fastpath. Such signals arriving via multiple paths are also referred to as multipath waves.

(3) Detection of First Arriving Wave The receiving side detects a signal that satisfies a predetermined detection criterion among the radio signals received from the transmitting side as a signal that has reached the receiving side via the fastpath. The receiving side then estimates the location parameters based on the detected signals. A signal detected as a signal that has reached the receiving side via the fast path is hereinafter also referred to as a first arriving wave.

The receiving side detects a signal that satisfies a predetermined detection criterion among the received radio signals as the first incoming wave. An example of a predetermined detection criterion is that the CIR value (eg amplitude or power) first exceeds a predetermined threshold. That is, the receiving side may detect, as the first incoming wave, a signal corresponding to a portion of the CIR whose CIR value first exceeds a predetermined threshold. The predetermined threshold used for detecting the first arriving wave is hereinafter also referred to as the fastpath threshold.

The signal received by the receiver can be either direct wave, delayed wave, or synthetic wave. A direct wave is a signal that is received by the receiving side through the shortest path between transmission and reception. That is, the direct wave is a signal that reaches the receiving side via the fastpath. A delayed wave is a signal that reaches the receiving side via a path other than the shortest path between transmission and reception, that is, via a path other than the fast path. The delayed wave is received by the receiving side with a delay from the direct wave. A composite wave is a signal received by a receiving side in a state in which a plurality of signals that have passed through a plurality of different paths are combined.

It should be noted here that the signal detected as the first arriving wave is not necessarily the direct wave. For example, if the direct wave is received while canceling out the delayed wave, the CIR value of the element corresponding to the direct wave may fall below a predetermined threshold and the direct wave may not be detected as the first arriving wave. In that case, a delayed wave or a composite wave that arrives later than the direct wave will be detected as the first arriving wave.

<2.3. Estimation of Positional Parameters>
(1) Distance Estimation The communication unit 200 performs distance measurement processing. Distance measurement processing is processing for estimating the distance between communication unit 200 and portable device 100 . The distance between the communication unit 200 and the portable device 100 is, for example, the distance R shown in FIG. Ranging processing includes transmitting and receiving a ranging signal and calculating a distance R based on the propagation delay time of the ranging signal. Propagation delay time is the time it takes for a signal to be transmitted and received.

Here, one of the plurality of wireless communication units 210 included in the communication unit 200 transmits and receives the ranging signal. The wireless communication unit 210 that transmits and receives ranging signals is hereinafter also referred to as a master. The distance R is the distance between the wireless communication unit 210 (more precisely, the antenna 211 ) functioning as a master and the portable device 100 .

In the ranging process, multiple ranging signals can be transmitted and received between the communication unit 200 and the portable device 100 . Among the plurality of ranging signals, the ranging signal transmitted from one device to the other device is also referred to as a first ranging signal. Next, the ranging signal transmitted as a response to the first ranging signal from the device that received the first ranging signal to the device that transmitted the first ranging signal is sent to the second Also referred to as a ranging signal. Next, from the device that received the second ranging signal, to the device that transmitted the second ranging signal, the ranging signal transmitted as a response to the second ranging signal is sent to the third It is also called a ranging signal.

An example of the flow of distance measurement processing will be described below with reference to FIG.

FIG. 7 is a sequence diagram showing an example of the flow of distance measurement processing executed in the system 1 according to this embodiment. The portable device 100 and the communication unit 200 are involved in this sequence. In this sequence, wireless communication section 210A functions as a master.

As shown in FIG. 7, first, the portable device 100 transmits a first ranging signal (step S102). When the radio communication unit 210A receives the first ranging signal, the control unit 230 calculates the CIR of the first ranging signal. After that, based on the calculated CIR, the control unit 230 detects the first arriving wave of the first ranging signal in the wireless communication unit 210A (step S104).

Next, the wireless communication unit 210A transmits a second ranging signal in response to the first ranging signal (step S106). Upon receiving the second ranging signal, the portable device 100 calculates the CIR of the second ranging signal. After that, the portable device 100 detects the first arriving wave of the second ranging signal based on the calculated CIR (step S108).

Next, the portable device 100 transmits a third ranging signal in response to the second ranging signal (step S110). When the radio communication unit 210A receives the third ranging signal, the control unit 230 calculates the CIR of the third ranging signal. After that, based on the calculated CIR, the control unit 230 detects the first arriving wave of the third ranging signal in the wireless communication unit 210A (step S112).

The portable device 100 controls the time INT ₁ from the transmission time of the first ranging signal to the reception time of the second ranging signal, and the time INT 1 from the reception time of the second ranging signal to the third ranging signal. The time INT ₂ until the signal transmission time is measured. Here, the reception time of the second ranging signal is the reception time of the first arriving wave of the second ranging signal detected in step S108. Then, the portable device 100 transmits a signal including information indicating times INT ₁ and INT ₂ (step S114). Such a signal is received, for example, by the wireless communication unit 210A.

The control unit 230 controls the time INT 3 from the reception time of the first ranging signal to the transmission time of the second ranging signal, and the time INT ₃ from the transmission time of the second ranging signal to the third ranging signal. Measure the time INT ₄ until the signal reception time. Here, the reception time of the first ranging signal is the reception time of the first arriving wave of the first ranging signal detected in step S104. Similarly, the reception time of the third ranging signal is the reception time of the first arriving wave of the third ranging signal detected in step S112.

Then, the control unit 230 estimates the distance R based on the times INT ₁ , INT ₂ , INT ₃ and INT ₄ (step S116). For example, the control unit 230 estimates the propagation delay time _τm using the following equation.

After that, the control unit 230 estimates the distance R by multiplying the estimated propagation delay time _τm by the speed of the signal.

- Contribution to Decrease in Estimation Accuracy The reception time of the ranging signal at the start or end of times INT ₁ , INT ₂ , INT ₃ , and INT ₄ is the reception time of the first arriving wave of the ranging signal. As described above, the signal detected as the first arriving wave is not necessarily the direct wave.

When a delayed wave or composite wave arriving later than the direct wave is detected as the first arriving wave, the reception time of the first arriving wave is longer than when the direct wave is detected as the first arriving wave. Delay. In that case, the estimation result of the propagation delay time τ _m will fluctuate from the true value (estimation result when the direct wave is detected as the first arriving wave). Then, the distance measurement accuracy is lowered by the amount of variation.

- Supplementary Note that the reception side may set the time at which a predetermined detection criterion is satisfied as the reception time of the first arriving wave. That is, the receiving side regards the time when the power value of the CIR first exceeds a predetermined threshold or the time when the reception power value of the received radio signal first exceeds a predetermined threshold as the reception time of the first arriving wave. good too. In addition, the receiving side detects the peak time of the detected first arriving wave (that is, the time when the power value is highest in the portion corresponding to the first arriving wave in the CIR, or the received power value of the first arriving wave is the highest) may be set as the reception time of the first arriving wave.

(2) Angle Estimation The communication unit 200 performs angle estimation processing. The angle estimation process is a process of estimating the angles α and β shown in FIG. The angle acquisition process includes receiving a signal for angle estimation and calculating angles α and β based on the result of receiving the signal for angle estimation. The angle estimation signal is a signal that is transmitted and received in angle estimation processing. An example of the flow of angle estimation processing will be described below with reference to FIG.

FIG. 8 is a sequence diagram showing an example of the flow of angle estimation processing executed in the system 1 according to this embodiment. The portable device 100 and the communication unit 200 are involved in this sequence.

As shown in FIG. 8, first, the portable device 100 transmits an angle estimation signal (step S202). Next, when the angle estimation signal is received by each of radio communication units 210A to 210D, control unit 230 calculates the CIR of the angle estimation signal received by each of radio communication units 210A to 210D. After that, control unit 230 detects the first arriving wave of the angle estimation signal based on the calculated CIR for each of radio communication units 210A to 210D (steps S204A to S204D). Next, control unit 230 detects the detected phase of the first arriving wave for each of radio communication units 210A to 210D (steps S206A to S206D). Then, control unit 230 estimates angles α and β based on the phase of the first arriving wave detected for each of radio communication units 210A to 210D (step S208).

Here, the phase of the first arriving wave is the phase at the reception time of the first arriving wave in the CIR. Alternatively, the phase of the first arriving wave may be the phase of the received radio signal at the reception time of the first arriving wave.

Details of the processing in step S208 will be described below. Let P _A be the phase of the first arriving wave detected for the radio communication unit 210A. Let _PB be the phase of the first arriving wave detected for the radio communication unit 210B. Let _PC be the phase of the first arriving wave detected for the radio communication unit 210C. Let _PD be the phase of the first arriving wave detected for the wireless communication unit 210D. In this case, the antenna array phase differences Pd _AC and Pd _BD in the X-axis direction and the antenna array phase differences Pd _BA and Pd _DC in the Y-axis direction are expressed by the following equations.

