CN116802516A

CN116802516A - Data processing method, device and vehicle

Info

Publication number: CN116802516A
Application number: CN202280000227.9A
Authority: CN
Inventors: 朱玉堂; 李美峰; 许少峰; 翟博韬; 张博
Original assignee: Huawei Technologies Co Ltd
Current assignee: Huawei Technologies Co Ltd
Priority date: 2022-01-21
Filing date: 2022-01-21
Publication date: 2023-09-22
Also published as: WO2023137682A1

Abstract

The application provides a data processing method, a data processing device and a vehicle, wherein the method can comprise the following steps: transmitting a transmitting signal through a detecting device and receiving an echo signal reflected by a target; determining an unambiguous speed measurement interval according to the real-time speed and the maximum unambiguous speed of the detection device; and determining the relative speed of the target relative to the detection device according to the echo signal and the real-time speed of the detection device, so that the relative speed is in a non-fuzzy speed measurement interval. According to the embodiment of the application, the real-time speed of the detection device is obtained, the non-fuzzy speed measurement interval of the detection device is dynamically updated, the condition that the detection device detects the occurrence of speed ambiguity of a target is reduced, the hardware complexity of the detection device is not required to be increased, the time-frequency resource of the detection device is saved, and the overall performance of the detection device is improved.

Description

Data processing method, device and vehicle

Technical Field

The present application relates to the field of intelligent steering, and more particularly, to a method, apparatus, and vehicle for data processing.

Background

With the development of society, intelligent terminals such as intelligent transportation devices, intelligent home devices, robots and the like are gradually entering into daily life of people. In particular in the field of autopilot, sensor technology is of paramount importance, whereas radar detection technology is an important part of sensor technology. Frequency modulated continuous wave (frequency modulated continuous wave, FMCW) radar is a common radar regime in automotive radar. In order to improve angular resolution, automotive radars typically employ a multiple-input multiple-output (multiple input multiple output, MIMO) antenna architecture. In order to accurately detect the speed of an object relative to an automotive radar, it is necessary to correspondingly enlarge the maximum unambiguous speed of the automotive radar, but this leads to deterioration of other indexes of the automotive radar. Therefore, how to reduce the ambiguity of the radar detection target speed without enlarging the maximum ambiguity speed of the radar and without deteriorating other indexes has become a urgent problem in the development of the automatic driving technology.

Disclosure of Invention

The application provides a data processing method, a device and a vehicle, which can reduce the condition that the speed of a target detected by a detection device is fuzzy, does not cause the deterioration of other indexes of the detection device, improves the overall performance of the detection device, does not additionally introduce phases among channels, ensures the accuracy of detecting the angle of the target, and does not need to increase the hardware complexity of the detection device.

In a first aspect, there is provided a method of data processing, the method comprising: transmitting a transmitting signal through a detecting device and receiving an echo signal reflected by a target; according to the real-time speed V of the detecting device _c With the maximum non-blurring speed V of the detecting device _max Determining a non-fuzzy speed measurement interval; based on echo signals and real-time velocity V _c And determining the relative speed V of the target relative to the detection device, so that the relative speed V is in a non-fuzzy speed measurement interval.

The detection means may be, for example, a radar. Specifically, it may be an FMCW radar. More specifically, the FMCW radar of the time division multiplexing-multiple input multiple output (time division multiplexing-multiple input multiple output, TDM-MIMO) system, the FMCW radar of the frequency division multiplexing-multiple input multiple output (frequency division multiplexing-multiple input multiple output, FDM-MIMO) system, and the FMCW radar of the code division multiplexing-multiple input multiple output (code division multiplexing-multiple input multiple output, CDM-MIMO) system are possible.

The transmission signal transmitted by the detection device may be a pulse signal, for example.

The real-time speed of the detection device and the maximum unambiguous speed of the detection device may be obtained from an external device or may be data information stored locally.

Based on the technical scheme, the non-fuzzy speed measurement interval of the detection device can be dynamically updated through the real-time speed of the detection device, the condition that the detection device detects the target speed is fuzzy is reduced, the maximum non-fuzzy speed of the detection device is not enlarged, the time-frequency resource of the detection device can be effectively saved, the deterioration of other indexes of the detection device is not caused, the overall performance of the detection device is improved, the phase among channels is not additionally introduced, the accuracy of detecting the target angle is ensured, and the hardware complexity of the detection device is not required to be increased.

With reference to the first aspect, in some implementations of the first aspect, the non-ambiguous speed measurement interval determined according to the real-time speed of the detection device is:

[-V _max -V _c ,V _max -V _c ]

it should be understood that the above technical scheme is applicable to TDM-MIMO mode FMCW radar, FDM-MIMO mode FMCW radar, and CDM-MIMO mode FMCW radar.

Based on the technical scheme, the length of the non-fuzzy speed measurement interval of the detection device is 2V _max . In the prior art, the length of the determined non-fuzzy speed measurement interval is more than 2V by directly expanding the maximum non-fuzzy speed of the detection device _max Thereby causing deterioration of other indexes of the detecting device. According to the scheme, time-frequency resources of the detection device can be effectively saved, the deterioration of other indexes of the detection device is not caused, the overall performance of the detection device is improved, the phase between channels is not additionally introduced, the accuracy of detecting the target angle is ensured, and the hardware complexity of the detection device is not required to be increased.

With reference to the first aspect, in some implementations of the first aspect, since the TDM-MIMO FMCW radar performs transmission of the multi-channel pulse signal in a round robin manner, the dynamically updated unambiguous speed measurement interval is not symmetrical about the origin 0. Therefore, in order for the TDM-MIMO FMCW radar to accurately resolve the relative velocity of the target, the velocity defuzzification scheme for the TDM-MIMO FMCW radar needs to include the following data preprocessing procedure:

determining first data, wherein the first data is obtained by performing distance dimension fast Fourier transform on echo signals received by a detection device; according to the real-time speed V of the detecting device _c And maximum non-blurring speed V _max Determining a first window function T (epsilon); from the first data and the first window function T (epsilon), a relative velocity V is determined.

For example, the virtual antenna channels synthesized by the MIMO radar transmitting and receiving antennas are more than one, and thus, the channels for receiving echo signals may be more than one. Therefore, the first data may be multidimensional data obtained by performing distance-dimensional fast fourier transform on the echo signal received by the detection device.

Based on the technical scheme, the signal spectrum of the echo signal speed dimension can be symmetrical about the real-time speed of the vehicle, the subsequent speed resolving process is not affected by the dynamic adjustment of the non-fuzzy speed measuring interval, and the resolving speed is in the non-fuzzy speed measuring interval.

With reference to the first aspect, in certain implementations of the first aspect, the first window function can be determined according to the following formulas (1) - (4):

ε＝1-V _c /V _max (2)

β(ε)＝[1，exp(jπε)，...，exp(jπε(L-1))] (4)

wherein ε represents the adjustment factor, α (ε) represents the first motion compensation factor, β (ε) represents the second motion compensation factor, N _t The number of transmitting antennas of the detecting device is represented, L represents the number of pulse signals transmitted by the transmitting antennas in a preset time length, T _w Representing a second window function between the pulse signals,represents N _r X 1-dimensional full 1 vector, +., Representing the kronecker product.

It should be appreciated that the second window function T _w Is a window function that increases between the chirp during the fast fourier transform of the velocity dimension when the unambiguous velocimetry interval is symmetric about the origin 0. The second window function has a plurality of forms, wherein the second window function is more commonly usedThe two-window function has a Hamming window, and is specifically characterized by the following formula (5):

where N represents the length of the hamming window.

The second window function commonly used is also the hanning window, specifically characterized by the following equation (6):

where N represents the length of the Hanning window.

