JP2019215281A - Radar device and target data output method - Google Patents

Abstract

To ensure that the shape of a target is correctly recognized.SOLUTION: A radar device comprises a generation unit 33, a filter processing unit 34 and an output unit 35. The generation unit generates instantaneous data that corresponds to a target on the basis of the reflected wave of a transmitted wave having been reflected by the target. The filter processing unit generates representative data per target on the basis of the instantaneous data generated by the generation unit. The output unit outputs output data in which the shape data of the target based on the instantaneous data used in generating the representative data is added to the representative data generated by the filter processing unit.SELECTED DRAWING: Figure 2

Description

  The present invention relates to a radar device and a target data output method.

  2. Description of the Related Art Conventionally, for example, there is a radar device mounted on a vehicle and detecting a target based on a reflected wave of a transmission wave transmitted from the vehicle reflected by the target. In such a radar device, one representative data represented by the target is generated from the instantaneous data corresponding to the same target, and the representative data is output (for example, see Patent Document 1).

JP 2015-210157 A

  However, in the related art, even when a plurality of instantaneous data are detected from the same target, only one representative data is output, so that the shape of the target cannot be correctly recognized from the output representative data. .

  The present invention has been made in view of the above, and an object of the present invention is to provide a radar device and a target data output method capable of correctly recognizing the shape of a target.

  In order to solve the above-described problem and achieve the object, a radar device according to an embodiment includes a generation unit, a filter processing unit, and an output unit. The generating unit generates instantaneous data corresponding to the target based on a reflected wave of the transmitted wave reflected by the target. The filter processing unit generates representative data for each target based on the instantaneous data generated by the generation unit. The output unit outputs output data obtained by adding shape data of the target based on the instantaneous data used for generating the representative data to the representative data generated by the filter processing unit.

  According to the present invention, it is possible to correctly recognize the shape of a target.

FIG. 1A is a diagram illustrating an example of mounting the radar device 1. FIG. 1B is a diagram showing an outline of a target data output method. FIG. 2 is a block diagram of the radar device. FIG. 3 is an explanatory diagram of processing from the pre-processing of the signal processing unit to the peak extraction processing in the signal processing unit. FIG. 4A is an explanatory diagram of the angle estimation process. FIG. 4B is an explanatory diagram (part 1) of the pairing process. FIG. 4C is an explanatory diagram (part 2) of the pairing process. FIG. 5A is a diagram (part 1) illustrating a specific example of output data. FIG. 5B is a diagram (part 2) illustrating a specific example of output data. FIG. 5C is a diagram (part 3) illustrating a specific example of output data. FIG. 6 is a flowchart illustrating a processing procedure executed by the radar device. FIG. 7 is a diagram illustrating a specific example of output data according to a modification.

  Hereinafter, embodiments of a radar device and a target data output method disclosed in the present application will be described in detail with reference to the accompanying drawings. The present invention is not limited by the embodiment. In the following, a case where the radar apparatus is an FM-CW (Frequency Modulated Continuous Wave) method will be described as an example, but the radar apparatus may be an FCM (Fast-Chirp Modulation) method.

  First, an outline of a target data output method according to the embodiment will be described with reference to FIGS. 1A and 1B. FIG. 1A is a diagram illustrating an example of mounting a radar device. FIG. 1B is a diagram showing an outline of target data output. FIG. 1A shows a host vehicle MC on which the radar device 1 according to the embodiment is mounted, and another vehicle LC located in front of the host vehicle MC.

  As shown in FIG. 1A, the radar device 1 is mounted in, for example, a front grill of the own vehicle MC, and detects a target (for example, another vehicle LC or the like) existing in the traveling direction of the own vehicle MC. The mounting location of the radar apparatus 1 may be mounted on another location such as a windshield, a rear grill, and left and right side portions (for example, left and right door mirrors).

  Further, as shown in FIG. 1A, the radar device 1 generates instantaneous data 100 corresponding to a target based on a reflected wave of a radio wave transmitted around the own vehicle MC reflected by another vehicle LC. The instantaneous data 100 includes information such as the relative speed of the direction to the own vehicle MC, the distance between the own vehicle MC and the instantaneous data 100, and the direction of the instantaneous data 100.