Angles α and β are calculated by the following equations. Here, λ is the wavelength of radio waves, and d is the distance between antennas 211 .

Therefore, angles calculated based on the respective antenna array phase differences are represented by the following equations.

The controller 230 calculates the angles α and β based on the calculated angles α _AC , α _BD , β _DC and β _BA . For example, the control unit 230 calculates the angles α and β by averaging the angles calculated for each of the two arrays in the X-axis and Y-axis directions, as shown in the following equations.

- One cause of reduced estimation accuracy As described above, the angles α and β are calculated based on the phase of the first arriving wave. As described above, the signal detected as the first arriving wave is not necessarily the direct wave.

In other words, a delayed wave or composite wave may be detected as the first arriving wave. Since the phases of the delayed wave and the composite wave are typically different from the phase of the direct wave, the angle estimation accuracy is reduced by the difference.

- Supplement Note that the signal for angle estimation and the signal for distance measurement may be the same. For example, the third ranging signal shown in FIG. 7 and the angle estimation signal shown in FIG. 8 may be the same. In this case, the communication unit 200 can calculate the distance R and the angles α and β by receiving one radio signal that serves as both the angle estimation signal and the second ranging signal.

(3) Coordinate Estimation The control unit 230 performs coordinate estimation processing. The coordinate estimation process is a process of estimating the three-dimensional coordinates (x, y, z) of the portable device 100 shown in FIG. methods can be employed.

- First Calculation Method The first calculation method is a method of calculating coordinates x, y, and z based on the results of ranging processing and angle estimation processing. In that case, the controller 230 first calculates the coordinates x and y using the following equations.

Here, the distance R and the coordinates x, y and z have the following relationship.

Using the above relationship, the control unit 230 calculates the coordinate z according to the following equation.

- Second Calculation Method The second calculation method is a method of calculating the coordinates x, y, and z by omitting the estimation of the angles α and β. First, the relationships of the following equations hold from the above equations (4), (5), (6) and (7).

By rearranging Equation (12) with respect to cosα and substituting it into Equation (9), the coordinate x is obtained by the following equation.

When formula (13) is rearranged with respect to cos β and substituted into formula (10), the coordinate y is obtained by the following formula.

Then, substituting the formulas (14) and (15) into the formula (11) and arranging them, the coordinate z is obtained by the following formula.

The process of estimating the coordinates of the portable device 100 in the local coordinate system has been described above. By combining the coordinates of the portable device 100 in the local coordinate system and the coordinates of the origin of the local coordinate system in the global coordinate system, the coordinates of the portable device 100 in the global coordinate system can also be estimated.

- One cause of degraded estimation accuracy As described above, the coordinates are calculated based on the propagation delay time and the phase. These are all estimated based on the first arriving wave. Therefore, the accuracy of coordinate estimation can be reduced for the same reason as the distance measurement process and angle estimation process.

(4) Estimation of Existence Area The location parameter may include the area where the portable device 100 exists among the plurality of predefined areas. As an example, when the area is defined by the distance from the communication unit 200, the control unit 230 estimates the area where the portable device 100 exists based on the distance R estimated by the ranging process. As another example, when the area is defined by the angle from the communication unit 200, the control unit 230 estimates the area where the portable device 100 exists based on the angles α and β estimated by the angle estimation process. As another example, when the area is defined by three-dimensional coordinates, the control unit 230 estimates the area where the portable device 100 exists based on the coordinates (x, y, z) estimated by the coordinate estimation process. .

In addition, as a process specific to the vehicle 202 , the control unit 230 may estimate the area where the portable device 100 exists from among a plurality of areas including the interior and exterior of the vehicle 202 . This makes it possible to provide detailed services, such as providing different services depending on whether the user is inside the vehicle or outside the vehicle. In addition, the control unit 230 specifies an area in which the portable device 100 is present, from a peripheral area within a predetermined distance from the vehicle 202 and a distant area at a predetermined distance or more from the vehicle 202. good too.

(5) Use of location parameter estimation result The location parameter estimation result can be used for authentication of the portable device 100, for example. For example, when the portable device 100 exists in an area close to the communication unit 200 on the side of the driver's seat, the control unit 230 determines that the authentication is successful and unlocks the door.

<<3. Technical issues>>
Technical problems of this embodiment will be described with reference to FIGS. 9 to 12. FIG. 9 to 12 are graphs for explaining technical problems of this embodiment. The horizontal axis is the chip length indicating the delay time, and the vertical axis is the absolute value of the CIR value (for example, power value). A chip length is a time width per pulse. For example, when generating pulses with a bandwidth of 500 MHz, a pulse width of approximately 2 ns is the chip length.

FIG. 9 shows the CIR when a signal arrives via the fastpath at a delay time of _1TC and a signal arrives via a route other than the fastpath at a delay time of _3TC . Referring to FIG. 9, the CIR waveform has a peak at each of delay times 1T _C and 3T _C. Therefore, it can be seen that the separation of two multipath waves separated by a delay time of 2T _C is sufficiently achieved with the CIR waveform.

FIG. 10 shows the CIR when a signal arrives via the fastpath at a delay time of _1TC and a signal arrives via a route other than the fastpath at a delay time of _2TC . The signal of the first wave arriving at the delay time of _1TC and the signal of the second wave arriving at the delay time of _2TC are in phase. Referring to FIG. 10, the CIR waveform has a peak at the delay time of _1TC , while the CIR waveform does not have a peak at the delay time of _2TC . Furthermore, the signal arriving at the delay time of 1T _C and the signal arriving at the delay time of 2T _C are combined in phase and appear as one waveform. Therefore, it can be seen that separation of two multipath waves with a delay time of 1 T _C apart is difficult to achieve with the CIR waveform.

FIG. 11 shows the CIR when the signal arrives via the fastpath at a delay time of _1.2TC , and the signal arrives via a route other than the fastpath at delay times of _1.7TC and _3.6TC. It is The signal of the first wave arriving at the delay time of _1.2TC and the signal of the second wave arriving at the delay time of _1.7TC are in opposite phase. Referring to FIG. 11, the CIR waveform peaks at delay times of _1.2TC and _3.6TC . On the other hand, a second peak appears near the delay time of _2.2TC . This is far from the true delay time of 1.7T _C. Therefore, it can be seen that separation of two multipath waves separated by a delay time of 0.5T _C is difficult to achieve with the CIR waveform.

As shown in FIGS. 10 and 11, when the difference between the delay times of two multipath waves arriving at the receiving side is short, the delay time with a peak in the CIR waveform may deviate from the original delay time. Therefore, the delay time detected as the reception time of the first arriving wave may fluctuate from the original delay time. In that case, the distance measurement accuracy is lowered by the amount of variation.

FIG. 12 shows the CIR waveform 23 when a signal arrives via the fastpath at a delay time of _1TC and a signal arrives via a path other than the fastpath at a delay time of _1.5TC . A CIR waveform 21 is a CIR waveform when a single signal is received via the fast path with a delay time of _1TC . A CIR waveform 22 is a CIR waveform when a single signal is received through a path other than the fastpath with a delay time of _1.5TC . The signal of the first wave arriving with a delay time of _1TC and the signal of the second wave arriving with a delay time of _2TC are out of phase by 90 degrees.

If the delay time difference between two multipath waves arriving at the receiving side is short, a delayed wave or a composite wave may be detected as the first arriving wave. In the example shown in FIG. 12, a composite wave is detected as the first arriving wave. Typically, the phases of the delayed wave and the composite wave are different from the phase of the direct wave, so the angle estimation accuracy is reduced by the difference.

As in the example shown in FIG. 12, when the combined wave of the direct wave and the delayed wave is detected as the first arriving wave, the delayed wave is combined at the sampling point 31 near the peak and the phase fluctuates greatly. . Therefore, if the angle is estimated based on the phase at the sampling point 31, the estimation accuracy will be degraded.

On the other hand, at a low-power sampling point before the peak, such as the sampling point 32, the influence of the delayed wave is reduced, so the phase fluctuation is reduced. However, since the power value is reduced in exchange for the reduced influence of the delayed wave, the influence of noise is increased, and the estimation accuracy is reduced accordingly.

Therefore, it is desirable to be able to separate multipath waves with a resolution higher than CIR.

<<4. Technical features >>
<4.1. Detection of First Arriving Wave>
The portable device 100 and the communication unit 200 detect the first arriving wave by the processing described in detail below. In the following, as an example, a case where the entity that detects the first arriving wave is the communication unit 200 will be described. The processing described below may be executed by the portable device 100 .

(1) Formulation of Delay Profile First, a delay profile (that is, CIR) in the PN (Pseudo-Noise) correlation method is formulated. The PN correlation method is a method of calculating the CIR by transmitting a signal composed of a random sequence such as a PN sequence signal shared by the transmitting side and the receiving side and taking the sliding correlation between the transmitted signal and the received signal. . A PN sequence signal is a signal in which 1s and 0s are arranged almost randomly.