With reference to the first aspect, in certain implementation manners of the first aspect, the FMCW radar speed disambiguation scheme for a TDM-MIMO system further includes the following speed resolving procedure:

determining second data, wherein the second data is data after the first data is added with a first window function and is subjected to speed dimension fast Fourier transform; determining the Doppler channel index N where the velocity unit length delta V and the relative velocity V of the detection device are located according to the second data _doppler The method comprises the steps of carrying out a first treatment on the surface of the According to the adjustment factor epsilon and the maximum non-blurring speed V _max Determining a compensation factor DeltaV; according to the length delta V of the speed unit and the Doppler channel index N _doppler And a compensation factor DeltaV, determining the relative velocity V.

The first data may be multi-dimensional data obtained by performing a distance-dimensional fast fourier transform on an echo signal received by the detection device. Therefore, the second data may be multidimensional data obtained by adding the first window function T (epsilon) to the first data and performing a speed-dimensional fast fourier transform.

Based on the technical scheme, when the TDM-MIMO mode FMCW radar adopts the method for dynamically updating the non-fuzzy speed measurement interval, the relative speed of the target can be accurately calculated according to the second data and the compensation factors determined after the data preprocessing, so that the condition of fuzzy speed of the radar detection target is reduced. Because the angle dimension Fourier transform of the FMCW radar is processed based on the result data after the velocity dimension Fourier transform, when the measured target has no velocity ambiguity, the accuracy of the angle dimension Fourier transform data processing result in the 3D-FFT processing can be effectively improved.

With reference to the first aspect, in certain implementations of the first aspect, the compensation factor Δv is determined according to the following formula (7):

ΔV＝-εV _max (7)

with reference to the first aspect, in certain implementations of the first aspect, the relative velocity V is determined according to the following equation (8):

V＝N _doppler δV+ΔV (8)

In a second aspect, there is provided an apparatus for data processing, comprising:

the control unit is used for controlling the detection device to emit signals and controlling the detection device to receive echo signals reflected by the target;

a determining unit for determining the real-time speed V of the detecting device _c Maximum non-blurring speed V of the detection device _max Determining a non-fuzzy speed measurement interval; and also for responding to echo signals and real-time velocity V _c And determining the relative speed V of the target relative to the detection device, so that the relative speed V is in a non-fuzzy speed measurement interval.

The detection means may be, for example, a radar. Specifically, it may be an FMCW radar. More specifically, the FMCW radar of TDM-MIMO system may be FMCW radar of FDM-MIMO system, or FMCW radar of CDM-MIMO system.

With reference to the second aspect, in some implementations of the second aspect, the non-ambiguous speed measurement interval determined according to the real-time speed of the detection device is:

[-V _max -V _c ,V _max -V _c ]

With reference to the second aspect, in some implementations of the second aspect, since the TDM-MIMO FMCW radar performs transmission of the multi-channel pulse signal in a round robin manner, and the dynamically updated unambiguous speed measurement interval is not symmetrical about the origin 0, the speed defuzzification scheme for the TDM-MIMO FMCW radar includes a data preprocessing procedure, in which the determining unit is specifically further configured to:

determining first data, wherein the first data is obtained by performing distance dimension fast Fourier transform on echo signals; according to real-time velocity V _c And maximum non-blurring speed V _max Determining a first window function T (epsilon); from the first data and the first window function T (epsilon), a relative velocity V is determined.

With reference to the second aspect, in certain implementations of the second aspect, the determining unit is further configured to be able to determine the first window function T (epsilon) according to the following formulas (1) - (4):

ε＝1-V _c /V _max (2)

β(ε)＝[1，exp(jπε)，...，exp(jπε(L-1))] (4)

Wherein ε represents the adjustment factor, α (ε) represents the first motion compensation factor, β (ε) represents the second motion compensation factor, N _t The number of transmitting antennas of the detecting device is represented, L represents the number of pulse signals transmitted by the transmitting antennas in a preset time length, T _w Representing a second window function between the pulse signals,represents N _r X 1-dimensional full 1 vector, +.,representing the kronecker product.

It should be appreciated that the second window function T _w Is a window function that increases between the chirp during the fast fourier transform of the velocity dimension when the unambiguous velocimetry interval is symmetric about the origin 0. There are a number of forms of the second window function, of which the more common second window function is a hamming window, specifically characterized by the following equation (5):

where N represents the length of the hamming window.

where N represents the length of the Hanning window.

With reference to the second aspect, in certain implementations of the second aspect, the determining unit is further configured to:

determining second data, wherein the second data is data after the first data is added with a first window function T (epsilon) and subjected to speed-dimensional fast Fourier transform; determining the Doppler channel index N where the velocity unit length delta V and the relative velocity V of the detection device are based on the second data _doppler The method comprises the steps of carrying out a first treatment on the surface of the According to the adjustment factor epsilon and the maximum non-blurring speed V _max Determining a compensation factor DeltaV; according to the length delta V of the speed unit and the Doppler channel index N _doppler And a compensation factor DeltaV, determining the relative velocity V.

With reference to the second aspect, in certain implementations of the second aspect, the determining unit is further configured to determine the compensation factor Δv according to the following formula (7):

ΔV＝-εV _max (7)

with reference to the second aspect, in certain implementations of the second aspect, the determining unit is further configured to determine the relative velocity V according to the following formula (8):

V＝N _doppler δV+ΔV (8)

in a third aspect, there is provided a data processing apparatus comprising a memory for storing computer instructions; also included is a processor for executing computer instructions stored in a memory to cause an apparatus to perform the method of any one of the possible implementations of the method designs of the first aspect described above.

In a fourth aspect, there is provided a vehicle, performing the method of the first aspect; alternatively, an apparatus comprising the data processing of the second aspect.

In the present application, a vehicle may include one or more different types of vehicles or movable objects that operate or move on land (e.g., highways, roads, railways, etc.), on water (e.g., waterways, rivers, oceans, etc.), or spatially. For example, the vehicle may include an automobile, a bicycle, a motorcycle, a train, a subway, an airplane, a ship, an aircraft, a robot, or other type of vehicle or movable object, or the like.

In a fifth aspect, a computer storage medium is provided, where computer instructions are stored in the computer storage medium, which instructions, when executed on a computer, cause the computer to perform the method of any one of the possible implementation manners of the method design of the first aspect.

In a sixth aspect, a chip is provided, comprising a processor for performing the method of any one of the possible implementations of the method designs of the first aspect.

The chip may be a baseband chip, for example.

In a seventh aspect, a computer program product is provided, which computer program code or instructions, when executed on a computer, cause the computer to perform the method of any one of the possible implementations of the method design of the first aspect described above.

Drawings

Fig. 1 is a functional block diagram of a vehicle 100 provided by an embodiment of the present application.

Fig. 2 is a schematic diagram of an FMCW radar and a CDM-MIMO FMCW radar according to an embodiment of the application.

Fig. 3 is a schematic diagram of an FMCW radar and a TDM-MIMO FMCW radar according to an embodiment of the present application.

Fig. 4 is a schematic diagram of three ways of improving the maximum unambiguous speed of FMCW radar provided by an embodiment of the application.

Fig. 5 is a schematic diagram of 3D-FFT signal processing provided by an embodiment of the present application.

Fig. 6 is a flow chart of a conventional 3D-FFT signal processing of FMCW radar provided by an embodiment of the application.

Fig. 7 is a schematic diagram of a method for dynamically updating a non-fuzzy speed measurement interval according to an embodiment of the present application.

Fig. 8 is a flowchart of a method for dynamically updating a non-fuzzy speed measurement interval according to an embodiment of the present application.

Fig. 9 is a flowchart of a method for improving preprocessing of 3D-FFT speed dimension data provided by an embodiment of the present application.

Fig. 10 is a flow chart of a method for improving 3D-FFT speed resolution provided by an embodiment of the present application.

