WO2023226388A1

WO2023226388A1 - Data processing method and apparatus

Info

Publication number: WO2023226388A1
Application number: PCT/CN2022/139416
Authority: WO
Inventors: 闫鸿慧; 康文武; 张蓉
Original assignee: 华为技术有限公司
Priority date: 2022-05-25
Filing date: 2022-12-15
Publication date: 2023-11-30
Also published as: CN117169888A

Abstract

A data processing method and apparatus. The data processing method comprises: a radar device (101) dividing, into L pieces of second fast time dimension and slow time dimension data and along a fast time dimension, first fast time dimension and slow time dimension data acquired by each of N receiving antennas; then, performing 2DFFT on each piece of second fast time dimension and slow time dimension data, so as to obtain L*N pieces of first range-Doppler map data, and extracting, from each piece of first range-Doppler map data, target data at a first target range and a first target speed, so as to obtain L*N pieces of target data; and finally, determining L groups of data from the L*N pieces of target data, wherein each group of data comprises N pieces of target data, and the N pieces of target data correspond to N different receiving antennas. By means of the data processing method and apparatus, the angular resolution of a radar device (101) may not be reduced, thereby improving the accuracy of a detected target object (102) at a first target range and a first target speed.

Description

Data processing method and processing device

This application claims priority to the Chinese patent application filed with the State Intellectual Property Office of China on May 25, 2022, with application number 202210577346.2 and the application name "Data Processing Method and Processing Device", the entire content of which is incorporated into this application by reference. middle.

Technical field

The present application relates to the field of radar technology, and in particular, to a data processing method and processing device.

Background technique

Vehicle-mounted radar equipment is mainly used to detect the road conditions in the current driving area of the vehicle, and plays an important role in assisting the vehicle in avoiding obstacles and sensing the surrounding environment of the vehicle. Among them, the target detection of vehicle-mounted radar equipment mainly includes obtaining the distance between the target point and the vehicle, the speed of the target point, and the angle information between the target point and the vehicle.

One method for vehicle-mounted radar equipment to detect targets is as follows: obtain the fast-time dimension-slow-time dimension data collected by each of the N receiving antennas of the radar, and conduct two-dimensional data on each fast-time dimension-slow time dimension data. Discrete fast Fourier transform (two-dimensional discrete fast fourier transform, 2DFFT), obtain N range-doppler map (RD-Map) data, where each range-doppler map data includes velocity information and distance information; then, based on N RD-Map data, use non-coherent accumulation (NCI) and constant false-alarm rate (CFAR) to determine the target distance and target speed (the Only objects at the target distance and target speed are considered to be targets that need to be detected, and there may be multiple target points that need to be detected corresponding to the target distance and target speed), and the target distance is extracted from each RD-Map data and the data on the target speed to form a set of data including N pieces of data; then, based on this set of data, multiple sets of different data are obtained, where each set of data in the multiple sets of data includes M pieces of data among the N pieces of data, M is less than N; finally, multiple sets of data are input into the super-resolution algorithm to detect the angle of the target point that may exist in the target distance and target speed.

However, the above process results in low accuracy of detected target points in target distance and target speed.

Therefore, how to improve the accuracy of detected target distance and target speed has become an urgent technical problem to be solved.

Contents of the invention

This application provides a data processing method and processing device, which can improve the accuracy of the detected target object at the first target distance and first target speed.