In the following, it is assumed that a PN sequence signal u(t) of unit amplitude is transmitted as a transmission signal (for example, preamble symbols of distance measurement signals and angle estimation signals). A unit amplitude is a defined amplitude known between transmission and reception.

In the following description, it is assumed that the antenna on the receiving side receives an L-wave multipath wave as a signal corresponding to the transmission signal transmitted from the transmitting side. A multipath wave is a signal received by a receiving side via multiple paths. That is, when the transmitting side transmits one signal, the receiving side receives L signals via a plurality of paths.

In this case, the received signal x(t) is represented by the following equation.

where t is time. h _i is the complex response value of the i-th multipath wave. T _0i is the propagation delay time of the i-th multipath wave. f is the frequency of the carrier wave of the transmission signal. v(t) is the internal noise. Internal noise is noise generated inside the circuit on the receiver side.

For example, in the PN correlation method, correlation with the received signal x(t) is taken while shifting the time of the known transmitted signal u(t) on the receiver side as shown in the following equation.

Note that u ^* () is the complex conjugate of u().

z(τ) is also called the delay profile. |z(τ)| ² is also referred to as the power delay profile. τ is the delay time.

A delay profile when an L-wave multipath wave is received is expressed by the following equation.

Here, r(τ) is the autocorrelation function of the PN sequence signal. An autocorrelation function is a function that correlates a signal with itself. r(τ) is given by the following equation.

Also, n(τ) is an internal noise component. n(τ) is given by the following equation.

(2) Sparse reconstruction Assume that the number of samples of the received signal is M (where M>L). The received signal is then sampled at M discrete delay times τ ₁ , τ ₂ , . . . , τ _M . The discrete delay time is a discrete value representing the delay time. z(τ) is a delay profile calculated based on the received signal sampled at the discrete delay time τ. A data vector z consisting of M delay profiles is represented by the following equation. However, the following equation is an equation when the receiving side receives only one preamble symbol.

When L-wave multipath waves are received, the data vector z is expressed as follows.

Note that r(τ) is called a mode vector.

Furthermore, when the data vector z is expressed in matrix form, it is expressed as in the following equation.

Here, A ₀ is also referred to as a modal matrix.

_S0 is also referred to as the signal vector.

In sparse reconstruction, the data vector z is converted to a form containing the matrix product of A and s.

T ₁ , T ₂ , . . . , _TN represent N delay times to search. T ₁ , T ₂ , . . . , _TN are also referred to as delay time bins. A delay time bin is an example of a set time. Note that N>>L.

Here, A is also referred to as an extended mode matrix. The extended mode matrix is a matrix consisting of a plurality of elements representing a delay profile when it is assumed that signals are received at each of a plurality of delay time bins. For example, r(T ₁ ), an element of the extended mode matrix A, is the delay profile of the signal assuming it was received at time T ₁ .

s is also called an extended signal vector. An extended signal vector is a vector consisting of a plurality of elements representing the presence or absence of a signal for each delay time bin, as well as the amplitude and phase of the signal.

(3) Estimation of Propagation Delay Time Based on Extended Signal Vector According to sparse reconstruction, delay profile z is modeled in the form of As+n. Therefore, by solving an underdetermined problem with an unknown number of N and a condition number of M (M<N), the extended signal vector s can be obtained. The control unit 230 estimates the reception time of the first arrival wave based on the delay time bins corresponding to the multiple elements in the extended signal vector s.

Here, a non-zero element of the extended signal vector indicates the presence of a signal at the delay time bin corresponding to the non-zero element. On the other hand, a zero element in the extended signal vector indicates no signal at the delay time bin corresponding to the zero element. Therefore, the control unit 230 estimates the delay time bin corresponding to the non-zero element among the delay time bins corresponding to the multiple elements in the extended signal vector s as the reception time of the first arriving wave.

At that time, the control unit 230 estimates the sparse solution of the extended signal vector s, and estimates the delay time bin corresponding to the non-zero element in the estimated sparse solution as the reception time of the first arriving wave. A sparse solution is a vector in which only a certain number of elements are non-zero. The predetermined number is the number of pulses included in the received signal as pulses corresponding to one pulse included in the transmitted signal. That is, a sparse solution is a vector whose only L elements are non-zero and the other elements are zero if L multipath waves are received. For example, if _s2 is non-zero in s=[ _s1 , _{s2 , .}

In particular, the control unit 230 estimates the earliest delay time bin among the delay time bins corresponding to the non-zero elements among the elements included in the extended signal vector s as the reception time of the first arriving wave. For example, when _{s 2 ,} s ₄ , and s ₆ of s=[s ₁ , s ₂ , _. At times _T4 and _T6 , it is determined that a signal has been received via a path other than the fastpath.

The signal resolution obtained by the sparsely reconstructed model is determined by the size of N (that is, the number of elements of the extended signal vector s) when modeling in the sparse reconstruction. Therefore, by increasing the number of N in sparse reconstruction, it is possible to separate multipath waves with finer resolution than CIR. Therefore, in this embodiment, the number N of delay time bins is made larger than the number M of samples of the received signal. In other words, in this embodiment, the time intervals of _{the N} delay _time bins T _{1 ,} T ₂ , . short. With such a configuration, it is possible to separate multipath waves with a finer resolution than the sampling interval of the received signal. As a result, it becomes possible to obtain the reception time of the first arriving wave with a finer resolution than the CIR.

(4) Compressed Sensing Algorithm The control unit 230 uses a compressed sensing algorithm to estimate the extended signal vector s, which is a sparse solution. A compressed sensing algorithm is an algorithm that assumes that an unknown vector is a sparse vector and estimates the unknown vector based on linear observations of the unknown vector. In this embodiment, the extended signal vector s is an example of an unknown vector. Linear observation is the result of multiplying an unknown vector by a coefficient. In this embodiment, the extension mode matrix A is an example of coefficients. Delay profile z is an example of a linear observation.

Compressed sensing algorithms include FOCUSS (Focal Underdetermined System Solver), ISTA (Iterative Shrinkage Thresholding Algorithm), and FISTA (Fast ISTA). In particular, FOCUSS is an algorithm that assumes initial values for unknown vectors and iteratively estimates unknown vectors using a generalized inverse matrix and a weight matrix. FOCUSS can accurately estimate an unknown vector with a small number of iterations by using a generalized inverse matrix and a weight matrix. The basic principle of FOCUSS is described in the first non-patent document "Irina F. Gorodnitsky, Member, IEEE, and Bhaskar D. Rao, "Sparse Signal Reconstruction from Limited Data Using FOCUSS: A Re-weighted Minimum Norm Algorithm", IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 45, NO. 3, MARCH 1997.

Another example of compressed sensing algorithm is M-FOCUSS (FOCUSS with multiple measurement vectors), which is an extension of FOCUSS described above. M-FOCUSS is an algorithm that applies FOCUSS to a plurality of unknown vectors in parallel. The basic principle of M-FOCUSS is described in the second non-patent document "Shane F. Cotter, et al; "Sparse Solutions to Linear Inverse Problems With Multiple Measurement Vectors", IEEE Transactions on Signal Processing, vol. 53, No. 7. , Jul. 2005, pp. 2477-2488."

The control unit 230 according to this embodiment uses M-FOCUSS to estimate the reception time of the first arriving wave. For this purpose, the control unit 230 first performs sparse reconfiguration to enable M-FOCUSS. Specifically, the control unit 230 divides the data matrix obtained by extending the data vector z with respect to the plurality of wireless communication units 210 into an extended mode matrix and an extended signal matrix obtained by extending the extended signal vector s with respect to the plurality of wireless communication units 210. Convert to matrix product form. Then, using M-FOCUSS, control section 230 estimates an extended signal matrix that satisfies a predetermined condition, and estimates the reception time of the first arrival wave based on the estimation result.

- Redefinition of Numerical Formulas Concerning Sparse Reconstruction In the above description, the CIR is calculated for a received signal received by one radio communication unit 210, and formulation is performed when sparse reconstruction is performed. In the following, formulation regarding a plurality of received signals received by a plurality of wireless communication units 210 is performed.

When portable device 100 transmits a transmission signal, control unit 230 determines the correlation between the reception signal received by each of wireless communication units 210 and the transmission signal set in each of wireless communication units 210. The CIR in each of the plurality of wireless communication units 210 is calculated by taking the timing from the specified time. The timing set in each of the plurality of wireless communication units 210 refers to the count start time of the time counter in each of the plurality of wireless communication units 210 . In the following description, it is assumed that the count start time is the same for each of the plurality of wireless communication units 210 . That is, it is assumed that each of the plurality of wireless communication units 210 is synchronized with each other. Of course, each of the plurality of wireless communication units 210 may be asynchronous.