Fig. 11 is a schematic block diagram of an apparatus 1100 for data processing according to an embodiment of the present application.

Detailed Description

The technical scheme of the application will be described below with reference to the accompanying drawings.

Fig. 1 is a functional block diagram illustration of a vehicle 100 provided in an embodiment of the present application. The vehicle 100 may include a perception system 120, a display device 130, and a computing platform 150, wherein the perception system 120 may include a variety of sensors that sense information regarding the surrounding environment of the vehicle 100. For example, the sensing system 120 may include a positioning system, which may be a global positioning system (global positioning system, GPS), or may be one or more of a Beidou system or other positioning system, an inertial measurement unit (inertial measurement unit, IMU), a lidar, a millimeter wave radar, an ultrasonic radar, and a camera device, wherein the radar may also be an FMCW radar.

Some or all of the functions of the vehicle 100 may be controlled by the computing platform 150. Computing platform 150 may include processors 151 through 15n (n is a positive integer), which is a circuit with signal processing capabilities, and in one implementation, may be a circuit with instruction fetch and execute capabilities, such as a central processing unit (central processing unit, CPU), microprocessor, graphics processor (graphics processing unit, GPU) (which may be understood as a microprocessor), or digital signal processor (digital signal processor, DSP), etc.; in another implementation, the processor may implement a function through a logical relationship of hardware circuitry that is fixed or reconfigurable, e.g., a hardware circuit implemented as an application-specific integrated circuit (ASIC) or a programmable logic device (programmable logic device, PLD), such as an FPGA. In the reconfigurable hardware circuit, the processor loads the configuration document, and the process of implementing the configuration of the hardware circuit may be understood as a process of loading instructions by the processor to implement the functions of some or all of the above units. Furthermore, a hardware circuit designed for artificial intelligence may be used, which may be understood as an ASIC, such as a neural network processing unit (neural network processing unit, NPU), tensor processing unit (tensor processing unit, TPU), deep learning processing unit (deep learning processing unit, DPU), etc. In addition, computing platform 150 may also include a memory for storing instructions, some or all of processors 151 through 15n may invoke the instructions in the memory, execution quality, to implement the corresponding functionality.

The technical solution of the embodiment of the present application may be applied to the vehicle 100 described above, and may also be applied to other vehicles. By way of example, the vehicle may include one or more different types of vehicles or movable objects that operate or move on land (e.g., highways, roads, railways, etc.), on water (e.g., waterways, rivers, oceans, etc.), or spatially. For example, the vehicle may include an automobile, a bicycle, a motorcycle, a train, a subway, an airplane, a ship, an aircraft, a robot, or other type of vehicle or movable object, or the like.

Compared with other range-finding speed measuring radars, the FMCW radar has a simpler structure. The FMCW radar has mature technology, low required transmitting power peak value, easy modulation, low cost and simple signal processing, thus being a common radar system in the automotive radar. It should be appreciated that conventional FMCW radar is a signaling mechanism employing single shot single receive (single input single output, SISO), however, in an autopilot scenario, since conventional FMCW radar is basically operated at medium and low pulse repetition frequencies, the factors determining the maximum unambiguous speed of FMCW radar are the pulse repetition frequency and pulse repetition signal wavelength of FMCW radar, and the lower the pulse repetition frequency of the radar, the lower the maximum unambiguous speed of the radar, while the pulse repetition signal wavelength remains unchanged. Therefore, when the speed of the detected target is too high and exceeds the maximum non-ambiguous speed of the conventional FMCW radar, a speed ambiguity problem, namely, a speed error detected by the FMCW radar, is caused. In addition, in this scenario, the distance and speed do not meet the requirements of autopilot for target detection, and an angle parameter needs to be introduced, so the angular resolution of FMCW radar is also important. Therefore, in order to improve the angular resolution, the automotive radar generally adopts an FMCW system of a MIMO antenna architecture, and the MIMO radar mainly includes an FDM-MIMO type FMCW radar, a CDM-MIMO type FMCW radar, and a TDM-MIMO type FMCW radar.

The speed measurement principle of the above-mentioned several MIMO radars is the same, and they all estimate the slow time frequency of the target, that is, the target doppler shift, by performing fourier transform on the slow time sampled signal of the target echo. Since the target doppler shift is mathematically equal to the ratio between the target velocity and 2 times the target echo wavelength, the target velocity is easily calculated based on the target doppler shift.

The key to MIMO radar implementation is how to distinguish the sounding signals of different transmit antennas from the target echo data. The detection signals are transmitted simultaneously by the respective transmission antennas of the FDM-MIMO and CDM-MIMO radars, the FDM-MIMO is distinguished by making the detection signals of the respective transmission antennas operate in different frequency bands, the CDM-MIMO radars are distinguished by making the detection signals of the respective transmission antennas modulate different phase codes, and the TDM-MIMO radars are distinguished by making the detection signals of the respective transmission antennas operate in different time periods.

The FDM-MIMO FMCW radar has the problems of wasting system radio frequency bandwidth, deteriorating the distance resolution capability, and deteriorating other indexes due to the maximum non-fuzzy speed of the FMCW radar. The CDM-MIMO mode FMCW radar needs to introduce phase shifters in different transmitting links, and code division multiplexing is achieved by adding different phase differences to pulse signals between each antenna channel. Fig. 2 is a schematic diagram of FMCW radar and CDM-MIMO mode FMCW radar transmitting pulse signals. Therefore, in the process of adding different phase differences for each antenna channel of the CDM-MIMO system FMCW radar, in practical application, the problem of introducing phase errors exists, so that the detection result is inaccurate, and the problem of deteriorating other indexes caused by the maximum non-fuzzy speed of the FMCW radar exists.

Considering factors such as hardware cost, in the automatic driving scene at the present stage, the FMCW radar of TDM-MIMO system is mainly used for target detection. The conventional FMCW radar adopts a SISO signal transceiving mechanism, and one pulse repetition period is formed by one pulse signal period, but one pulse repetition period of the TDM-MIMO mode FMCW radar is formed by jointly combining one pulse signal period transmitted by each of a plurality of antennas. Fig. 3 is a schematic diagram of FMCW radar and TDM-MIMO system FMCW radar transmitting pulse signals. The TDM-MIMO FMCW radar transmits pulse signals through a time division multiplexing mode through two antenna channels. Illustratively, compared with the conventional FMCW radar, the pulse repetition period of the TDM-MIMO system FMCW radar as shown in fig. 3 is 2 times that of the conventional FMCW radar, and the larger the pulse repetition period of the radar, and the smaller the pulse repetition frequency of the radar. Therefore, the TDM-MIMO system may further reduce the pulse repetition frequency of the FMCW radar. Also, factors determining the maximum unambiguous speed of the FMCW radar are the pulse repetition frequency and the pulse repetition signal wavelength of the FMCW radar, and when the pulse repetition signal wavelength is kept unchanged, the lower the pulse repetition frequency of the radar, the smaller the maximum unambiguous speed of the radar, and thus, the TDM-MIMO system may further reduce the maximum unambiguous speed of the FMCW radar.

Therefore, although the TDM-MIMO system FMCW radar can improve the angular resolution of a target, the problem of target speed blurring is more remarkable, and if the maximum non-blurring speed of the TDM-MIMO system FMCW radar is improved, other indexes of the radar are also deteriorated.

In one embodiment, the FMCW radar of various systems can basically accurately detect the speed of the normally running vehicle when the FMCW radar is stationary. However, in an autopilot scenario, the FMCW radar described above is typically used to detect objects during movement. Therefore, the relative speed of the measured target relative to the FMCW radar is easily caused to be larger than the maximum non-fuzzy speed, so that the condition that the speed of the measured target is fuzzy is caused, the range of the non-fuzzy speed measurement interval of the FMCW radar is more required, and the maximum non-fuzzy speed is more required: for example, the maximum non-ambiguous speed of the FMCW radar is 120km/h, and the vehicle carrying the FMCW radar and the vehicle to be tested travel in opposite directions and have speeds of 120km/h, in which case the relative speed of the vehicle to be tested may be 240km/h, exceeding the maximum non-ambiguous speed of the FMCW radar, the relative speed of the target to be tested detected by the FMCW radar is ambiguous without employing a speed disambiguation operation.