In a first aspect, this application provides a data processing method, including: obtaining the first fast time dimension-slow time dimension data collected by each of the N receiving antennas of the radar device, and each first fast time dimension-slow time dimension data. The length of the fast time dimension in the time dimension data is M1; the first fast time dimension-slow time dimension data collected by each receiving antenna is divided into L second fast time dimension-slow time dimension data, each of which The length of the fast time dimension in the two fast time dimension-slow time dimension data is M2, and the length of the slow time dimension in each second fast time dimension-slow time dimension data is the same as the length of the second fast time dimension collected by each receiving antenna. The lengths of the slow and fast time dimensions in a fast time dimension-slow time dimension data are the same, and M2 is less than M1; for each of the L second fast time dimensions corresponding to each receiving antenna-each second slow time dimension data Perform a two-dimensional discrete Fourier transform on the fast time dimension-slow time dimension data to obtain L first range Doppler map data corresponding to each receiving antenna; obtain L first range Doppler map data corresponding to each receiving antenna. First target data on the first target distance and the first target speed in each first range Doppler map data; based on L*N corresponding to the N receiving antennas The first target data determines L groups of data, each group of data in the L groups of data includes N first target data, and the N first target data corresponds to N different receiving antennas one-to-one; for the L Group data are processed using preset super-resolution algorithms.

In this embodiment, the radar equipment divides the first fast time dimension-slow time dimension data acquired by each of the N receiving antennas into L second fast time dimension-slow time dimension data along the fast time dimension, so that For each receiving antenna, there will be L second fastest time dimension-slow time dimension data, and for N receiving antennas, there will be a total of L*N second fastest time dimension-slow time dimension data; after that, for L*N Each second fast time dimension-slow time dimension data in the second fast time dimension-slow time dimension data is subjected to two-dimensional discrete fast Fourier transform to obtain L*N first range Doppler map data, and The target data on the first target distance and the first target speed are extracted from each first range Doppler map data, that is, there are L*N target data, and finally the L*N target data are formed into L groups of data, Each set of data includes N target data and the N target data corresponds to N different receiving antennas.

It can be understood that in the existing technology, after the radar equipment obtains the N target data corresponding to the first target distance and the first target speed corresponding to the N receiving antennas, the N target data will first be formed into a group. data; then obtain multiple sets of data based on this set of data, where each set of data in the obtained multiple sets of data only includes M target data out of N target data, M is less than N; finally, use the preset Super-resolution algorithms process multiple sets of data. It can be seen that in the existing technology, when the radar equipment uses the preset super-resolution algorithm to process multiple sets of data, each set of data does not fully use the data collected by the N receiving antennas of the radar equipment. It should be understood that not fully using the data collected by the N receiving antennas of the radar equipment is essentially equivalent to reducing the antenna diameter of the radar equipment, which will lead to a reduction in the angular resolution of the radar equipment, further reducing the detected first target distance. and the accuracy of the target point on the first target speed is lower. In this embodiment, since each set of data input to the preset super-resolution algorithm includes N target data, and since the N target data correspond to N different receiving antennas, it is equivalent to maintaining the original The antenna diameter of some radar equipment, therefore, will not reduce the angular resolution of the radar equipment, that is, it will improve the accuracy of the detected target distance and target speed.

In conjunction with the first aspect, in a possible implementation, the method further includes: performing a two-dimensional discrete Fourier transform on the first fast time dimension-slow time dimension data collected by each receiving antenna to obtain the The second range Doppler map data corresponding to each of the receiving antennas is calculated; the N second range Doppler map data corresponding to the N receiving antennas are non-coherently accumulated NCI to obtain the third range Doppler map data. Le map; obtain the second target data on the third range Doppler map, the second target data is data that meets the preset conditions; convert the second target data on the third range Doppler map The distance information and speed information on the map are respectively determined as the first target distance and the first target speed.

In conjunction with the first aspect, in a possible implementation, the method further includes: non-coherently accumulating NCI on the L*N first range Doppler map data corresponding to the N receiving antennas, and obtaining the first Four range Doppler diagrams; obtaining third target data on the fourth range Doppler diagram, where the third target data is data that meets preset conditions; placing the third target data on the fourth range Doppler diagram. The distance information and speed information on the range Doppler map are respectively determined as the first target distance and the first target speed.

In conjunction with the first aspect, in a possible implementation, the method further includes: obtaining L second fast time dimension-slow time dimension data corresponding to any one of the N receiving antennas; Perform non-coherent accumulation NCI on the L second fast time dimension-slow time dimension data corresponding to any one of the receiving antennas to obtain the fifth range Doppler map; obtain the fourth target data on the fifth range Doppler map , the fourth target data is data that satisfies preset conditions; the distance information and speed information of the fourth target data on the fifth range Doppler map are respectively determined as the first target distance and the Describe the first target speed.