  In addition, the radar device 1 performs time-series filtering on the generated instantaneous data 100, and generates target data corresponding to the target as a filter value. This makes it possible to track the target data.

  Thereafter, the radar apparatus 1 performs grouping by integrating a plurality of target data as target data of the same object into one, and generates representative data 50. For example, the radar apparatus 1 regards target data whose detection position and speed are close to each other within a predetermined range as target data of the same object, and generates one representative data 50 from the target data.

  However, even if a plurality of target data are detected, only one representative data 50 is output for one target. Therefore, the shape of the target cannot be recognized from the representative data 50.

  Therefore, in the target data output method according to the embodiment, the output data Do obtained by adding the shape data relating to the shape of the target to the representative data 50 is output so that the shape of the target is correctly recognized.

  Specifically, as shown in FIG. 1B, the output data Do includes the instantaneous data 100 used for generating the representative data 50 in addition to the representative data 50. That is, in the target data output method according to the embodiment, the instantaneous data 100 used to generate the representative data 50 is reused as the target shape data.

  As described above, in the target data output method according to the embodiment, the shape of the target can be correctly recognized by adding the instantaneous data 100 to the representative data 50 as shape data indicating the shape of the object. Note that the instantaneous data 100 is an example of shape data, and may be other data as long as the data indicates a shape.

  Next, the configuration of the radar device 1 according to the embodiment will be described in detail with reference to FIG. FIG. 2 is a block diagram illustrating a configuration of the radar device 1 according to the embodiment. In FIG. 2, only the components necessary for describing the features of the present embodiment are represented by functional blocks, and descriptions of general components are omitted.

  As shown in FIG. 2, the radar device 1 includes a transmission unit 10, a reception unit 20, and a processing unit 30. The radar device 1 is connected to a vehicle control device 2 that controls the behavior of the host vehicle MC.

  The vehicle control device 2 controls a vehicle such as a PCS (Pre-crash Safety System) or an AEB (Advanced Emergency Braking System) based on the detection result of the target by the radar device 1.

  The transmission unit 10 includes a signal generation unit 11, an oscillator 12, and a transmission antenna 13. The signal generation unit 11 generates a modulation signal for transmitting a millimeter wave frequency-modulated by a triangular wave under the control of a transmission / reception control unit 31 described later. The oscillator 12 generates a transmission signal based on the modulation signal generated by the signal generation unit 11, and outputs the transmission signal to the transmission antenna 13. Note that, as shown in FIG. 2, the transmission signal generated by the oscillator 12 is also distributed to a mixer 22, which will be described later.

  The transmission antenna 13 converts a transmission signal from the oscillator 12 into a transmission wave, and outputs the transmission wave to the outside of the vehicle MC. The transmission wave output by the transmission antenna 13 is a continuous wave frequency-modulated by a triangular wave. A transmission wave transmitted from the transmission antenna 13 to the outside of the own vehicle MC, for example, forward, is reflected by a target such as another vehicle LC and becomes a reflected wave.

  The receiving unit 20 includes a plurality of receiving antennas 21 forming an array antenna, a plurality of mixers 22, and a plurality of A / D converters 23. The mixer 22 and the A / D converter 23 are provided for each reception antenna 21.

  Each receiving antenna 21 receives a reflected wave from the target as a received wave, converts the received wave into a received signal, and outputs the signal to the mixer 22. Although the number of receiving antennas 21 shown in FIG. 2 is four, it may be three or less or five or more.

  The reception signal output from the reception antenna 21 is input to the mixer 22 after being amplified by an amplifier (not shown) (not shown). The mixer 22 mixes a part of the distributed transmission signal and a part of the reception signal input from the reception antenna 21, removes unnecessary signal components, generates a beat signal, and outputs the beat signal to the A / D conversion unit 23. .

  The beat signal has a beat frequency that is the difference between the frequency of the transmission signal (hereinafter, referred to as “transmission frequency”) and the frequency of the reception signal (hereinafter, referred to as “reception frequency”). The beat signal generated by the mixer 22 is converted into a digital signal by the A / D conversion unit 23 after the timing is adjusted between the reception antennas 21 by a synchronization unit (not shown), and then output to the processing unit 30.