It should be understood that the maximum unambiguous speed of the FMCW radar and the target speed detected by the FMCW radar are one of the parameters set forth in the radar coordinate system, i.e. the relative speed, whereas the speed of the vehicle is a parameter in the precondition of the geodetic coordinate system, i.e. the absolute speed.

In order to solve the problem of the speed ambiguity of the FMCW radar, the most direct way is to expand the maximum non-ambiguity speed of the FMCW radar as much as possible, namely, an improved FMCW radar is provided, which can raise the maximum non-ambiguity speed of the FMCW radar according to the following three ways, so as to solve the problem that the speed ambiguity of a detected target detected by the FMCW radar is easy to cause when there is relative motion between the FMCW radar and the detected target. However, the index (e.g., range resolution, detection distance, etc.) of the FMCW radar deteriorates to different extents depending on the manner in which it is employed.

Fig. 4 is a schematic diagram of three ways of improving the maximum unambiguous speed of an FMCW radar.

Mode 1: the maximum detection distance of the FMCW radar is ensured to be unchanged, but the radio frequency bandwidth of the FMCW radar needs to be reduced, resulting in poor range resolution of the FMCW radar. The worse the FMCW radar range resolution, the more difficult it is to distinguish between two or more objects that are closer in distance. For example, the poor range resolution of the FMCW radar may cause the FMCW radar to be indiscriminate and not correctly achieve target recognition.

Mode 2: the distance resolution of the FMCW radar is ensured to be unchanged, but the frequency modulation slope of the FMCW radar needs to be improved, on the basis, if the maximum detection distance of the FMCW radar is kept unchanged, the intermediate frequency sampling bandwidth of an analog-to-digital converter (analog to digital converter, ADC) of the FMCW radar is increased, the requirement on hardware of the radar is very high, and the manufacturing cost of the radar is improved. In addition, if the ADC intermediate frequency sampling bandwidth of the FMCW radar is kept unchanged, the maximum detection distance of the FMCW radar is reduced, so that the maximum detection distance of a target of the MCW radar is shortened, and the early warning time of the FMCW radar on a remote target is influenced.

Mode 3: increasing the maximum non-ambiguity speed of the FMCW radar by decreasing the duty cycle of the waveform can result in a decrease in the cloud data rate of the target point, a decrease in the number of frames for data refresh during the same time, and inaccuracy in target identification, trajectory correlation.

In view of this, according to the data processing method provided by the embodiment of the application, the speed of the current vehicle is used to dynamically update the non-fuzzy speed measurement interval of the FMCW radar, so that the condition that the speed of the FMCW radar detected target is fuzzy is reduced, the maximum non-fuzzy speed of the FMCW radar is not enlarged, the time-frequency resource of the FMCW radar can be effectively saved, the deterioration of other indexes of the FMCW radar is not caused, the overall performance of the FMCW radar is improved, the phase between channels is not additionally introduced, the accuracy of detecting the target angle is ensured, and the hardware complexity of the FMCW radar is not increased.

As the TDM-MIMO mode FMCW radar adopts a round-robin mode to transmit the multichannel pulse signals, and the method for dynamically updating the non-fuzzy speed measurement interval of the FMCW radar is also adopted for the TDM-MIMO mode FMCW radar, the non-fuzzy speed measurement interval of the radar is not necessarily symmetrical about an origin (0) but symmetrical about the moving speed of the radar. The TDM-MIMO FMCW radar signal is processed by a three-dimensional fast Fourier transform (3D-fast fourier transform, 3D-FFT) method, the interval of processing signal data in the speed dimension by the conventional 3D-FFT is symmetrical about the origin (0), in the embodiment of the application, when the TDM-MIMO FMCW radar is processed in the speed dimension in the 3D-FFT, the window function between the linear frequency modulation signals (chirp) can be modified according to the real-time speed, so that the 3D-FFT can process based on the updated signal data in the non-fuzzy speed measurement interval, and finally the calculation of the measured target speed is completed.

It should be understood that the chirp signal may be understood as the pulse repetition signal described above in the embodiment of the present application.

The data processing method provided by the embodiment of the application can comprise the speed de-blurring processing process, can generate the adjusting factors and the compensating factors according to the real-time speed of the radar, and can modify the window function among the chirp in the signal processing of the 3D-FFT speed dimension according to the adjusting factors. And correcting the speed calculation result according to the compensation factor to obtain the speed of the measured target. The data processing method realizes the FMCW radar of TDM-MIMO system, is also suitable for updating the non-fuzzy speed measuring interval according to the real-time speed of the radar, reduces the speed fuzzy condition of the FMCW radar detection target speed, does not enlarge the maximum non-fuzzy speed of the FMCW radar, saves the time-frequency resource of the radar, and improves the overall performance of the radar.

The scheme can be realized based on the FMCW radar without increasing hardware complexity, and the real-time adjustment of the radar in a non-fuzzy speed measurement interval is realized through the optimization of a radar detection method, so that the problem of fuzzy speed of a detection target of the FMCW radar is solved.

To facilitate an understanding of embodiments of the present application, terms and concepts related to embodiments of the present application are first explained.

Maximum non-ambiguous speed when the maximum pulse phase shift from one pulse to the next pulse that can be measured by the FMCW radar is 180, the radial velocity value of the target object corresponding to the 180 pulse phase shift is the maximum non-ambiguous speed.

Doppler shift: there is relative motion between the radar and the target, the signal waveform is compressed or stretched, and the reflected echo of the radar generates a frequency offset proportional to the relative radial velocity of the scatterer and the radar, which is the doppler shift.

Chirp signal: chirp signals refer to signals whose frequency varies linearly continuously over a period of time, and are a common type of radar signal.

3D-FFT: a basic method for measuring distance, speed and angle is applied to millimeter wave radar. Fig. 5 is a schematic diagram of 3D-FFT signal processing provided by an embodiment of the present application. The 3D-FFT processes the signals from the distance dimension, the velocity dimension and the angle dimension, respectively.

The distance dimension is the first dimension, where the fast time sampled signals within the chirp of all channels are FFT processed. Taking MIMO radar as an example, the channels refer to virtual antenna channels synthesized by MIMO radar transmit-receive antennas.

The speed dimension is the second dimension, where the slow time sampled signals between the chirp of all virtual antenna channels are FFT processed.

The angle dimension is the third dimension, and the FFT processing is carried out on the sampling signals among all the virtual antenna channels in the dimension.

Furthermore, the fast fourier transform (fast fourier transform, FFT) can only transform time domain data of a limited length, and therefore signal truncation of the time domain signal is required before performing the FFT. However, if the time domain signal is not periodically truncated, abrupt changes occur at both ends of the truncated time domain waveform, resulting in spectrum leakage. Therefore, by adding a window function, abrupt changes at two ends of the truncated time domain waveform can be smoothed, and side lobes of a spectral window can be reduced, so that spectrum leakage can be reduced.

Fig. 6 is a flowchart of a conventional 3D-FFT signal processing of an FMCW radar according to an embodiment of the present application, and a specific 3D-FFT processing flow is as follows:

s610, performing ADC (analog-to-digital converter) sampling processing on the chirp signal to obtain a processed digital sampling signal, and adding a first window function into the chirp signal;

It should be appreciated that in the FMCW radar signal processing scenario, where the chirp signal is originally a continuous analog signal, the FMCW radar needs to sample the signal through the ADC to obtain a set of discrete digital sampled signals, which can cause spectrum leakage, so it is necessary to reduce the side lobes of the spectral window by adding a window function to the chirp signal to reduce spectrum leakage. There are a number of forms of window functions, of which the more common window functions are hamming windows, specifically characterized by the following equation (1):

where N represents the length of the hamming window.