Let K be the number of wireless communication units 210 (that is, the number of antennas 211 ), and let k be an index indicating an individual antenna 211 . Z _k (τ), which is the CIR obtained by taking the correlation between the received signal received by the k-th antenna and the transmitted signal, is expressed by the following equation.

where x _k (t) is the received signal received by the k-th antenna.

A data vector z ^(k) obtained by discretizing the CIR at the k-th antenna with the sampling number M is expressed by the following equation.

where A _s is the modal matrix in which the mode vectors of all L waves are arranged in columns. A _s is represented by the following equation.

Also, _ss is a signal vector at a reference antenna (hereinafter also referred to as a reference antenna) among the K antennas. _ss is represented by the following equation.

Also, B _k is a diagonal matrix indicating the phase difference of the k-th antenna with respect to the reference antenna. _Bk is represented by the following equation.

Here, r _kL is the phase difference generated according to the arrival angle when the k-th antenna receives the L-th pulse. The phase difference here means a phase delay with respect to the reference antenna. As an example, B _k when K=4 and four antennas 211 form a 2×2 planar array will be described with reference to FIG.

FIG. 13 is a diagram for explaining a case where four antennas 211 form a 2×2 planar array. As shown in FIG. 13, antennas 211A to 211D form a 2×2 planar array. Let α be the angle formed between the direction of arrival of the received signal (that is, the straight line connecting the origin and the portable device 100) and the X axis, and β be the angle formed between the direction of arrival of the received signal and the Y axis. Then, antenna 211A is the first antenna (i.e., k=1), antenna 211B is the second antenna (i.e., k=2), antenna 211C is the third antenna (i.e., k=3), and antenna 211D is Let it be the fourth antenna (ie k=4). Assuming that k=1 is the reference antenna, B _k is expressed by the following equations.

Note that I is a unit matrix.

Also, n ^(k) is the internal noise vector of the k-th antenna.

Also, y _s ^(k) is the signal vector of the k-th antenna. y _s ^(k) is represented by the following equation using B _k and s _s .

In sparse reconstruction, the data vector z ^(k) is converted to a form containing the matrix product of the extended mode matrices A and y ^(k) .

where A is the extension mode matrix described above. Also, y ^(k) corresponds to the above-described extended signal vector at the k-th antenna.

-Application of M-FOCUSS When the above formula (43) is expanded to a plurality of wireless communication units 210 ignoring internal noise, Z is converted to a format including the matrix product of A and Y as shown in the following formula. be done.

Z is a matrix in which K data vectors z ^(k) are arranged. That is, Z is a vector in which the CIRs obtained in each of the plurality of radio communication units 210 are arranged for the plurality of radio communication units 210 . Z is also called a data matrix. Z is represented by the following formula.

Y is a matrix in which extended signal vectors in each of the plurality of wireless communication units 210 are arranged for the plurality of wireless communication units 210 . Y is also called an extended signal matrix. Y is represented by the following formula.

where y ^(k) is the k-th column vector of the augmented signal matrix Y; y[i], on the other hand, is the i-th row vector of the augmented signal matrix Y. The relationship between y ^(k) and y[i] will be described in detail with reference to FIG.

FIG. 14 is a diagram for explaining the relationship between y ^(k) and y[i]. As shown in FIG. 14, y ^(k) is the extended signal vector corresponding to the CIR at the kth antenna. Specifically, y ⁽¹⁾ is the extended signal vector corresponding to the CIR of the first antenna (ie, k=1). y ⁽²⁾ is the extended signal vector corresponding to the CIR of the second antenna (ie, k=2). y ⁽³⁾ is the extended signal vector corresponding to the CIR of the third antenna (ie, k=3). y ⁽⁴⁾ is the extended signal vector corresponding to the CIR of the fourth antenna (ie, k=4). On the other hand, y[i] is a vector in which elements corresponding to the i-th delay time in the CIR of all antennas are arranged. For example, y[1] is a vector of elements corresponding to delay time bin _T1 in four CIRs. y[N] is a vector in which elements corresponding to delay time bins T _N in four CIRs are arranged.

The control unit 230 estimates an extended signal matrix Y that minimizes a given norm. At that time, the control unit 230 estimates the extended signal matrix Y, which minimizes a predetermined norm and is a sparse solution, on the condition that the above formula (44) is satisfied.

The predetermined norm is the norm of a vector obtained by arranging, for a plurality of delay times, values obtained by performing a predetermined operation on a plurality of elements corresponding to the same delay time among the elements constituting the extended signal matrix Y. That is, the predetermined norm may be the norm of a vector in which N values obtained by performing a predetermined operation on a plurality of elements constituting y[i] are arranged.

As an example, the predetermined operation may be taking the square root of the sum of the squares of a plurality of elements corresponding to the same delay time. In that case, the predetermined norm may be the norm of the N-dimensional vector shown in the following equation.

As another example, the predetermined operation may be an average.

Here, the norm is the length of the vector. The norm may be the lp norm. The lp norm is represented by the following equation.

Here, p is a constant of 0 or more and 1 or less. However, in Expression (48), it is assumed that 0 ⁰ =0.

Below, as a predetermined norm, the control unit 230 arranges the square roots of the sum of the squares of a plurality of elements corresponding to the same delay time among the elements constituting the extended signal matrix Y for a plurality of delay times. Suppose we estimate an extended signal matrix Y that minimizes the lp-norm of the vector. Specifically, the control unit 230 estimates an extended signal matrix Y that minimizes a predetermined norm by repeatedly calculating STEP1 to STEP3 below.

where Y _m is a candidate for the extended signal matrix Y that minimizes a given norm. m is the number of iterations. y _m−1 [N] is a vector that constitutes Y _m−1 and is a vector consisting of elements corresponding to the i-th delay time in the extended signal matrix. i is the index of the delay time. N is the maximum value of the delay time index i.

The initial value _Y0 of _Ym is given by the following equation.

where A ⁻ is the generalized inverse of the extended mode matrix A. The generalized inverse may be a Moore-Penrose generalized inverse. So the initial value Y ₀ is the minimum norm solution for Y. However, the initial value Y ₀ is not a sparse solution.

The control unit 230 repeatedly executes STEP1 to STEP3. As an example, STEP1 to STEP3 may be repeatedly executed until Y _m converges. As another example, STEP1 to STEP3 may be repeatedly executed a predetermined number of times. This makes it possible to estimate an extended signal matrix Y that is closer to the true value.

- Estimation of Reception Time of First Arriving Wave The control unit 230 estimates the reception time of the first arriving wave based on the extended signal matrix Y that minimizes the predetermined norm estimated by M-FOCUSS. In M-FOCUSS, the extended signal matrix Y is estimated under the condition that it matches the CIR in multiple radio units 210 . Therefore, compared to the case where the extended signal vector s is estimated under the condition that it matches with one CIR, it is possible to improve the accuracy of estimating the reception time of the first arriving wave.

The estimation method is as described above regarding the estimation of the propagation delay time based on the extended signal vector. That is, the control section 230 estimates the delay time corresponding to the non-zero element in the extended signal matrix that minimizes the predetermined norm as the reception time of the first arriving wave. In particular, control section 230 estimates the earliest delay time among the delay times corresponding to the non-zero elements in the extended signal matrix that minimizes the predetermined norm as the reception time of the first arriving wave. A plurality of y ^(k) s forming the extended signal matrix Y have non-zero elements in the common delay time.

(5) Regularization parameter Above, an example of a method of estimating the extended signal matrix Y that minimizes a predetermined norm by M-FOCUSS and estimating the reception time of the first arrival wave based on the estimated extended signal matrix Y Stated.

Here, in order to further improve the estimation accuracy of the extended signal matrix Y, the control unit 230 according to the present embodiment, in estimating the extended signal matrix Y that minimizes a predetermined norm, uses a regularization parameter One of the features is to perform iterative calculations using

For example, in the case of M-FOCUSS, the control unit 230 may use the following formula (53) instead of formula (50) in STEP 2 above.

where α is the regularization parameter. I is the identity matrix. Note that iterative calculation using a regularization parameter is possible not only in M-FOCUSS but also in FOCUSS. Regularization parameters in FOCUSS and M-FOCUSS are mentioned in the first non-patent document and the second non-patent document.

By using the regularization parameter in the iterative calculation, the convergence of Y _m is facilitated, and an effect of improving the estimation accuracy of the extended signal matrix Y that minimizes the predetermined norm is expected.

On the other hand, if the value of the regularization parameter used in the iterative calculation is not appropriate, the accuracy of estimating the extended signal matrix Y that minimizes the predetermined norm may conversely deteriorate.

In order to avoid the situation as described above, the control unit 230 according to the present embodiment may perform control to change the regularization parameter to a suitable value according to the reception status of the received signal in the iterative calculation.