  The processing unit 30 includes a transmission / reception control unit 31, a signal processing unit 32, and a storage unit 36. The signal processing unit 32 includes a generation unit 33 and a filter processing unit 34.

  The storage unit 36 stores history data 36a. The history data 36a is information including the history of the target data and the history of the instantaneous data 100 in a series of signal processing executed by the signal processing unit 32.

  The processing unit 30 is, for example, a microcomputer including a CPU (Central Processing Unit), a ROM (Read Only Memory) and a RAM (Random Access Memory) corresponding to the storage unit 36, a register, and other input / output ports. The entire device 1 is controlled.

  When the CPU of the microcomputer reads out and executes the program stored in the ROM, the microcomputer functions as the transmission / reception control unit 31 and the signal processing unit 32. Note that the transmission / reception control unit 31 and the signal processing unit 32 may be entirely configured by hardware such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

  The transmission / reception control unit 31 controls the transmission unit 10 including the signal generation unit 11 and the reception unit 20. The signal processing unit 32 periodically executes a series of signal processing. Subsequently, each component of the signal processing unit 32 will be described.

  The generation unit 33 generates the instantaneous data 100. Specifically, the generation unit 33 generates the instantaneous data 100 by performing a frequency analysis process, a peak extraction process, and an instantaneous data generation process.

  In the frequency analysis processing, a fast Fourier transform (FFT: Fast Fourier Transform) processing (hereinafter, referred to as “FFT processing”) is performed on the beat signal input from each A / D conversion unit 23. The result of the FFT processing is a frequency spectrum of the beat signal, and a power value (signal level) for each frequency of the beat signal (for each frequency bin set at a frequency interval corresponding to the frequency resolution).

  In the peak extraction processing, a peak frequency that becomes a peak in the result of the FFT processing by the frequency analysis processing is extracted. In the peak extraction processing, a peak frequency is extracted for each of an “UP section” and a “DN section” of a beat signal described later.

  In the instantaneous data generation process, an angle estimation process for calculating an arrival angle and a power value of a reflected wave corresponding to each of the peak frequencies extracted in the peak extraction process is executed. At the time of execution of the angle estimation process, the arrival angle is an angle at which the target is estimated to be present, and thus may be referred to as an “estimated angle” below.

  In the instantaneous data generation process, a pairing process of determining a correct combination of the peak frequencies of the “UP section” and the “DN section” based on the calculation result of the calculated estimated angle and the power value is executed.

  In the instantaneous data generation processing, the distance of each target to the own vehicle MC and the relative speed of the direction to the own vehicle MC are calculated from the determined combination result. In the instantaneous data processing, the calculated estimated angle, distance, and relative speed of each target are output to the filter processing unit 34 as the instantaneous data 100 for the latest cycle (latest scan), and the history data 36a of the storage unit 36 is stored. To be stored.

  In order to make the description easy to understand, FIGS. 3 to 4C show the flow of processing from the pre-stage processing of the signal processing unit 32 to the processing in the signal processing unit 32. FIG. 3 is an explanatory diagram of the processing from the pre-processing of the signal processing unit 32 to the peak extraction processing of the generation unit 33.

  FIG. 4A is an explanatory diagram of the angle estimation process. 4B and 4C are explanatory diagrams of the pairing process. FIG. 3 is divided into three regions by two thick downward white arrows. Hereinafter, each of these areas is described as an upper row, a middle row, and a lower row.

  As shown in the upper part of FIG. 3, the transmission signal fs (t) is transmitted from the transmission antenna 13 as a transmission wave, is reflected by a target, arrives as a reflected wave, and is received by the reception antenna 21 at the reception signal fr (t). ).

  At this time, as shown in the upper part of FIG. 3, the reception signal fr (t) is delayed from the transmission signal fs (t) by a time difference T according to the distance between the host vehicle MC and the target. Due to the time difference T and the Doppler effect based on the relative speeds of the host vehicle MC and the target, the beat signal has a frequency fup in an “UP section” where the frequency increases and a frequency fdn in a “DN section” where the frequency decreases. Are obtained as a repeated signal (see the middle part of FIG. 3).

  The lower part of FIG. 3 schematically shows the result of the FFT processing of the beat signal in the frequency analysis processing for each of the “UP section” side and the “DN section” side.