Also commonly used window functions are hanning windows, specifically characterized by the following formula (2):

where N represents the length of the Hanning window.

In addition, there are other forms of window functions, as the application is not limited in this regard.

S620, performing fast Fourier transform on the signal with the window function added in the chirp to obtain a fast Fourier transform result signal of a distance dimension;

s630, acquiring a fast Fourier transform result signal of the distance dimension, and adding a window function between chirp;

similarly, the above-mentioned fast fourier transform result signal in the distance dimension also has a spectrum leakage phenomenon, so that a window function needs to be added between chirps to reduce side lobes of the spectrum window, so as to reduce spectrum leakage. Common window functions are hamming windows, hanning windows, etc.

S640, performing fast Fourier transform on the signal with the window function added between the chirp to obtain a fast Fourier transform result signal of a speed dimension;

s650, acquiring a fast Fourier transform result signal of the speed dimension, and adding a window function between antenna channels;

similarly, the fast fourier transform result signal in the above-mentioned velocity dimension also has a spectrum leakage phenomenon, so that a window function needs to be added between chirp to reduce side lobes of the spectrum window, so as to reduce spectrum leakage. Common window functions are hamming windows, hanning windows, etc.

In one embodiment, S650 also includes a target motion error compensation operation for the TDM-MIMO radar.

S660, performing fast Fourier transform on the signals with the window function added among the antenna channels to obtain fast Fourier transform result signals of any channel with an angle dimension;

it should be appreciated that the form of the window function added during the processing of the signals in each dimension does not affect each other during the 3D-FFT of the signals. For example, the window function added in chirp is a hanning window, and the window function added between chirp may be a hanning window, a hamming window, or another window function. Similarly, the window function added between the antenna channels can be a hanning window, a hamming window or other window functions. This embodiment is not limited thereto.

It will be appreciated that the radar described above enables detection of a target object by three-dimensional fast fourier transforms, the detection results being used for further analysis, e.g. target recognition, high-precision mapping, path planning, etc.

It should be appreciated that the 3D-FFT described above is applicable to the scenario of FMCW radar probe targets of TDM-MIMO system.

The velocity unit length, the radar can estimate the slow time frequency of the target, i.e., the target doppler frequency, by fourier transforming the slow time sampled signal of the target echo. Since the target doppler frequency is mathematically equal to the ratio between the target velocity and 2 times the target echo wavelength, the target velocity can be calculated based on the target doppler frequency. The speed unit refers to a speed interval corresponding to the interval of adjacent frequency points in the radar slow time Fourier transform, and the size of the speed unit is defined as the length of the speed unit.

The Doppler channel index refers to a frequency point index of the slow time Fourier transform output.

Fig. 7 is a schematic diagram of a method for dynamically updating a non-fuzzy speed measurement interval according to an embodiment of the present application. The vehicle 701 mounts a detection device.

It will be appreciated that in an operating condition of the detection device, the signal transmitting means transmits a pulse signal to the surroundings at a particular pulse repetition frequency, which pulse signal, when it encounters a target object, causes a reflection of the pulse signal, wherein a part of the reflected signal is received by the detection device and is thus reciprocated. The signal that this part reflects back and is received by the detection means is often referred to as echo signal. It should be understood that the pulse signal is an electromagnetic wave signal whose propagation speed is much greater than the speeds of the vehicle 701, the object 702, and the object 703, and thus the process of transmitting the pulse signal and receiving the echo signal by the probe device within a single chirp can be approximately regarded as unaffected.

In one embodiment, the detection device may be an FMCW radar.

For easy understanding, the present embodiment will be described in detail by taking the FMCW radar as an example of the detection device.

In one embodiment, the maximum unambiguous speed of the FMCW radar (denoted as V _max ) In an actual traffic scene, the FMCW radar can basically accurately measure the speed of a vehicle which normally runs on a road when the FMCW radar is stationary, namely, the speed value of the vehicle running relative to the stationary FMCW radar is smaller than or equal to the maximum non-fuzzy speed value.

It should be appreciated that the maximum unambiguous speed described above is one of the performance parameters of the FMCW radar itself.

Preferably, the FMCW radar is used for the explanation of the accurate speed measurement of a vehicle running on a road normally when the FMCW radar is stationary.

Under a normal traffic scene meeting the above conditions, there are two driving relations between vehicles: the same direction running and the opposite direction running.

In the first case, the FMCW radar-mounted vehicle 701 travels opposite to the target 702.

Wherein the speed of the vehicle 701 is denoted as V _c The moving speed of the object 702 is denoted as V ₁ 。

It should be appreciated that target 702 may be a moving vehicle, a stationary object, or a walking pedestrian, as the embodiments of the present application are not limited in this regard. However, whether a stationary object, a walking pedestrian, or other moving object, the velocity value relative to the FMCW radar is less than or equal to the maximum unambiguous velocity value of the FMCW radar in the normal traffic scenario set forth in this embodiment.

On the premise of meeting the requirement, V _c And V ₁ The following relationships should be satisfied respectively：

V _c ∈[0,V _max ],V ₁ ∈[-V _max ,0]

The FMCW radar mounted on the vehicle 701 emits a pulse signal to the surroundings at a prescribed pulse repetition frequency, and a part of the pulse signal is received by the radar in the form of an echo signal after encountering the target 702. At the same time, the computing platform of the automobile driving system can detect the real-time speed V of the vehicle 701 through the speed sensor _c . It should be understood that the radar is mounted on the vehicle 701, so the FMCW radar also has a motion speed V _c 。

In an embodiment of the present application, the relative speed of target 702 with respect to radar is noted as: v (V) ₁ ' so when V ₁ ∈[-V _max ,0]V at the time of ₁ ' the following relationship should be satisfied:

V ₁ ’∈[-V _max -V _c ,-V _c ]

based on the premise of the present embodiment, when the relative speed of the target 702 with respect to the radar satisfies the above relationship, the radar can clearly accurately detect the relative speed of the target 702, so as to accurately identify the target 702, and no speed ambiguity occurs.

In the second case, the FMCW radar-mounted vehicle 701 travels in the same direction as the target 703.

Wherein the speed of the vehicle 701 is denoted as V _c The moving speed of the target 703 is denoted as V ₂ 。

In the normal traffic scenario set forth in the present embodiment, the speed value of the target 703 relative to the FMCW radar is less than or equal to the maximum unambiguous speed value of the FMCW radar.

Under the premise of meeting the requirement, V _c And V ₂ The following relationships are satisfied:

V _c ∈[0,V _max ],V ₂ ∈[0,V _max ]

FMCW radar mounted on vehicle 701 according to a specified pulseThe repetition frequency is swept to transmit a pulse signal to the surroundings, wherein a portion of the pulse signal, after encountering the target 703, is received by the radar in the form of an echo signal. At the same time, the computing platform of the automobile driving system can detect the real-time speed V of the vehicle 701 through the speed sensor _c . It should be understood that the radar is mounted on the vehicle 701, so the FMCW radar also has a motion speed V _c 。

In an embodiment of the present application, the relative speed of the target 703 with respect to the radar is noted as: v (V) ₂ ' so when V ₂ ∈[0,V _max ]At the same time, V ₂ ' satisfy the following relationship:

V ₂ ’∈[-V _c ,V _max -V _c ]

based on the premise established in the present embodiment, when the relative speed of the target 703 with respect to the radar satisfies the above-described relationship, the radar is obviously capable of accurately detecting the relative speed of the target 703, is capable of accurately recognizing the target 703, and does not occur in a case of speed ambiguity.