In conjunction with the first aspect, in a possible implementation, the preset condition includes: the data satisfying the preset condition is peak data in range Doppler map data.

In a second aspect, this application provides a data processing device, including: an acquisition module for acquiring the first fast time dimension-slow time dimension data collected by each of the N receiving antennas of the radar equipment, each first The length of the fast time dimension in the fast time dimension-slow time dimension data is M1; the processing module is used to divide the first fast time dimension-slow time dimension data collected by each receiving antenna into L second fast times dimension - slow time dimension data, the length of the fast time dimension in each second fast time dimension - slow time dimension data is M2, and the length of the slow time dimension in each second fast time dimension - slow time dimension data The length of the slow time dimension in the first fast time dimension-slow time dimension data collected by each receiving antenna is the same, and M2 is smaller than M1; the processing module is also used to calculate the L corresponding to each receiving antenna. Each second fast time dimension-slow time dimension data in the second fast time dimension-slow time dimension data is subjected to a two-dimensional discrete Fourier transform to obtain L first distances corresponding to each receiving antenna. Puler chart data; the acquisition module is also used to acquire the first target distance in each of the L first range Doppler chart data corresponding to each receiving antenna. and first target data at the first target speed; a processing module further configured to determine L groups of data based on L*N first target data corresponding to the N receiving antennas, each group of data in the L groups of data It includes N first target data, and the N first target data corresponds to N different receiving antennas one-to-one; the processing module is also used to process the L group of data using a preset super-resolution algorithm.

Combined with the second aspect, in a possible implementation, the processing module is further configured to: perform a two-dimensional discrete Fourier transform on the first fast time dimension-slow time dimension data collected by each receiving antenna, Obtain the second range Doppler map data corresponding to each of the receiving antennas; the processing module is also configured to perform non-transformation on the N second range Doppler map data corresponding to the N receiving antennas one-to-one. Coherently accumulate NCI to obtain a third range Doppler map; the acquisition module is also used to: obtain second target data on the third range Doppler map, where the second target data satisfies preset conditions data; the processing module is further configured to: determine the distance information and speed information of the second target data on the third range Doppler map as the first target distance and the first target speed respectively. .

Combined with the second aspect, in a possible implementation, the processing module is further configured to perform non-coherent accumulation NCI on the L*N first range Doppler map data corresponding to the N receiving antennas, Obtain a fourth range Doppler map; the acquisition module is also used to: acquire third target data on the fourth range Doppler map, where the third target data is data that meets preset conditions; the The processing module is further configured to: determine the distance information and speed information of the third target data on the fourth range Doppler map as the first target distance and the first target speed respectively.

In conjunction with the second aspect, in a possible implementation, the acquisition module is further configured to: acquire L second fast time dimension-slow time dimension data corresponding to any one of the N receiving antennas; The processing module is also used to perform non-coherent accumulation NCI on the L second fast time dimension-slow time dimension data corresponding to any one of the receiving antennas, and obtain the fifth range Doppler map; the acquisition module is also used In: obtaining the fourth target data on the fifth range Doppler map, the fourth target data is data that satisfies the preset conditions; the processing module is also used to: obtain the fourth target data at the location The distance information and speed information on the fifth range Doppler map are respectively determined as the first target distance and the first target speed.

Combined with the second aspect, in a possible implementation, the data satisfying the preset condition is peak data in range Doppler map data.

In a third aspect, this application provides an automatic driving equipment, including the device described in the second aspect or any possible implementation manner thereof.

Illustratively, the automatic driving device is a vehicle.

In a fourth aspect, the present application provides a data processing device, including: a memory and a processor; the memory is used to store program instructions; the processor is used to call the program instructions in the memory to execute the first aspect or any of them. One possible implementation is the method described in .