  As shown in the lower part of FIG. 3, after the FFT processing, waveforms in the respective frequency regions on the “UP section” side and the “DN section” side are obtained. In the peak extraction processing, a peak frequency that becomes a peak in such a waveform is extracted.

  For example, in the case of the example shown in the lower part of FIG. 3, a peak extraction threshold is used. On the “UP section” side, peaks Pu1 to Pu3 are determined as peaks, respectively, and peak frequencies fu1 to fu3 are extracted.

  On the “DN section” side, the peaks Pd1 to Pd3 are determined as peaks by the same peak extraction threshold, and the peak frequencies fd1 to fd3 are respectively extracted.

  Here, the frequency component of each peak frequency extracted by the peak extraction processing may include a mixture of reflected waves from a plurality of targets. Therefore, in the instantaneous data generation process, an angle estimation process for calculating the azimuth for each of the peak frequencies is performed, and the presence of a target corresponding to each peak frequency is analyzed.

  The azimuth calculation in the instantaneous data generation processing can be performed using a known arrival direction estimation technique such as ESPRIT (Estimation of Signal Parameters via Rotational Invariance Techniques).

  FIG. 4A schematically shows the azimuth calculation result of the instantaneous data generation process. In the instantaneous data generation processing, from the peaks Pu1 to Pu3 of the azimuth calculation result, the estimated angles of the targets (each reflection point) respectively corresponding to these peaks Pu1 to Pu3 are calculated. The magnitude of each of the peaks Pu1 to Pu3 is a power value. In the instantaneous data generation process, as shown in FIG. 4B, the angle estimation process is performed on each of the “UP section” side and the “DN section” side.

  Then, in the instantaneous data generation process, a pairing process of combining peaks having close estimated angles and power values in the direction calculation result is performed. Further, from the combination result, in the instantaneous data generation processing, the distance of each target (each reflection point) corresponding to each combination of peaks and the relative speed of the direction to the own vehicle MC are calculated.

  The distance can be calculated based on the relationship of “distance∝ (fup + fdn)”. The relative speed can be calculated based on the relationship of “speed∝ (fup−fdn)”. As a result, as shown in FIG. 4C, a pairing processing result indicating the instantaneous data 100 of the estimated angle, the distance, and the relative speed of each reflection point RP with respect to the host vehicle MC is obtained.

  Returning to FIG. 2, the filter processing unit 34 will be described. As shown in FIG. 2, the filter processing unit 34 includes a prediction unit 34a, an assignment unit 34b, a weighting unit 34c, a resampling unit 34d, and a representative data generation unit 34e.

  The filter processing unit 34 generates target data corresponding to the instantaneous data 100 by applying a particle filter for analyzing the instantaneous data 100 generated by the generation unit 33 using a predetermined number of particle distributions. .

  In the particle filter, a plurality of hypotheses are set for the true state of the target and analyzed. The hypothesis is one assumed value for the state of the target such as a position or a speed. For example, in the position space, the hypotheses are scattered in a predetermined distribution and look like one moving particle, so the hypotheses are referred to as particles here. In addition, particle group data in which a predetermined number of particles are combined into one hypothesis is also used. For example, the particle group data is an average value of the state of the particles, and can be said to be one of the most likely hypotheses in the distribution of a predetermined number of particles.

  The prediction unit 34a performs prediction processing of particle group data and particles. Specifically, the prediction unit 34a sets the latest cycle to time t, and predicts the state of the particle and particle group data at time t based on the particle and particle group data at time t-1 in the previous cycle. For example, there is a method of predicting a motion model and a measurement period ΔT based on the state of particles and particle group data such as speed and position. That is, in the prediction process, the prediction unit 34a predicts the state of the particle and the particle group data at the time t from the state of the past particle and the particle group data.

  The allocating unit 34b performs a process of allocating the instantaneous data 100 in the latest cycle to the latest particle group data, which is the prediction result of the prediction unit 34a. Specifically, the allocating unit 34b determines the current time based on a predetermined allocation range corresponding to the current particle group data generated by the prediction unit 34a, a position difference between the instantaneous data 100 and the particle group data, a speed difference, and the like. Is assigned to the particle group data.