In one embodiment, the relative speed of the radar detection target may also be the radial speed of the target relative to the radar, i.e. the speed component of the speed of the target under detection in the direction of the radar echo signal, i.e. the projection of the speed vector in the direction of the radar line of sight.

In the two cases, the maximum non-fuzzy speed of the FMCW radar and the real-time speed of the vehicle carrying the FMCW radar are obtained, and the non-fuzzy speed measurement interval for the FMCW radar to accurately measure the speed of the target in the two cases is determined by the FMCW radar:

[-V _max -V _c ,V _max -V _c ]

it should be understood that taking the radar detection of a vehicle traveling on a road as an example, the unambiguous speed measurement interval determined directly from the maximum unambiguous speed range of the FMCW radar is:

[-V _max ,V _max ]

as is clear from the above description, in a vehicle equipped with FMCW radarIn dynamic scenarios, if FMCW radar is also used as above [ -V _max ,V _max ]In order to avoid blurring the speed measurement interval, the condition of blurring the speed of the measured object is easy to be caused: for example, the radar-mounted vehicle and the vehicle to be tested travel in opposite directions, and the speed values are V _max The relative speed of the FMCW radar detection target with respect to itself is actually-2V _max Non-fuzzy speed measuring interval [ -V ] beyond FMCW radar _max ,V _max ]Therefore, the relative speed of the detected target is only the end value V of the non-blurred section _max Causing a speed ambiguity. To solve this problem, the most straightforward approach is to expand the maximum unambiguous speed of the radar, e.g. by a factor of 2, and thus expand the unambiguous speed measurement interval of the FMCW radar to [ -2V _max ,2V _max ]At this time, the length of the extended non-fuzzy speed measurement interval is 4V _max . However, as can be seen from the above description, expanding the maximum non-ambiguous speed of the FMCW radar by 2 times may cause deterioration of other indexes of the FMCW radar or increase the complexity and cost of hardware.

Further, taking the above case as an example, when the vehicle 701 and the vehicle 702 are traveling in opposite directions, the radar detects that the relative speed of the vehicle 702 should be in the interval [ -2V _max ,0]And (3) inner part. Taking the above-described case two as an example, when the vehicle 701 and the vehicle 703 travel in the same direction, the radar detects that the relative speed of the vehicle 703 should be within the interval [0, v _max ]And (3) inner part. Thus, if the maximum non-ambiguous speed is directly enlarged, the non-ambiguous speed measurement region of FMCW radar can be used only in the region [ -2V _max ,V _max ]For interval V _max ,2V _max ]Is not utilized, which also illustrates the scheme for solving the problem by enlarging the maximum non-ambiguous speed of the FMCW radar, and the waste of radar time-frequency resources is necessarily existed.

In the embodiment of the application, the FMCW radar dynamically updates the non-fuzzy speed measurement interval by acquiring the maximum non-fuzzy speed and the real-time speed of the vehicle 701: [ -V) _max -V _c ,V _max -V _c ]The interval eliminates the influence of the self-moving speed of the radar on the speed measuring process of the target, ensures that the detected relative speed of the measured target does not have speed blurring as far as possible, and the length of the interval is still 2V _max The maximum unambiguous speed of the radar is not enlarged either. In addition, the method provided by the embodiment of the application can dynamically update the non-fuzzy speed measurement interval along with the self-moving speed of the radar, and the maximum interval range of the interval capable of accurately measuring the speed is [ -2V _max ,V _max ]The range of the non-fuzzy speed measurement interval is adaptively adjusted by utilizing the relatively smaller maximum non-fuzzy speed to cover [ -2V _max ,V _max ]The interval range of the radar is not wasted, the whole performance of the radar is improved, meanwhile, the method can be realized based on the FMCW radar, no new measurement error is introduced, and the complexity of hardware is not increased.

Fig. 8 shows a flowchart of a method for dynamically updating a non-fuzzy speed measurement interval according to an embodiment of the present application.

S810, transmitting a transmitting signal through a detecting device and receiving an echo signal reflected by a target;

in one embodiment, the detection device is an FMCW radar.

In one embodiment, the transmission signal is a pulse signal, and in particular, may be a pulse repetition signal.

S820, determining a non-fuzzy speed measurement interval according to the real-time speed of the detection device and the maximum non-fuzzy speed of the detection device.

It should be understood that the real-time speed and the maximum non-ambiguous speed of the detection device are known parameters, and may be obtained from an external device or may be locally stored data information, which is not limited by the embodiment of the present application.

In one embodiment, the FMCW radar can also adjust its pulse repetition frequency and/or the wavelength of the pulse signal in real time according to the road speed limit information, so as to adjust its maximum non-fuzzy speed, and because the road speed limit is usually much smaller than the highest speed of the vehicle, the radar dynamically updates its maximum non-fuzzy speed according to the road speed limit information on the basis of dynamically updating the non-fuzzy speed measurement section according to its own moving speed, so that the non-fuzzy speed section of the radar can be further reduced, thereby further saving the time-frequency resource of the radar and improving the overall performance of the radar.

For ease of understanding, embodiments of the present application will be described in detail with respect to a scenario with a fixed pulse repetition frequency only. But in the scene that the pulse repetition frequency is updated in real time according to the road speed limit information, the method for dynamically updating the non-fuzzy speed measurement interval provided by the embodiment of the application is also applicable.

In one embodiment, the radar may be mounted on a vehicle, and thus, the real-time speed of the radar may be equal to the real-time speed of the vehicle on which the radar is mounted. It should be understood that the computing platform may obtain the real-time speed of the vehicle through a sensor, for example, through a hall sensor, etc., and may also obtain the real-time speed of the vehicle through other manners. The radar acquires the real-time speed of the vehicle from the sensor every time the radar transmits a pulse signal outwards.

In one embodiment, according to the non-ambiguous speed measurement intervals in case one and case two shown in fig. 6, the non-ambiguous speed measurement interval is finally determined as follows:

[-V _max -V _c ,V _max -V _c ]

and S830, determining the relative speed of the target relative to the detection device according to the echo signal and the real-time speed of the detection device, so that the relative speed is in a non-fuzzy speed measurement interval.

It should be understood that, according to the data processing method provided by the embodiment of the application, the non-fuzzy speed measurement interval of the FMCW radar is dynamically updated according to the speed of the current vehicle, the condition that the speed of a radar detection target is fuzzy is reduced, and the maximum non-fuzzy speed of the FMCW radar is not enlarged. Compared with the improved FMCW radar, the method can effectively save time-frequency resources of the FMCW radar, does not cause deterioration of other indexes of the radar, improves the overall performance of the FMCW radar, does not additionally introduce phases among channels, ensures the accuracy of detecting the angle of a target, and does not need to increase the hardware complexity of the radar.

For CDM-MIMO mode FMCW radar, after updating the non-fuzzy speed measurement interval, directly utilizing the real-time speed V of the radar _c And (3) performing Fourier transform on the slow time sampling signal, and compensating the target speed obtained by resolving.

After updating the non-fuzzy speed measurement interval for the FDM-MIMO FMCW radar, the real-time speed V of the radar is directly utilized _c And (3) performing Fourier transform on the slow time sampling signal, and compensating the target speed obtained by resolving.

It should be understood that, unlike the CDM-MIMO FMCW radar and the FDM-MIMO FMCW radar, which transmit multi-channel pulse signals in a concurrent manner, the TDM-MIMO FMCW radar transmits multi-channel pulse signals in a round-robin manner, and, when the TDM-MIMO FMCW radar is in a moving state, the radar dynamically updates an unambiguous speed measurement section according to its real-time speed, which is not symmetrical about the origin 0, and in general, the speed-resolving process of the TDM-MIMO FMCW radar needs to be improved, in particular, a window function added between chirp is improved, so that the speed-dimensional fast fourier transform processes based on the sampled signals of the unambiguous speed measurement section, and the speed value of the measured object is resolved.