In a fifth aspect, the present application provides a computer-readable medium that stores program code for computer execution, and the program code includes a program code for performing the steps of the first aspect or any of the possible implementations thereof. instructions for the method described.

In a sixth aspect, the present application provides a computer program product. The computer program product includes computer program code. When the computer program code is run on a computer, the computer implements the first aspect or any one thereof. The methods described in Possible implementations.

Description of the drawings

Figure 1 is a schematic diagram of an application scenario provided by an embodiment of this application;

Figure 2 is a schematic structural diagram of target detection using radar equipment provided by an embodiment of the present application;

Figure 3 is a flowchart of the data processing method provided by the embodiment of the present application;

Figure 4 is a schematic structural diagram of dividing the first fast time dimension-slow time dimension data into L second fast time dimension-slow time dimension data provided by the embodiment of the present application;

Figure 5 is a schematic diagram of the process of obtaining L first range Doppler map data provided by an embodiment of the present application;

Figure 6 is a structural schematic diagram of extracting data on the first target distance and the first target speed provided by the embodiment of the present application;

Figure 7 is a structural schematic diagram of determining L groups of data based on L*N first target data provided by the embodiment of the present application;

Figure 8 is a structural representation of a data processing device provided by an embodiment of the present application;

Figure 9 is a schematic structural diagram of a data processing device provided by another embodiment of the present application.

Detailed ways

With the rapid development of science and technology, intelligent driving technology has received more and more attention, resulting in a variety of intelligent driving equipment. These intelligent driving equipment can include, for example, self-driving cars, drones, etc.

An important function that an intelligent driving device must have is the perception of the external environment to prevent collisions with other objects or people. Target recognition and detection through radar equipment has now become an important means for intelligent driving equipment to obtain its own posture and environmental information relative to the environment, and has been widely used in the field of intelligent driving.

Figure 1 is a schematic diagram of an application scenario provided by an embodiment of the present application. As shown in Figure 1, the radar equipment 101 will transmit a detection signal to a certain target area through the transmitting antenna. At this time, if the target area includes the target 102, the target 102 will reflect the echo signal. Correspondingly, the radar equipment The receiving antenna in 101 receives the echo signal reflected by the target 102. Specifically, if the radar device 101 includes N receiving antennas, each receiving antenna receives the echo signal reflected by the target 102; then, the radar device 101 is based on N echo signals received by N receiving antennas are used for target detection. Among them, when the radar device 101 performs target detection based on the echo signal, it mainly includes determining the distance between the target 102 and the radar device 101 based on the echo signal, the speed of the target 102 and the relationship between the target 102 and the vehicle-mounted radar device 101. angle information.

Optionally, the radar device 101 may be suitable for target detection through detection signals in application scenarios such as unmanned driving, autonomous driving, intelligent driving or connected driving.

Optionally, the target 102 may be an obstacle or pedestrian within the measurement range of the radar device 101 .

For example, the radar device 101 described in this application may be millimeter wave radar, lidar, ultrasonic radar, etc., which does not constitute a limitation of this application.

Optionally, the radar device 101 described in this application can also be applied to terminals. For example, the terminal can be a transportation vehicle or an intelligent device. The terminal can be a motor vehicle (such as an unmanned vehicle, a smart vehicle, an electric vehicle, a digital vehicle, etc.), a drone, a rail car, a bicycle, a traffic light, etc. The terminal can be a mobile phone, tablet computer, laptop computer, personal digital assistant, sales terminal, vehicle-mounted computer, augmented reality device, virtual reality, wearable device, vehicle-mounted terminal, etc.

Specifically, FIG. 2 is a schematic diagram of the target detection process in the prior art provided by this application.