  In addition, when there is the instantaneous data 100 that does not exist in the allocation range of any target data, the allocating unit 34b treats the instantaneous data 100 as a new target. After performing a predetermined process on the new target, particle group data is generated, and particles are also given at the same time. The predetermined processing can also span the measurement cycle. For example, for a new target, the continuity is simply evaluated for several cycles, and it is confirmed that the continuity does not occur spontaneously due to noise or the like. It is also possible to generate particle group data later and add particles.

  The weighting unit 34c weights each particle forming the particle group data based on the current instantaneous data 100 allocated to the particle group data by the allocation unit 34b.

  For example, the weighting unit 34c increases the weight of particles similar to the current instant data 100 among the current particles, and decreases the weight of particles not similar to the current instant data 100. The degree of “similarity” here refers to an evaluation value of a cost function described based on, for example, a position difference or a speed difference.

  Next, the resampling unit 34d rearranges (resamples) the particles based on the weights of the current particles. Specifically, the resampling unit 34d moves particles having a small weight to a position near the instantaneous data 100 (having a large weight). More specifically, the resampling unit 34d rearranges particles whose weight is smaller than the predetermined threshold to particle data whose weight is equal to or larger than the predetermined threshold. Thereby, the current particle group data generated by the prediction can be corrected with the instantaneous data that may be closer to the true value. This makes it possible to correct the target data generated by the representative data generating unit 34e, which will be described later, so as to have higher accuracy, that is, closer to the true value.

  The representative data generation unit 34e generates target data, and generates representative data 50 in which the target data is aggregated for each same target. Specifically, first, the representative data generation unit 34e generates target data based on the current particle rearranged by the resampling unit 34d. For example, the representative data generation unit 34e generates a probability density function from the particle distribution and generates target data based on the center of gravity, or simply generates target data based on the average of each particle. Note that the particle group data is updated with the representative data.

  Subsequently, the representative data generation unit 34e performs grouping of integrating a plurality of target data as one target data of the same target. For example, the representative data generation unit 34e generates each target data when the plurality of target data exists within a predetermined range and the difference between the speed components included in each target data is equal to or less than a predetermined value. It is regarded as target data of the same target, and grouping is performed.

  After that, the representative data generation unit 34e generates the representative data 50 from the plurality of grouped target data. Further, the representative data generation unit 34e performs a process of associating the type of the target with the representative data 50.

  The representative data generation unit 34e classifies each target into a preceding vehicle, a stationary object (including a stationary vehicle), and an oncoming vehicle based on the relative speed. For example, the representative data generation unit 34e classifies a target having a relative speed that is the speed and direction of the own vehicle MC and is greater than the speed as the “preceding vehicle”.

  Further, for example, the representative data generation unit 34e classifies a target having a relative speed that is substantially opposite to the speed of the host vehicle MC as a “stationary object”. Further, for example, the representative data generation unit 34e classifies a target having a relative speed that is opposite to the speed of the own vehicle MC and is greater than the magnitude of the speed as an “oncoming vehicle”.

  Note that the representative data generation unit 34e obtains travel information including the steering angle and the travel speed of the own vehicle MC, and calculates the travel speed and the travel direction of the own vehicle MC, thereby obtaining the ground speed of the target. The target may be classified.

  Next, the output unit 35 will be described. The output unit 35 outputs, to the representative data 50 generated by the filter processing unit 34, output data Do obtained by adding shape data of a target based on the instantaneous data 100 used to generate the representative data 50. The shape data can use any element to represent the shape of the target. What to use may be appropriately determined according to the requirements of the system.

  For example, when the outline is expressed by a sequence of points, a data structure that outputs the positions of a plurality of instantaneous data 100, that is, a set of X and Y coordinates, may be used. In this case, for example, since the format is similar to the measurement data of one layer in LIDAR or the like, a format similar to the external shape processing performed at the output destination can be used.

  In addition, it is also possible to express a shape as a straight line, an L-shape, or a square by combining a series of points, and to output values characteristic of them as shape data. The data format of the shape data includes a vehicle control device such as image data indicating the shape of the other vehicle LC or coordinate data indicating the shape of the other vehicle LC in a two-dimensional coordinate system based on the radar device 1. It can be set as appropriate according to the specifications of 2. Alternatively, the shape data may be compressed and then output, and the shape data compressed at the output destination (for example, the vehicle control device 2) may be decompressed and used.