The above-described velocity de-blurring scheme is divided into two parts: an improved 3D-FFT speed dimension data preprocessing method and an improved 3D-FFT speed resolution method. Therefore, for the TDM-MIMO mode FMCW radar, the data processing method provided by the embodiment of the application can comprise the speed de-blurring processing process.

Fig. 9 shows a flowchart of a method for improving 3D-FFT speed dimension data preprocessing provided by an embodiment of the present application.

S910, determining first data, wherein the first data is obtained by performing distance dimension fast Fourier transform on the echo signal.

In one embodiment, for the TDM-MIMO FMCW radar, the radar has a hardware characteristic of multiple input multiple output, so that the radar has a case that multiple antenna channels receive echo signals, and the radar needs to perform 3D-FFT processing on the echo signals received by the multiple antenna channels. Therefore, the first data may be multidimensional data obtained by performing distance-dimensional fast fourier transform on the echo signal received by the detection device.

It should be appreciated that the echo signal described above is a sampled signal.

After receiving echo signals, the conventional TDM-MIMO FMCW radar firstly needs to perform a distance-dimension FFT on the signals to obtain signal data after distance-dimension fast Fourier transform. Since the radar has a plurality of antenna channels for receiving echo signals, the data after the distance-dimensional fast fourier transform can be signal data of the plurality of antenna channels, that is, the signal data is multidimensional, and similarly, the signal data after the speed-dimensional and angle-dimensional fast fourier transform can also be multidimensional, which is not limited by the embodiment of the application.

S920, determining a first window function according to the real-time speed and the maximum non-fuzzy speed of the detection device.

In one embodiment, the adjustment factor ε is determined and a second window function T between the chirps is obtained before the first window function is determined _w 。

In one embodiment, the adjustment factor may be determined according to the following formula (3):

ε＝1-V _c /V _max (3)

wherein V is _c Indicating the real-time speed of the detecting device, V _max Indicating the maximum non-ambiguous speed of the detection means.

It should be appreciated that the second window function T _w When the non-fuzzy speed measurement interval is symmetrical about the origin 0, a window function is added between the chirp in the speed dimension fast Fourier transform process, and a commonly used second window function is a Hamming window, a Hanning window and the like.

Furthermore, it is also necessary to obtain a first motion compensation factor α (ε) within the chirp of each channel and a second motion compensation factor β (ε) between the chirp of each channel.

In one embodiment, the first motion compensation factor α (ε) may be a vector that may be determined by equation (4) as follows:

in one embodiment, the second motion compensation factor β (ε) may be a vector that may be determined by equation (5) below:

β(ε)＝[1，exp(jπε)，...，exp(jπε(L-1))] (5)

in the above formula (4) and formula (5), N _t Representing the number of transmit antennas, L represents the number of transmit chirp of a single transmit antenna in one frame time.

It should be understood that, because the technical solution of the present application is based on the above-mentioned non-fuzzy speed measurement interval dynamically adjusted according to the real-time speed of the detecting device, the first motion compensation factor and the second motion compensation factor are also adjusted in real time according to the speed of the detecting device, the signal in chirp can be subjected to corresponding motion compensation by the first motion compensation factor, and the signal between chirp can be subjected to corresponding motion compensation by the second motion compensation factor.

After obtaining the second window function T _w After the first motion compensation factor α (epsilon) and the second motion compensation factor β (epsilon), the first window function T (epsilon) between the chirp can be determined based on the adjustment factors.

In one embodiment, the first window function T (ε) may be determined by equation (6) below:

wherein,is N _r X 1-dimensional full 1 vector, N _r Indicating the number of receiving antennas, L indicating the number of transmitting chips of a single transmitting antenna in one frame time, and # indicating the dot product,representing the kronecker product.

It is understood that equation (6) can be derived from equations (3) - (5).

In one embodiment, the multidimensional data Y after the first window function is added between the chirp on any distance channel can be obtained according to the first window function T (epsilon) obtained by the above process.

In one embodiment, the multidimensional data Y after completing the inter-chirp addition of the first window function on any distance channel can be determined by the following formula (7):

wherein X is multidimensional data of a target echo in any distance channel after distance dimension FFT.

It should be understood that the above X may be obtained by performing a distance dimension FFT on each distance channel echo signal according to the existing 3D-FFT processing flow.

S930, determining the relative speed of the measured object according to the first data and the first window function.

It will be appreciated that after the improved 3D-FFT speed dimension data preprocessing described above, an improved 3D-FFT speed resolution is also required.

Fig. 10 shows a flowchart of a method for improving 3D-FFT speed resolution provided by an embodiment of the present application.

S1010, determining second data, wherein the second data is data after the first data is added with a first window function and is subjected to speed dimension fast Fourier transform.

In one embodiment, the first data may be multidimensional data obtained by performing distance-dimensional fast fourier transform on the echo signal received by the detecting device, and therefore the second data may be multidimensional data obtained by performing speed-dimensional fast fourier transform on the first data after the first window function is added.

S1020, determining the Doppler channel index where the speed unit length and the relative speed of the detection device are located according to the second data.

It should be understood that the above-mentioned detecting device may be a TDM-MIMO type FMCW radar, and the speed unit length is a system parameter of the TDM-MIMO type FMCW radar itself, which may be directly obtained.

In one embodiment, the Doppler channel index of the target can be obtained by the slow time Fourier transform.

S1030, determining a compensation factor according to the adjustment factor and the maximum non-blurring speed;

in one embodiment, the compensation factor may be determined according to the following equation (8):

ΔV＝-εV _max (8)

wherein DeltaV is the compensation factor described above. It will be appreciated that ε and V in equation (8) _max All the parameters are determined in the preprocessing process of the improved 3D-FFT speed dimension data, and the numerical value of the parameters is unchanged.

S1040, determining a compensated speed calculation result of the measured target according to the speed unit length of the detection device, the Doppler channel index of the measured target and the compensation factor.

In one embodiment, the velocity solution of the measured object may be determined according to the following formula (9):

V＝N _doppler δV+ΔV (9)

wherein N is _doppler Indicating the doppler channel index where the target is located and δv indicating the velocity unit length of the probe device.

It is understood that by the method for improving the 3D-FFT speed deblurring, the TDM-MIMO FMCW radar can accurately calculate the speed of a detected target by using the method for dynamically updating the non-blurred speed measurement interval according to the self-moving speed of the radar, and the condition of speed blurring of the radar detection target is reduced.

Because the angle dimension FFT in the FMCW radar 3D-FFT processing is processed based on the result data after the speed dimension FFT processing, when the measured target has no speed ambiguity, the multichannel data processed by the speed dimension FFT processing is also accurate, thereby effectively improving the accuracy of the angle dimension FFT data processing result in the 3D-FFT processing.

In addition, the TDM-MIMO mode FMCW radar realizes the data processing method in the embodiment, the non-fuzzy speed measuring interval of the radar can be dynamically updated through the real-time speed of the radar, the condition that the speed of a radar detection target is fuzzy is reduced, the maximum non-fuzzy speed of the radar is not enlarged, the time-frequency resource of the radar can be effectively saved, the deterioration of other indexes of the radar is not caused, the overall performance of the radar is improved, and the hardware complexity of the radar is not increased.

Fig. 11 shows a schematic block diagram of an apparatus 1100 for data processing according to an embodiment of the present application. As shown in fig. 11, the detecting device 1100 includes:

A control unit 1110, configured to control the probe device to transmit a signal, and control the probe device to receive an echo signal reflected by a target;

a determining unit 1120 for determining the real-time velocity V of the detecting device _c Maximum non-blurring speed V of the detection device _max And determining a non-fuzzy speed measurement interval.