More specifically, the control unit 230 according to the present embodiment divides the iterative calculation into a plurality of stages and executes the iterative calculation, and sets the value of the regularization parameter used in the iterative calculation in the second and subsequent stages among the plurality of stages to the value of the previous stage. One of the features is that the value of the regularization parameter is set to be equal to or greater than the value of the regularization parameter used in the iterative calculation, and the value of the regularization parameter is changed in the iterative calculations from the second stage onward based on the reception status of the second signal.

The control of the regularization parameter according to this embodiment will be described in detail below. FIG. 15 is a flowchart showing an example of control of regularization parameters according to this embodiment.

Note that FIG. 15 illustrates a case where the control unit 230 performs iterative calculation in two steps. In addition, in FIG. 15 and the following description, the N-th iterative calculation may be referred to as N-th order estimation. For example, the iterative calculation in the first stage is called primary estimation, and the iterative calculation in the second stage is called secondary estimation.

In the example shown in FIG. 15, the control unit 230 sets α_1 as the regularization parameter and γ_1 as the convergence determination value, and performs primary estimation (S402).

The convergence determination value is a reference value used for determining the convergence of _Ym for each N-th order estimation. When the convergence determination value is γ, the control unit 230 may perform the convergence determination based on the following formula (54). However, the following formula (54) is just an example.

Next, the control unit 230 estimates the arrival time of the received signal based on the extended signal matrix Y estimated in step S402 (S404). Here, an example method for estimating the arrival time of the received signal will be described with reference to FIG.

FIG. 16 is an explanatory diagram for explaining an outline of the extended signal matrix Y according to this embodiment. As shown in FIG. 16, the horizontal direction (row direction) of the extended signal matrix Y is the element direction, and the vertical direction (column direction) is the time direction. FIG. 16 shows an example of an extended signal matrix Y with four elements.

First, the control unit 230 estimates the extended signal power vector p _Y based on the extended signal matrix Y. The extended signal power vector _pY is a vector obtained by calculating the square of the absolute value of each component of the extended signal matrix Y and averaging the element directions.

For example, if the element of the i-th row j-th column of the extension signal matrix Y according to the above equation (46) is Y (i, j), the control unit 230, based on the following equation (55), the extension signal power vector p _Y may be estimated.

Then, the control section 230 may estimate a component of the extended signal power vector p _Y that is equal to or greater than a predetermined value as the arrival time of the received signal. Note that the predetermined value may be set to a value obtained by, for example, “maximum value of extended signal power vector _pY ×0.5”, but is not limited to such an example.

An example of estimating the arrival time of a received signal has been described above. Note that the method of estimating the arrival time of the received signal is not limited to the example described above. Again, with reference to FIG. 15, the continuation of the control example of the regularization parameter according to this embodiment will be described.

After the arrival time of the received signal is estimated (S404), the controller 230 determines whether or not the arrival interval of the received signal is equal to or less than the threshold θ (S406).

The arrival interval of the received signal may be, for example, the interval between the first arrival wave and the second arrival wave. In this case, the threshold value θ may be set to 0.5 ns or 2 ns.

However, the above is just an example. As the arrival interval of the received signals, the widest interval, the narrowest interval, the average value or the median value of a plurality of intervals between arrival waves, or the like may be employed. Also, the threshold value θ may be appropriately designed.

For example, the control unit 230 determines the arrival time of the received signal with the maximum extended signal power vector _pY among the estimated arrival times of the received signals, and the arrival time of the other received signal closest to the arrival time of the received signal. The difference between the time and the time may be used as the arrival interval of the received signal.

In the example shown in FIG. 15, when the arrival interval of received signals is greater than the threshold θ (S406: No), the control unit 230 sets α_2 as the regularization parameter and γ_2 as the convergence determination value, and performs secondary estimation ( S408).

On the other hand, if the arrival interval of the received signals is equal to or less than the threshold θ (S406: Yes), the control unit 230 sets α_3 as the regularization parameter and γ_3 as the convergence determination value, and performs secondary estimation (S4010).

Here, the control unit 230 according to the present embodiment is characterized in that the value of the regularization parameter used in the Nth order estimation is changed to be greater than or equal to the value of the regularization parameter used in the N-1th order estimation, which is the previous stage. be one of

Also, one of the characteristics of the control unit 230 according to the present embodiment is that the value of the regularization parameter is reduced as the arrival interval of the received signal increases after the secondary estimation.

In the example shown in FIG. 15, the control unit 230 may control the value of the regularization parameter to satisfy α_3>α_2≧α_1.

For example, when the value of α_1 in the primary estimation is 10 ⁻⁴ , the control unit 230 may set the value of α_2 to 10 ⁻³ and the value of α_3 to 10 ⁻² .

Note that in the example shown in FIG. 15, a single threshold θ is set for the determination in step S406, but two or more thresholds may be used in the determination. That is, the number of branches based on the arrival intervals of received signals may be three or more. Even in this case, it is sufficient that the above-mentioned two features related to the control of the regularization parameter are satisfied.

In addition, the control unit 230 according to the present embodiment relates to the convergence determination value used for the convergence determination of the iterative calculation, and the convergence determination value used in the secondary estimation and subsequent estimations is set to be smaller than the convergence determination value used in the previous stage (N-1st estimation). One of the characteristics is to

In the example shown in FIG. 15, the controller 230 may perform control so that the convergence determination value satisfies γ_1>γ_2.

For example, when γ_1 in the first-order estimation is 10 ⁻¹ , control section 230 may set γ_2 to 10 ⁻⁶ .

Note that the convergence determination value may be the same value regardless of the branch in step S406 in the calculation at the same stage, or may adopt a different value depending on the branch.

After the secondary estimation (S408 or S410) using the regularization parameter and the convergence determination value set as described above, the control unit 230 obtains the first arrival The arrival time and arrival angle of the wave are estimated (S412).

Note that FIG. 15 illustrates a case where the control unit 230 divides the iterative calculation into two stages (that is, the primary estimation and the secondary estimation), but the number of stages related to the iterative calculation may be three or more. Even in this case, the value of the regularization parameter used in the N-th order estimation is set larger than the value of the regularization parameter used in the previous stage N−1-th order estimation, and the convergence determination used in the N-th order estimation It is sufficient if the value is set smaller than the convergence judgment value used in the N-1 order estimation which is the previous stage.

Subsequently, the flow of iterative calculation using the regularization parameter and the convergence determination value according to this embodiment will be described in more detail.

FIG. 17 is a flowchart showing an example of the flow of iterative calculation using regularization parameters and convergence determination values according to this embodiment. Note that FIG. 17 illustrates a case where the control unit 230 divides the iterative calculation into two stages (that is, primary estimation and secondary estimation).

In the example shown in FIG. 17, first, the wireless communication unit 210 receives the second signal (S500).

Next, the control unit 230 calculates an initial value for FOCUSS or M-FOCUSS (S501). In step S501, 0 is substituted for m, which is a variable indicating the number of iterations.

Next, control unit 230 performs primary estimation consisting of steps S510 to S514.

In the primary estimation, the control unit 230 sequentially increments m (S510) and performs calculations corresponding to STEPs 1 to 3 described above (S511 to S513). A regularization parameter is used in the calculation corresponding to STEP2 described above in S512.

Next, the control unit 230 performs convergence determination related to primary estimation using the convergence determination value (S514).

When determining that the convergence condition is not satisfied (S514: No), the control unit 230 returns to step S510.

On the other hand, when determining that the convergence condition is satisfied (S514: Yes), the control unit 230 changes the regularization parameter and the convergence determination value (S515).

Next, control unit 230 performs secondary estimation consisting of steps S520 to S524.

In the secondary estimation, the control unit 230 sequentially increments m (S520) and performs calculations corresponding to STEPs 1 to 3 described above (S521 to S523). The regularization parameter changed in step S515 is used in the calculation corresponding to STEP2 described above in S522.

Next, the control unit 230 performs convergence determination related to secondary estimation using the convergence determination value changed in step S515 (S524).

When determining that the convergence condition is not satisfied (S524: No), the control unit 230 returns to step S520.

On the other hand, if the control unit 230 determines that the convergence condition is satisfied (S524: Yes), the control unit 230 estimates the arrival time and arrival angle of the first arrival wave from the extended signal matrix Y′ obtained in the secondary estimation (S525 ).

An example of the flow of iterative calculation using the regularization parameter and the convergence determination value according to the present embodiment has been described above.

Note that FIG. 17 illustrates a case where the number of iterations is continuously incremented between the first estimation and the second estimation, but the number of iterations may be reset to 0 between the first and second estimations. good.

In addition, although FIG. 17 illustrates the case where the iterative calculation is performed until the convergence condition is satisfied, the iterative calculation may end after a predetermined number of executions.

Further, in the above-described example, an example was mainly described in which the signal reception condition used for setting the value of the regularization parameter after the secondary estimation is the arrival interval of the received signal. The reception condition of the signal used for is not limited to the arrival interval of the received signal.