  5A to 5C are diagrams illustrating specific examples of the output data Do. Time t-1 and time t shown below indicate the passage of time, the current time is time t, and the previous scan at time t is time t-1.

  As shown in FIG. 5A, for example, when the amount of change in the distance and the azimuth between the representative data 50a at the time t-1 and the representative data 50b at the time t is equal to or less than a predetermined value, the output data Do at the time t includes the representative data. Shape data generated based on the instantaneous data 100a used for the data 50a and the instantaneous data 100b used for generating the representative data 50b are included.

  That is, in a period in which the change in the relative distance between the host vehicle MC and the other vehicle LC and the azimuth are equal to or smaller than the predetermined values, the shape data based on the past instantaneous data 100 in the period is included in the output data Do.

  In other words, during the period in which the relative positional relationship between the host vehicle MC, that is, the radar device 1 and the other vehicle LC is within a predetermined displacement, the present vehicle MC, based on the instantaneous data in the period including the past, is used. Generate shape data.

  That is, when the relative position change between the own vehicle MC and the other vehicle LC is small, the current shape data is generated based on the past instantaneous data 100a together with the present instantaneous data 100b. Note that the instantaneous data 100 may be used as it is as the shape data.

  Thus, at time t, even if the current instantaneous data 100b is small, shape data can be generated more precisely. For example, when the instantaneous data 100b is used for the current shape data, the number of pieces of data constituting the shape data can be increased, and a more detailed shape can be recognized. Here, the case where the past instantaneous data 100 is used as the current shape data based on the own vehicle MC has been described, but the present invention is not limited to this.

  Further, as shown in FIG. 5B, the relative positional relationship of the instantaneous data 100a based on the representative data 50a with respect to the representative data 50a at the time t-1 is stored, and the past data is stored based on the positional relationship. It is also possible to correct the instantaneous data 100a and generate shape data.

  In other words, even if the positions of the representative data 50a in the past and the representative data 50b of the present time are different, the relative positional relationship between the representative data 50b and the instantaneous data 100a is likely not to change between the past and the present. Therefore, this time, the shape data is generated not based on the past instantaneous data 100a itself, but based on the relative positional relationship between the instantaneous data 100a and the representative data 50a. Note that the instantaneous data 100b corrected based on the relative positional relationship may be used as it is as the shape data.

  Specifically, as shown in FIG. 5B, the position coordinates of each instantaneous data 100a based on the representative data 50a at the time t-1 are stored, and each instantaneous data 100a is stored based on the representative data 50b at the time t. Place.

  Accordingly, the output data Do at the time t includes the instantaneous data 100a at the time t-1 and the instantaneous data 100b at the time t. That is, by storing the coordinate position of each instantaneous data 100 based on the representative data 50, the relative distance between the own vehicle MC and the other vehicle LC or the angle of the other vehicle LC with respect to the own vehicle MC changes. However, the past instantaneous data 100b can be used for generating the current shape data.

  Therefore, even in such a case, more instantaneous data 100 can be used for generating shape data, so that a more detailed shape can be recognized.

  In such a case, for example, when the host vehicle MC or the other vehicle LC turns, the past instantaneous data 100 can be rotated according to the turning amount. That is, the above example is a case of the parallel movement, and the relative positional relationship between the representative data 50 and the instantaneous data 100 is used as it is. Further, correction by rotation may be performed.

  Specifically, as shown in FIG. 5C, for example, when the other vehicle LC turns from time t-1 to time t, the instantaneous data 100 at time t-1 is rotated based on the turning amount of the other vehicle LC. To generate shape data at time t.

  In such a case, based on the ground vector of the other vehicle LC obtained by subtracting the vector indicating the actual moving component of the own vehicle MC from the vector related to the relative speed of the own vehicle MC and the other vehicle LC included in the representative data 50b. The amount of turning is detected.

  That is, the turning amount of the other vehicle LC is detected by detecting the turning amount of the own vehicle MC based on the steering angle and the traveling speed of the own vehicle MC and subtracting the vector based on the moving component of the own vehicle MC from the vector based on the relative speed. Is detected.