As shown in Figure 2, the process of target detection by radar device 101 is as follows: for a radar device including receiving antenna 1, receiving antenna 2... receiving antenna N, the radar device first obtains the data collected by each of the N receiving antennas. Fast time dimension-slow time dimension data, and perform two-dimensional discrete fast fourier transform (2DFFT) on each fast time dimension-slow time dimension data to obtain N range Doppler maps (range-doppler map, RD-Map) data, where each range Doppler map data includes speed information and distance information; then, the radar equipment uses non-coherent accumulation based on N RD-Map data. accumulation (NCI) and constant false-alarm rate (CFAR) to determine the target distance and target speed (only objects at the target distance and target speed are considered to be targets that need to be detected), and from each RD- The target distance and target speed data (data on the black cells in Figure 2) are extracted from the Map data to form a set of N data; later, due to the same target distance and target speed in the corresponding RD-Map data There may be multiple targets at the same target speed. Therefore, the super-resolution algorithm will continue to be used for processing. Since the super-resolution algorithm must be based on multiple sets of data, after obtaining a set including N data After the data is obtained, multiple sets of different data will be obtained based on the set of data. Each set of data in the multiple sets of data includes M data out of N data, and M is less than N. Finally, the multiple sets of data are input to In the super-resolution algorithm, the angle of the target object that may exist in the target distance and target speed is detected.

However, the above process results in low accuracy in detecting targets at target distance and target speed, where angular resolution refers to the ability to distinguish targets at the same target distance and target speed.

After analysis, it was found that the reasons for the low accuracy of the detected target distance and target speed caused by the above process are as follows: the angular resolution of the radar equipment is related to the diameter of the antenna. Normally, the larger the diameter of the antenna of the radar equipment, the smaller the accuracy of the target. The larger, the higher the angular resolution of the radar device. However, in the current target detection process, when multiple sets of data are obtained based on a set of data that originally included N pieces of data, the multiple sets of data obtained only include the original M pieces. That is, each set of data input to the super-resolution algorithm only uses the data of the M receiving antennas of the radar, but does not completely use the data collected by the N receiving antennas of the radar. It should be understood that not fully using the data collected by the N receiving antennas of the radar is essentially equivalent to reducing the diameter of the radar antenna, which causes the radar to distinguish between the same distance and the same speed. The ability to distinguish between targets is reduced, that is, the angular resolution is reduced, which further reduces the accuracy of outputting targets at the same distance and speed. For example, the radar can originally distinguish two targets at the same distance and the same speed, that is, output two angle values. However, due to the reduced angular resolution, it only outputs the angle value of one target, resulting in the detection of The target is inaccurate.

In view of this, the present application provides a data processing method and processing device, which can improve the accuracy of the detected target object at the first target distance and first target speed.

Figure 3 is a schematic flowchart of a data processing method provided by an embodiment of the present application. As shown in Figure 3, the method of this embodiment includes S301, S302, S303, S304, S305 and S306. This data processing method can be performed by the radar device 101 shown in FIG. 1 .

S301. Obtain the first fast time dimension-slow time dimension data collected by each of the N receiving antennas of the radar device. The length of the fast time dimension in each first fast time dimension-slow time dimension data is M1.

It should be understood that for radar equipment, when using a transmitting antenna to transmit a signal (also known as transmitting a linear frequency modulation signal or a chirp signal), if the transmitted signal acts on a target, the target will reflect the signal to the radar equipment. In this embodiment, the transmitted signal is also called the original transmitted signal, and the signal reflected to the radar after being acted upon by the target object is also called an echo signal.

Specifically, if the radar device includes N receiving antennas, each receiving antenna will receive the echo signal. Moreover, after each receiving antenna receives the echo signal, it will conjugate and multiply the echo signal with the original transmit signal, that is, deskew processing, to convert the high-frequency signal into a low-frequency signal, and then pass it through the low-pass signal. filter, the original fast-time (fast-time) dimension-slow-time (slow-time) dimension data corresponding to each receiving antenna can be obtained.

In this embodiment, the original fast-time dimension-slow-time dimension data collected by each receiving antenna is also called the first fast time dimension-slow time dimension data, and for each first fast time dimension-slow time dimension data dimensional data, the length of its fast time dimension is M1. Among them, for the concepts and detailed introduction of fast-time and slow-time dimensional data, please refer to the description in related technologies and will not be described again here.