For example, the control unit 230 according to the present embodiment may change the value of the regularization parameter based on the signal power of the received signal after secondary estimation. More specifically, one of the characteristics of the control unit 230 may be that the value of the regularization parameter is decreased as the signal power of the received signal increases after the secondary estimation.

The received power here may be, for example, the maximum value of the extended signal power vector _pY . Also, the received power may be the total value or average value of a plurality of components with large values of the extended signal power vector _pY . For example, the received power may be the sum of the components of the extended signal power vector _pY that are greater than or equal to a predetermined value.

Moreover, the control unit 230 according to the present embodiment may change the value of the regularization parameter based on the noise power of the received signal after the secondary estimation. More specifically, one of the characteristics of the control unit 230 may be that the smaller the noise power of the received signal is, the smaller the value of the regularization parameter after the secondary estimation.

When the noise power of the received signal is used to set the regularization parameter, the control unit 230 may estimate the noise power of the received signal by including STEP 4 below after STEP 3 in the iterative calculation. . That is, the control unit 230 may estimate the noise power of the received signal by adding the processing of STEP4 according to the following equation (56) after steps S513 and S523 shown in FIG.

In Equation (56), σ ² is the noise power, ||·|| _F is the Frobenius norm, and Tr[·] is the matrix trace (sum of diagonal elements).

Also, the control unit 230 according to the present embodiment may change the value of the regularization parameter based on the SNR (Signal to Noise Ratio) of the received signal after the secondary estimation. More specifically, one of the characteristics of the control unit 230 may be that the larger the SNR value of the received signal, the smaller the value of the regularization parameter after the secondary estimation. This allows a regularization parameter suitable for the SNR situation to be set. Note that SNR is an example of signal-to-noise ratio.

For example, if the received power is SP, the control unit 230 may calculate the SNR by the following formula (57).

Further, after the secondary estimation, the control unit 230 may set the value of the regularization parameter by combining the reception conditions of the various signals described above. As an example, the control of the regularization parameter based on the SNR of the received signal and the arrival interval of the received signal will be described in detail.

FIG. 18 is a flowchart illustrating an example of control of regularization parameters according to the second embodiment. Note that FIG. 18 illustrates a case where the control unit 230 performs iterative calculation in two steps, as in FIG. 15 . In addition, in FIG. 18, explanations overlapping with those in FIG. 15 may be omitted.

First, in the case of the example shown in FIG. 18, the control unit 230 sets α_1 as the regularization parameter and γ_1 as the convergence judgment value, and from the input extended signal matrix Y ₀ and noise power σ ²⁽⁰⁾ , the linear Estimation is performed to estimate the extended signal matrix Y (S602). A case where α_1 is 10 ⁻⁴ , γ_1 is 10 ⁻¹ , and σ ²⁽⁰⁾ is 0 will be mainly described below, but the present invention is not limited to such an example.

Next, the control unit 230 estimates the arrival time of the received signal based on the extended signal matrix Y estimated in step S602 (S604).

Subsequently, the control unit 230 estimates the SNR of the received signal based on the extended signal matrix Y and the noise power σ ^2(a) estimated in step S602 (S606).

Then, the control unit 230 determines whether or not the SNR of the received signal is equal to or less than the specified value Φ (S608). In the example shown in FIG. 18, when the SNR of the received signal is greater than the specified value Φ (S608: No), the control unit 230 sets α_2 as the regularization parameter and γ_2 as the convergence determination value, and performs secondary estimation ( S610). Note that the specified value Φ may be set to 20 dB, for example.

If the SNR of the received signal is equal to or less than the specified value Φ (S608: Yes), the control section 230 determines whether or not the arrival interval of the received signal is equal to or less than the threshold θ (S612).

In the example shown in FIG. 18, when the arrival interval of received signals is greater than the threshold θ (S612: No), the control unit 230 sets α_3 as the regularization parameter and γ_2 as the convergence determination value, and performs secondary estimation ( S614).

On the other hand, if the arrival interval of received signals is equal to or less than the threshold θ (S612: Yes), the control unit 230 sets α_4 as the regularization parameter and γ_2 as the convergence determination value, and performs secondary estimation (S616).

Here, as described above, one of the characteristics of the control unit 230 according to the present embodiment is that the larger the SNR value of the received signal, the smaller the value of the regularization parameter after the secondary estimation. Also, one of the characteristics of the control unit 230 is that after the secondary estimation, the larger the arrival interval of the received signal, the smaller the value of the regularization parameter.

According to these two characteristics, in the case of the example shown in FIG. 18, the control unit 230 may control the values of the regularization parameter to satisfy α_4>α_3>α_2≧α_1. Note that α_2 here is an example of the first value.

For example, when the value of α_1 in the primary estimation is 10 ⁻⁴ , the control unit 230 may set the value of α_2 to 10 ⁻⁴ , the value of α_3 to 10 ⁻³ , and the value of α_4 to 10 ⁻² .

Note that in the example shown in FIG. 18, a single specified value Φ and a single threshold θ are set for the determinations in steps S608 and S610, respectively, but the number of specified values and thresholds used in the determination is two or more. may be That is, in the flow described above, the regularization parameter is branched into three patterns (α_2, α_3, α_4) depending on the SNR of the received signal and the arrival interval of the received signal, but may be branched into four or more patterns.

Further, in the example shown in FIG. 18, in step S608, determination is made based on the SNR of the received signal, and in subsequent step S610, determination is made based on the arrival interval of the received signal. An inter-arrival-based decision may be made followed by a SNR-based decision of the received signal.

In addition, the control unit 230 according to the present embodiment relates to the convergence determination value used for the convergence determination of the iterative calculation, and the convergence determination value used in the secondary estimation and subsequent estimations is set to be smaller than the convergence determination value used in the previous stage (N-1st estimation). One of the characteristics is to

In the example shown in FIG. 15, the controller 230 may perform control so that the convergence determination value satisfies γ_1>γ_2. For example, when γ_1 in the first-order estimation is 10 ⁻¹ , control section 230 may set γ_2 to 10 ⁻⁶ .

Note that the convergence determination value may be the same value regardless of the branching in steps S608 and S612 in the calculation at the same stage, or may adopt a different value depending on the branching.

After the secondary estimation (S610, S614 or S616) using the regularization parameter and the convergence determination value set as described above, the control unit 230 performs the second The arrival time and arrival angle of one arrival wave are estimated (S618).

The iterative calculation using the regularization parameter according to the present embodiment has been described above. According to the iterative calculation, it is possible to improve the estimation accuracy of the extended signal matrix that minimizes the predetermined norm, and in turn improve the accuracy of distance measurement value calculation and position estimation.

(6) Threshold processing Threshold processing may be performed in M-FOCUSS. The threshold processing here is processing to set elements equal to or lower than the second threshold to zero. For example, the control unit 230 may set zero the elements below the second threshold among the diagonal components of the weight matrix _Wm in Equation (49) of STEP1 above. The second threshold may be set based on the maximum value of the diagonal elements of the weight matrix _Wm . As an example, the control unit 230 may set to zero the values whose ratio to the maximum value is equal to or less than the second threshold for the diagonal components of the weight matrix _Wm .

According to the above-described threshold processing, when creating the weight matrix W _m , among the elements of the extended signal matrix Y _m , elements with values less than the second threshold are considered to be noise rather than signals, and zero is converted to This allows the extended signal matrix Y _m to converge more quickly. In addition, since non-zero elements are reduced, it becomes possible to easily obtain a sparse solution.

<4.2. Estimation of Positional Parameters>
The control unit 230 estimates the position parameter based on the first arriving wave detected by the processing described above.

- Ranging process The control unit 230 estimates the distance R between the portable device 100 and the communication unit 200 based on the reception time of the first arriving wave estimated by the process described above. The method of estimating the distance R is as described above with reference to FIG.

Specifically, the communication unit 200 calculates the CIR for the first ranging signal and performs sparse reconstruction and M-FOCUSS. Then, the communication unit 200 sets the time corresponding to the earliest delay time bin among the delay time bins corresponding to the non-zero elements among the elements included in the extended signal matrix Y estimated by M-FOCUSS to the first Time INT ₃ is measured as the reception time of the first arriving wave of the ranging signal.

Similarly, the communication unit 200 calculates the CIR for the third ranging signal and performs sparse reconstruction and M-FOCUSS. Then, the communication unit 200 sets the time corresponding to the earliest delay time bin among the delay time bins corresponding to the non-zero elements among the elements included in the extended signal matrix Y estimated by M-FOCUSS to the third Time INT ₄ is measured as the reception time of the first arriving wave of the ranging signal.

Then, the control unit 230 estimates the propagation delay time based on the times T ₁ to T ₄ and estimates the distance R. As described above, M-FOCUSS can estimate the reception time of the first arriving wave with high accuracy. Therefore, it is possible to improve the distance measurement accuracy.

-Angle Estimation Processing The communication unit 200 estimates the angles α and β based on the phase at the reception time of the first arriving wave estimated by the processing described above. The method of estimating the angles α and β is as described above with reference to FIG.