  This makes it possible to accurately calculate the change in the direction between the own vehicle MC and the other vehicle LC, that is, the turning amount of the own vehicle MC and the other vehicle LC. Therefore, it is possible to reduce the error of the past instantaneous data 100 based on the turning.

  In other words, it is possible to improve the certainty of the shape data given to the output data Do. In the examples described so far, the case where the instantaneous data 100a at the time t-1 is used as the shape data has been described. However, the instantaneous data 100 before the time t-1 can be used as the shape data.

  Further, the method of detecting the turning amount of the other vehicle LC is not limited to the above example. For example, the radar device 1 may detect the turning amount of the other vehicle LC based on information acquired from another device mounted on the own vehicle MC, or may perform wireless communication such as inter-vehicle communication. The turning amount may be detected based on information obtained from another vehicle LC.

  Next, a processing procedure executed by the radar device 1 according to the embodiment will be described with reference to FIG. FIG. 6 is a flowchart illustrating a processing procedure executed by the radar device 1 according to the embodiment.

  As shown in FIG. 6, first, the generation unit 33 generates the instantaneous data 100 corresponding to the target based on the reflected wave of the transmitted radio wave reflected by the target (step S101).

  Subsequently, the prediction unit 34a of the filter processing unit 34 performs a prediction process of predicting the current particle data based on the previous particle data and a prediction data corresponding to the current target data based on the previous target data. Prediction processing is performed (step S102). Here, the prediction data indicates particle and particle group data.

  Subsequently, the allocating unit 34b allocates the current instantaneous data 100 to the current particle group data (Step S103). Subsequently, the allocating unit 34b determines the presence or absence of a new target based on the presence or absence of the instantaneous data 100 to which the current particle data has not been allocated (step S104). When the instantaneous data 100 is a new target (Step S104, Yes), the allocating unit 34b assigns predetermined particle group data (for example, particle groups in the initial state) to the instantaneous data 100 corresponding to the new target. Is set (step S111).

  Subsequently, when the instantaneous data 100 is not a new target (Step S104, No), the weighting unit 34c weights each of the current particle data based on the instantaneous data 100 (Step S105).

  Subsequently, the resampling unit 34d performs resampling of the current particle group data based on the weight by the weighting unit 34c (Step S106). Subsequently, the representative data generation unit 34e updates the probability density function of the resampled current particle group data, and generates target data based on the probability density function (step S107).

  Subsequently, the representative data generating unit 34e generates the representative data 50 from the target data of the same target (Step S108), and the output unit 35 generates the output data Do obtained by adding the shape data to the representative data 50 (Step S108). Step S109).

  Then, the output unit 35 outputs the output data Do (Step S110), and ends the processing.

  As described above, the radar device 1 according to the embodiment includes the generation unit 33, the filter processing unit 34, and the output unit 35. The generation unit 33 generates instantaneous data corresponding to the target based on the reflected wave of the transmission wave reflected by the target. The filter processing unit 34 generates the representative data 50 for each target based on the instantaneous data generated by the generation unit 33.

  The output unit 35 outputs, to the representative data 50 generated by the filter processing unit 34, output data Do obtained by adding target shape data based on the instantaneous data 100 used to generate the representative data 50. Therefore, according to the radar device 1 according to the embodiment, the shape of the target can be correctly recognized.

  By the way, in the above-described embodiment, the case where the shape data is generated using the instantaneous data 100 generated by one radar device 1 has been described, but the instantaneous data 100 generated by the plurality of radar devices 1 is integrated. It is also possible to generate shape data.

  FIG. 7 is a diagram illustrating a specific example of the output data Do according to the modification. Here, a case will be described in which the host vehicle MC includes a plurality of radar devices 1A to 1C that detect targets ahead of the host vehicle MC from different positions.

  For example, the radar device 1A shown in FIG. 7 generates shape data based on the instantaneous data 100B and the instantaneous data 100C generated by the radar devices 1B and 1C in addition to the instantaneous data 100A generated by the radar device 1A. It is possible to output output data Do including such shape data.

  In the example shown in FIG. 7, the radar device 1A generates instantaneous data 100A corresponding to the rear of the other vehicle LC, the radar device 1B generates the instantaneous data 100B corresponding to the left side of the other vehicle LC, and the radar device 1C A case where instantaneous data 100C corresponding to the right side of another vehicle LC is generated is shown.