Optionally, the determining unit 1120 is specifically further configured to: according to the maximum non-fuzzy speed and the real-time speed, the following non-fuzzy speed measurement interval is determined:

[-V _max -V _c ,V _max -V _c ]

optionally, the determining unit 1120 is specifically further configured to: and determining the speed of the target relative to the detection device according to the echo signal and the real-time speed, so that the calculated speed is in a non-fuzzy speed measurement interval.

Optionally, the determining unit 1120 is specifically further configured to: determining first data, wherein the first data is obtained by performing distance dimension fast Fourier transform on echo signals; a first window function is determined based on the real-time speed and the maximum non-ambiguous speed of the detection means.

Optionally, the determining unit 1120 is specifically further configured to: determining second data, wherein the second data is data after the first data is added with a first window function and is subjected to speed dimension fast Fourier transform; determining the Doppler channel index where the speed unit length and the relative speed of the detection device are located according to the second data; determining a compensation factor according to the adjustment factor and the maximum non-blurring speed; the relative velocity is determined based on the velocity unit length, the doppler channel index, and the compensation factor.

The embodiment of the application also provides an FMCW radar, which comprises the FMCW radar capable of realizing the method. Further, the radar further comprises a control chip, and the control chip is connected with the antenna device of the FMCW radar. The control chip is used for controlling the antenna device to transmit or receive signals.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.

In the several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.

The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

A method of data processing, comprising:

transmitting a transmitting signal through a detecting device and receiving an echo signal reflected by a target;

according to the real-time speed V of the detecting device _c With the maximum non-blurring speed V of the detection device _max Determining a non-fuzzy speed measurement interval;

based on the echo signal and the real-time velocity V _c And determining the relative speed V of the target relative to the detection device, so that the relative speed V is in the non-fuzzy speed measurement interval.
The method of claim 1, wherein the step of determining the position of the substrate comprises,

the non-fuzzy speed measurement interval is as follows:

[-V _max -V _c ,V _max -V _c ]。
the method according to claim 2, wherein said echo signal and said real-time velocity V are based on _c Determining the relative velocity V of the object with respect to the detection device, comprising:

Determining first data, wherein the first data is obtained by performing distance dimension fast Fourier transform on the echo signals;

according to the real-time velocity V _c And the maximum non-blurring speed V _max Determining a first window function T (epsilon);

the relative velocity V is determined from the first data and the first window function T (epsilon).
A method according to claim 3, wherein said transmitted signal comprises a pulsed signal, said signal being in accordance with saidReal-time velocity V _c And the maximum non-blurring speed V _max Determining a first window function T (epsilon) comprising:

determining the first window function according to the following formulas (1) - (4):

ε＝1-V _c /V _max (2)

β(ε)＝[1，exp(jπε)，…，exp(jπε(L-1))] (4)

wherein ε represents the adjustment factor, α (ε) represents the first motion compensation factor, β (ε) represents the second motion compensation factor, N _t The number of the transmitting antennas of the detecting device is represented, L represents the number of the transmitting antennas for transmitting the pulse signals in a preset time length, and T represents _w Representing a second window function between the pulse signals,represents N _r X 1-dimensional full 1 vector, +.,representing the kronecker product.
A method according to claim 3 or 4, wherein said determining said relative velocity V from said first data and said first window function T (e) comprises:

Determining second data, wherein the second data is data after the first data is subjected to speed dimension fast Fourier transform after the first window function T (epsilon) is added to the first data;

determining the speed unit length delta V of the detection device and the Doppler channel index N where the relative speed V is located according to the second data _doppler ；

According to the adjustment factor epsilon and the maximum non-blurring speed V _max Determining a compensation factor DeltaV;

according to the length delta V of the speed unit and the Doppler channel index N _doppler And the compensation factor Δv, determining the relative velocity V.
The method according to claim 5, wherein said adjusting factor ε and said maximum non-blurring speed V _max Determining the compensation factor Δv includes:

the compensation factor Δv is determined according to the following formula (5):

ΔV＝-εV _max 。 (5)
the method according to claim 5 or 6, characterized in that said doppler channel index N according to said velocity unit length δv _doppler And the compensation factor Δv, determining the relative velocity, comprising:

the relative velocity V is determined according to the following formula (6):

V＝N _doppler δV+ΔV。 (6)
the method according to any one of claims 1 to 7, wherein the detection means is a frequency modulated continuous wave FMCW radar of the time division multiplexed multiple input multiple output TDM-MIMO system.
A data processing apparatus, comprising:

the control unit is used for controlling the detection device to emit signals and controlling the detection device to receive echo signals reflected by the target;

a determining unit for determining the real-time speed V of the detecting device _c With the maximum non-blurring speed V of the detection device _max Determining a non-fuzzy speed measurement interval; and also for determining the echo signal and the real-time velocity V _c And determining the relative speed V of the target relative to the detection device, so that the relative speed V is in the non-fuzzy speed measurement interval.
The apparatus of claim 9, wherein the device comprises a plurality of sensors,

the non-fuzzy speed measurement interval is as follows:

[-V _max -V _c ,V _max -V _c ]。
the apparatus according to claim 10, wherein the determining unit is specifically configured to:

determining first data, wherein the first data is obtained by performing distance dimension fast Fourier transform on the echo signals;

according to the real-time velocity V _c And the maximum non-blurring speed V _max Determining a first window function T (epsilon);

the relative velocity V is determined from the first data and the first window function T (epsilon).
The apparatus according to claim 11, wherein the determining unit is specifically configured to:

Determining the first window function T (epsilon) according to the following formulas (1) - (4):

ε＝1-V _c /V _max (2)

β(ε)＝[1，exp(jπε)，…，exp(jπε(L-1))] (4)

wherein ε represents the adjustment factor, α (ε) represents the first motion compensation factor, β (ε) represents the second motion compensation factor, N _t The number of the transmitting antennas of the detecting device is represented, L represents the number of the transmitting antennas for transmitting the pulse signals in a preset time length, and T represents _w Representing a second window function between the pulse signals,represents N _r X 1-dimensional full 1 vector, +.,representing the kronecker product.
The apparatus according to claim 11 or 12, wherein the determining unit is specifically configured to:

determining second data, wherein the second data is data after the first data is subjected to speed dimension fast Fourier transform after the first window function T (epsilon) is added to the first data;

determining the speed unit length delta V of the detection device and the Doppler channel index N where the relative speed V is located according to the second data _doppler ；

According to the adjustment factor epsilon and the maximum non-blurring speed V _max Determining a compensation factor DeltaV;

according to the length delta V of the speed unit and the Doppler channel index N _doppler And the compensation factor Δv, determining the relative velocity V.
The apparatus according to claim 13, wherein the determining unit is specifically configured to:

The compensation factor Δv is determined according to the following formula (5):

ΔV＝-εV _max 。 (5)
the apparatus according to claim 13 or 14, wherein the determining unit is specifically configured to:

the relative velocity V is determined according to the following formula (6):

V＝N _doppler δV+ΔV。 (6)
the apparatus according to any one of claims 9 to 15, wherein the detection means is a frequency modulated continuous wave FMCW radar of the time division multiplexed multiple input multiple output TDM-MIMO system.
A data processing apparatus, comprising:

a memory for storing computer instructions;

a processor for executing computer instructions stored in the memory to cause the apparatus to perform the method of any one of claims 1 to 8.
A vehicle comprising the apparatus of any one of claims 9 to 17.
A computer storage medium having stored therein computer instructions which, when executed on a computer, cause the computer to perform the method of any of claims 1 to 8.
A chip comprising a processor for performing the method of any one of claims 1 to 8.
A computer program product, characterized in that the computer program code or instructions, when executed on a computer, cause the computer to perform the method of any of claims 1 to 8.