More specifically, the control unit 230 estimates the angles α and β based on the phases of the non-zero elements included in the extended signal matrix Y estimated by the process described above. In particular, the control unit 230 estimates the angles α and β based on the phase of the element with the earliest corresponding delay time among the one or more non-zero elements included in the extended signal matrix Y. For example, in the extended signal matrix Y estimated by applying M-FOCUSS to the CIR obtained by the antenna configuration shown in FIG. 13, the earliest non-zero element shall appear at the delay time T _i . In that case, the antenna array phase difference Pd _AC is calculated by the following equation.

Alternatively, the antenna array phase difference Pd _AC may be calculated by the following equation.

Note that angle( ) is a function for calculating the phase angle of a complex number. Y(i, k) is the element of the i-th row and the k-th column of the extended signal matrix Y.

Other antenna array phase differences are calculated in the same manner as above to calculate angles α and β.

As described above, M-FOCUSS can estimate the reception time of the first arriving wave with high accuracy. Angle estimation accuracy can also be improved by estimating the angle based on the phase of the element corresponding to the reception time of the first arrival wave estimated with high accuracy among the elements that make up the extended signal matrix Y. becomes.

<4.3. Process Flow>
FIG. 19 is a flowchart showing an example of the flow of position parameter estimation processing executed by the communication unit 200 according to this embodiment.

As shown in FIG. 19, the controller 230 first calculates the CIR for each antenna (step S302). Next, the control unit 230 converts the data matrix consisting of the CIR at each antenna into a format including the matrix product of the extended mode matrix and the extended signal matrix by sparse reconstruction (step S304). Next, the control unit 230 estimates an extended signal matrix that minimizes a predetermined norm by M-FOCUSS (step S306). Then, the control unit 230 estimates a position parameter based on the estimated extended signal matrix (step S308).

<4.4. Where to apply M-FOCUSS>
As explained above, a transmitter may send a signal including multiple preambles including one or more preamble symbols as a transmission signal. In that case, the receiving side correlates each of the portions corresponding to the plurality of preamble symbols in the received signal with the preamble symbol at regular time intervals after the transmitting side transmits the transmission signal. can be calculated.

M-FOCUSS may be applied to the accumulated CIR obtained by accumulating the CIR for each preamble symbol. On the other hand, M-FOCUSS may be applied for CIR per preamble symbol.

Note that the CIR may be calculated for each pulse. In that case, M-FOCUSS may be applied to the CIR after integration, which is obtained by accumulating the CIR for each pulse, or may be applied to the CIR for each pulse.

Also, the CIR may be calculated for the entire preamble. In that case, M-FOCUSS may be applied to the CIR calculated for the entire preamble.

Similar results can be obtained in either method.

<4.5. Scope of application of M-FOCUSS>
M-FOCUSS may be applied for the entire CIR.

On the other hand, M-FOCUSS may be applied to part of the CIR in the time axis direction. This reduces the computational load compared to when M-FOCUSS is applied to the entire CIR.

In particular, if the purpose is to detect the first arriving wave, it is desirable to apply M-FOCUSS only to a portion of the CIR near the reception time of the first arriving wave. A strong correlation is obtained only at the delay time when the pulse arrangement of the transmission signal and the pulse arrangement of the reception signal completely match, and the correlation is low at other portions. Therefore, even if M-FOCUSS is applied only to a part of the CIR near the reception time of the first arriving wave, the detection accuracy of the first arriving wave can be maintained.

In this way, by applying M-FOCUSS only to a portion of the CIR near the reception time of the first arriving wave, the detection accuracy can be maintained as compared to applying M-FOCUSS to the entire CIR. However, it is possible to reduce the computational load.

<<5. Supplement >>
Although the preferred embodiments of the present invention have been described in detail above with reference to the accompanying drawings, the present invention is not limited to such examples. It is obvious that a person having ordinary knowledge in the technical field to which the present invention belongs can conceive of various modifications or modifications within the scope of the technical idea described in the claims. It is understood that these also naturally belong to the technical scope of the present invention.

For example, in the above embodiments, the CIR is the correlation calculation result, but the present invention is not limited to such an example. As an example, the CIR may be the received signal (a complex number with IQ components) itself. The CIR value may be a complex number itself having the IQ component of the received signal, may be the amplitude or phase of the received signal, or may be the sum of squares of the I and Q components of the received signal (or the square of the amplitude). ). In this case, the receiving side detects the first arriving wave from the received signal. For example, the receiving side may use the fact that the amplitude or power of the received radio signal first exceeds a predetermined threshold as a predetermined detection criterion for detecting the first arriving wave. In that case, the receiving side may detect the signal (more specifically, the sampling point) of the received signal whose amplitude or received power first exceeds a predetermined threshold as the first arriving wave.

For example, in the above embodiment, the controller 230 calculates the CIR, detects the first arriving wave, and estimates the position parameter, but the present invention is not limited to such examples. At least one of these processes may be performed by wireless communication section 210 . For example, each of the plurality of wireless communication units 210 may calculate the CIR and detect the first arriving wave based on the received signal received by each. Also, estimation of location parameters may be performed by the wireless communication unit 210 functioning as a master, for example.

For example, in the above embodiments, the angles α and β are calculated based on the antenna array phase difference in the antenna pair, but the present invention is not limited to such an example. As an example, the communication unit 200 may calculate the angles α and β by performing beamforming with multiple antennas 211 . In that case, the communication unit 200 scans the main lobes of the multiple antennas 211 in all directions, determines that the portable device 100 exists in the direction with the highest received power, and calculates the angles α and β based on this direction. .

For example, in the above embodiment, as described with reference to FIG. 3, the local coordinate system is a coordinate system having a coordinate axis parallel to the axis connecting the antenna pair. Not limited. For example, the local coordinate system may be a coordinate system having coordinate axes that are not parallel to the axes connecting the antenna pairs. Also, the origin is not limited to the center of the plurality of antennas 211 . The local coordinate system according to this embodiment may be arbitrarily set based on the arrangement of the multiple antennas 211 of the communication unit 200 .

For example, in the above embodiment, an example in which four antennas 211 form a 2×2 planar array has been described, but the present invention is not limited to such an example. The number of antennas 211 is not limited to four, and their arrangement shape is not limited to a planar array. For example, multiple antennas 211 may be arranged as a linear array. A linear array means that a plurality of antennas 211 are arranged on the same line. As an example, an example in which four antennas 211 form a linear array will be described with reference to FIG.

FIG. 20 is a diagram for explaining a case where four antennas 211 form a linear array. As shown in FIG. 20, antennas 211A to 211D form a linear array. Let the axis along which the antennas 211A to 211D are arranged be the coordinate axis, and let θ be the angle between the coordinate axis and the arrival direction of the received signal. Then, antenna 211A is the first antenna (i.e., k=1), antenna 211B is the second antenna (i.e., k=2), antenna 211C is the third antenna (i.e., k=3), and antenna 211D is Let it be the fourth antenna (ie k=4). Assuming that k=1 is the reference antenna, B _k is expressed by the following equations.

For example, in the above embodiment, an example in which M-FOCUSS is applied to multiple CIRs in multiple wireless communication units 210 has been described, but the present invention is not limited to such an example. M-FOCUSS may be applied to multiple CIRs obtained from one wireless communication unit 210 . In that case, the control unit 230 uses a matrix in which a plurality of CIRs obtained from one wireless communication unit 210 are arranged as a data matrix, and the data matrix is an extension mode matrix and an extension in which the extension signal vectors are arranged for a plurality of CIRs. Convert to a format containing the matrix product of the signal matrix and Then, the control unit 230 estimates the reception time of the first arriving wave by applying M-FOCUSS to the conversion result. Also in this example, it is possible to improve the estimation accuracy of the reception time of the first arriving wave, as in the above embodiment.

As an example, one wireless communication unit 210 may receive a signal including multiple preambles from the portable device 100 . In that case, control section 230 calculates one CIR for one preamble received by radio communication section 210 . Then, control section 230 converts a plurality of CIRs calculated from a plurality of preambles into a format including the matrix product, and applies M-FOCUSS.

As another example, one wireless communication unit 210 may receive a signal from the portable device 100 multiple times. A signal here is a signal containing one or more preambles. In that case, the control unit 230 calculates one CIR for one signal received by the wireless communication unit 210 . Then, the control unit 230 converts a plurality of CIRs calculated from the signals received a plurality of times into a format including the matrix product, and applies M-FOCUSS.

Note that when applying M-FOCUSS to a plurality of CIRs obtained from one radio communication unit 210, _Bk is expressed by the following equation.

On the other hand, the control unit 230 may apply FOCUSS to one CIR obtained from one wireless communication unit 210 .