  In this case, for example, the radar apparatus 1A selects the instantaneous data 100 corresponding to the same target from the instantaneous data 100A to 100C, and gives the selected instantaneous data 100A to 100C to the representative data 50 as shape data.

  That is, it is possible to add the instantaneous data 100 captured from various angles to the representative data 50 as shape data. As described above, by generating the shape data based on the instantaneous data 100 captured from various angles, it becomes possible to recognize the shape of the target viewed from various angles.

  Here, a case has been described where the radar apparatus 1A aggregates the instantaneous data 100 of the other radar apparatuses 1B and 1C, but a microcomputer or ECU that aggregates the instantaneous data 100 of the radar apparatuses 1A to 1C is separately provided. It may be. That is, the output unit 35 may be provided in a housing separate from the radar device 1.

  Also, for example, the radar device 1A may be configured to include a plurality of transmission units 10 and a plurality of reception units 20, and the radar device 1A may generate the instantaneous data 100B and the instantaneous data 100 corresponding to the instantaneous data 100C. Good.

  Further, in the above-described embodiment, the case where the shape data is generated based on the instantaneous data 100 has been described. However, the present invention is not limited to this, and the particle itself used in the particle filter and the probability density representing the particle distribution are used. Shape data may be generated using a function. Note that, for example, the particle itself or the probability density function may be used as it is for the shape data, or a particle processed into a predetermined format may be used as the shape data.

  Alternatively, the particle group data may be converted into a histogram, and the existence probability of the target at each position may be used as shape data. Even in such a case, it is possible to correctly recognize the shape of the target. Note that a Kalman filter such as an extended Kalman filter (EKF) or an unscented Kalman filter (UKF) may be used for calculating the existence probability.

  Further effects and modifications can be easily derived by those skilled in the art. Thus, the broader aspects of the present invention are not limited to the specific details and representative embodiments shown and described above. Accordingly, various modifications can be made without departing from the spirit or scope of the general inventive concept as defined by the appended claims and their equivalents.

REFERENCE SIGNS LIST 1 radar device 33 generation unit 34 filter processing unit 34a prediction unit 34b allocation unit 34c weighting unit 34d resampling unit 34e representative data generation unit 35 output unit 50 representative data 100 instantaneous data Do output data

Claims (7)

A generation unit that generates instantaneous data corresponding to the target based on the reflected wave of the transmitted wave reflected by the target,
A filter processing unit that generates representative data for each target based on the instantaneous data generated by the generation unit,
An output unit that outputs output data obtained by adding shape data of the target based on the instantaneous data used for generating the representative data to the representative data generated by the filter processing unit. Characteristic radar device. The output unit includes:
The shape data is generated based on the instantaneous data in a period in which a relative positional relationship between the representative data and the radar device falls within a predetermined displacement. The described radar device. The output unit includes:
The shape data based on the present instantaneous data and the past instantaneous data corrected based on the present representative data using the positional relationship between the past instantaneous data based on the past representative data. The radar device according to claim 1, wherein the radar device generates: The output unit includes:
The shape data is generated by rotating the past instantaneous data corresponding to the representative data in accordance with a turning amount of the target with respect to a road surface estimated based on the representative data. The described radar device. The output unit includes:
The shape data on the basis of the instantaneous data based on the transmission waves transmitted from the plurality of transmission units provided at different positions and the reception waves received by the reception units each forming a pair with the plurality of transmission units. The radar device according to any one of claims 1 to 4, wherein the radar device is generated. The filter processing unit includes:
For the instantaneous data, generate the representative data using a particle filter that performs analysis using a predetermined number of particle distribution,
The output unit includes:
The radar device according to any one of claims 1 to 5, wherein the shape data is generated based on a distribution shape of the particles. A generation step of generating instantaneous data corresponding to the target based on the reflected wave of the transmitted wave reflected by the target,
A filtering step of generating representative data for each target based on the instantaneous data generated by the generating step;
An output step of outputting output data obtained by adding shape data of the target based on the instantaneous data used for generating the representative data to the representative data generated by the filtering process. Characteristic target data output method.

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