For example, in the above embodiment, an example has been described in which the person to be authenticated is the portable device 100 and the authenticator is the communication unit 200, but the present invention is not limited to such an example. The roles of portable device 100 and communication unit 200 may be reversed. For example, the mobile device 100 may identify location parameters. Also, the roles of the portable device 100 and the communication unit 200 may be dynamically exchanged. Also, location parameters may be specified and authenticated between the communication units 200 .

For example, in the above embodiments, an example in which the present invention is applied to a smart entry system has been described, but the present invention is not limited to such an example. The present invention is applicable to any system that estimates location parameters and performs authentication by transmitting and receiving signals. For example, the present invention is applicable to a pair including any two devices among portable devices, vehicles, smart phones, drones, homes, home electric appliances, and the like. In that case, one of the pair acts as the authenticator and the other acts as the authenticated person. Note that a pair may include two devices of the same type, or may include devices of two different types. The present invention can also be applied to a wireless LAN (Local Area Network) router specifying the location of a smartphone.

For example, in the above embodiments, UWB is used as the wireless communication standard, but the present invention is not limited to such an example. For example, a wireless communication standard that uses infrared rays may be used.

Also, a series of processes by each device described in this specification may be implemented by a program stored in a non-transitory computer readable storage medium. Each program, for example, is read into a RAM when executed by a computer, and executed by a processor such as a CPU. The storage medium is, for example, a magnetic disk, an optical disk, a magneto-optical disk, a flash memory, or the like. Also, the above program may be distributed, for example, via a network without using a storage medium.

Also, the processes described in this specification using flowcharts do not necessarily have to be performed in the order shown. Some processing steps may be performed in parallel. Also, additional processing steps may be employed, and some processing steps may be omitted.

1: System, 100: Portable Device, 110: Wireless Communication Unit, 111: Antenna, 120: Storage Unit, 130: Control Unit, 200: Communication Unit, 202: Vehicle, 210: Wireless Communication Unit, 211: Antenna, 220: Storage unit, 230: control unit

Claims (15)

a wireless communication unit that wirelessly receives a signal from another communication device;
a second signal that is a signal corresponding to the first signal received by the wireless communication unit when the other communication device transmits a signal including a pulse as the first signal; Take the correlation between the signal and the at every specified time,
A data matrix that is a matrix in which one or a plurality of correlation calculation results, which are results of correlation between the second signal and the first signal in the wireless communication unit, is arranged at the predetermined time,
an extension mode matrix, which is a matrix consisting of a plurality of elements representing the results of the correlation calculation when it is assumed that signals are received at each of a plurality of set times;
A matrix in which an extended signal vector, which is a vector consisting of a plurality of elements representing the presence or absence of a signal in the wireless communication unit for each set time, and the amplitude and phase of the signal, is arranged for one or more of the correlation calculation results. an extended signal matrix;
into a form containing the matrix product of
Estimate the extended signal matrix that minimizes a predetermined norm by iterative calculation using a regularization parameter that is a small positive amount,
A control unit that estimates the reception time of the second signal based on the extended signal matrix that minimizes the predetermined norm;
with
The control unit performs the iterative calculation in a plurality of steps, and sets the value of the regularization parameter used in the iterative calculation of the second and subsequent steps among the plurality of steps to the value of the regularization parameter used in the iterative calculation of the previous step. value or more, and change the value of the regularization parameter based on the reception status of the second signal in the iterative calculation after the second stage,
Communication device. The control unit changes the value of the regularization parameter based on the interval of incoming waves related to the second signal in the iterative calculation after the second stage.
A communication device according to claim 1 . The control unit reduces the value of the regularization parameter in the iterative calculation after the second stage as the interval between incoming waves related to the second signal increases.
3. A communication device according to claim 2. The control unit sets the value of the regularization parameter based on the signal power of the second signal in the iterative calculation after the second stage.
A communication device according to claim 1 . The control unit sets the value of the regularization parameter based on the noise power related to the second signal in the iterative calculation after the second stage.
A communication device according to claim 1 . The control unit sets the value of the regularization parameter based on the signal-to-noise ratio of the second signal in the iterative calculations after the second stage.
6. A communication device according to claim 5. The control unit reduces the value of the regularization parameter as the value of the signal-to-noise ratio of the second signal increases in the iterative calculations after the second stage.
7. A communication device according to claim 6. The control unit sets the value of the regularization parameter to a first value when the signal-to-noise ratio of the second signal is greater than a specified value, and the signal-to-noise ratio of the second signal is if less than or equal to the specified value, then setting the value of the regularization parameter to a value greater than the first value;
7. A communication device according to claim 6. With respect to a convergence determination value used for determining convergence of the iterative calculation, the control unit makes the convergence determination value used in the second and later iterative calculations smaller than the convergence determination value used in the iterative calculation of the previous stage.
A communication device according to claim 1 . The control unit calculates, as the predetermined norm, a value obtained by performing a predetermined operation on a plurality of elements corresponding to the same set time among the elements constituting the extended signal matrix, for a plurality of the set times. estimating the augmented signal matrix that minimizes the norm of the ordered vectors;
A communication device according to claim 1 . The control unit arranges, as the predetermined norm, square roots of sums of squares of a plurality of elements corresponding to the same set time among the elements constituting the extended signal matrix for a plurality of the set times. estimating the augmented signal matrix that minimizes the norm of the vector;
11. A communication device according to claim 10. The control unit, in the iterative calculation, by repeatedly calculating formula (1), formula (2), and formula (3), to estimate the extended signal matrix that minimizes the predetermined norm,
12. A communication device according to claim 11.

where Y _m is the candidate for the augmented signal matrix that minimizes the given norm. m is the number of iterations. y _m−1 [i] is a vector that constitutes Y _m−1 and is a vector consisting of elements corresponding to the i-th set time in the augmented signal matrix. N is the maximum value of the set time index i. p is a constant of 0 or more and 1 or less. A is the extension mode matrix. Z is the data matrix. α is the regularization parameter. I is the identity matrix. The initial value _Y0 of _Ym is given by the following equation.

where A ⁻ is the generalized inverse of A. In the iterative calculation, the control unit calculates the noise power related to the second signal by Equation (5).
9. A communication device according to any one of claims 5-8.

where σ ^2(m) is the noise power. M is the number of time samples and K is the number of elements. ||·|| _F is the Frobenius norm. Tr[·] is the matrix trace (diagonal sum). a second signal that is a signal corresponding to the first signal received by a wireless communication unit when another communication device transmits a signal including a pulse as the first signal; and the first signal. , is taken at regular intervals, and
A data matrix that is a matrix in which one or a plurality of correlation calculation results, which are results of correlation between the second signal and the first signal in the wireless communication unit, is arranged at the predetermined time,
an extension mode matrix, which is a matrix consisting of a plurality of elements representing the results of the correlation calculation when it is assumed that signals are received at each of a plurality of set times;
A matrix in which an extended signal vector, which is a vector consisting of a plurality of elements representing the presence or absence of a signal in the wireless communication unit for each set time, and the amplitude and phase of the signal, is arranged for one or more of the correlation calculation results. an extended signal matrix;
into a form containing the matrix product of
Estimate the extended signal matrix that minimizes a predetermined norm by iterative calculation using a regularization parameter that is a small positive amount,
estimating the reception time of the second signal based on the extended signal matrix that minimizes the predetermined norm;
including
The estimating is performed by dividing the iterative calculation into a plurality of stages, and using the value of the regularization parameter used in the iterative calculation of the second and subsequent stages among the plurality of stages in the iterative calculation of the previous stage. and changing the value of the regularization parameter based on the reception status of the second signal in the iterative calculation after the second stage,
Information processing methods. the computer,
a second signal that is a signal corresponding to the first signal received by a wireless communication unit when another communication device transmits a signal including a pulse as the first signal; and the first signal. , is taken at regular intervals, and
A data matrix that is a matrix in which one or a plurality of correlation calculation results, which are results of correlation between the second signal and the first signal in the wireless communication unit, is arranged at the predetermined time,
an extension mode matrix, which is a matrix consisting of a plurality of elements representing the results of the correlation calculation when it is assumed that signals are received at each of a plurality of set times;
A matrix in which an extended signal vector, which is a vector consisting of a plurality of elements representing the presence or absence of a signal in the wireless communication unit for each set time, and the amplitude and phase of the signal, is arranged for one or more of the correlation calculation results. an extended signal matrix;
into a form containing the matrix product of
Estimate the extended signal matrix that minimizes a predetermined norm by iterative calculation using a regularization parameter that is a small positive amount,
A control unit that estimates the reception time of the second signal based on the extended signal matrix that minimizes the predetermined norm;
function as
causing the control unit to divide the iterative calculation into a plurality of stages and execute the iterative calculation, and set the value of the regularization parameter used in the iterative calculation of the second stage and later among the plurality of stages as the value of the regularization parameter used in the iterative calculation of the preceding stage. value or more, and change the value of the regularization parameter based on the reception status of the second signal in the iterative calculation after the second stage;
program.

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