It can be understood that for N receiving antennas, there will be N first fast time dimension-slow time dimension data, and the length of the fast time dimension in any two first fast time dimension-slow time dimension data is the same , and the length of the slow time dimension in any two first fast time dimension-slow time dimension data is the same.

S302: Divide the first fast time dimension-slow time dimension data collected by each receiving antenna into L second fast time dimension-slow time dimension data, and the fast time in each second fast time dimension-slow time dimension data The length of the dimension is M2, and the length of the slow time dimension in each second fast time dimension-slow time dimension data is the same as the length of the slow time dimension in the first fast time dimension-slow time dimension data collected by each receiving antenna. , M2 is smaller than M1.

In this embodiment, after the radar device obtains the first fast time dimension-slow time dimension data collected by each receiving antenna, it will collect the first fast time dimension-slow time dimension data collected by each receiving antenna along the fast time dimension. The data is divided so that each receiving antenna can correspond to L fast time dimension-slow time dimension data.

In this embodiment, when dividing the first fast time dimension-slow time dimension data collected by each receiving antenna along the fast time dimension, if the fast time in each fast time dimension-slow time dimension data after division The dimension length is M2, then M2 should be smaller than the length M1 of the fast time dimension in the first fast time dimension-slow time dimension data.

In this embodiment, each divided fast time dimension-slow time dimension data is called the second fast time dimension-slow time dimension data.

In this embodiment, when the first fast time dimension-slow time dimension data collected by each receiving antenna is divided into L second fast time dimension-slow time dimension data, the L second fast time dimension-slow time dimension data The length of the slow time dimension in any two second fastest time dimension-slow time dimension data in the dimensional data is the same, and the length of each second fastest time dimension-slow time dimension data in the slow time dimension is the same as the second fastest time dimension-slow time dimension data. The lengths of the slow time dimensions in a fast time dimension-slow time dimension data are the same.

As an example, FIG. 4 is a schematic structural diagram of dividing the first fast time dimension-slow time dimension data into L second fast time dimension-slow time dimension data provided by the embodiment of the present application. In this embodiment, L equals 3 as an example. As shown in Figure 4, if the length of the fast time dimension in the first fast time dimension-slow time dimension data collected by the receiving antenna is 9, the length of the slow time dimension in the first fast time dimension-slow time dimension data collected by the receiving antenna is 4, that is, it can be considered as a matrix with 9 rows and 4 columns. When dividing the fast time dimension into 3 second fast time dimension-slow time dimension data, the first fast time dimension-slow time dimension data can be The first 3 rows in are divided into the first and second fastest time dimension - slow time dimension data, and the middle 3 rows in the first fast time dimension - slow time dimension data are divided into the second and second fastest time dimension - slow time Dimension data, divide the last three rows in the first fast time dimension-slow time dimension data into the third second fast time dimension-slow time dimension data. It can be seen that after dividing the first fast time dimension-slow time dimension data collected by each receiving antenna, each receiving antenna will correspond to 3 second fast time dimension-slow time dimension data.

It should be noted here that each number in the above-mentioned Figure 4 is only an example and does not constitute a limitation of the present application.

For another example, assume that the length of the fast time dimension in the first fast time dimension-slow time dimension data is 500, and the length of the slow time dimension is K. That is, the first fast time dimension-slow time dimension data can be considered as a 500-row K column matrix, then during specific division, the matrix of K columns including 1 to 400 rows can be divided into the first and second fast time dimension-slow time dimension data, and the matrix of K columns including 51 to 450 rows can be divided into the second second fastest time dimension-slow time dimension data, and divide the matrix of K columns including 101-500 rows into the third second fastest time dimension-slow time dimension data. It can be seen that after dividing the first fast time dimension-slow time dimension data collected by each receiving antenna, each receiving antenna will correspond to 3 second fast time dimension-slow time dimension data.

For another example, assume that the length of the fast time dimension in the first fast time dimension-slow time dimension data is 500, and the length of the slow time dimension is K. That is, the first fast time dimension-slow time dimension data can be considered as a 500-row K column matrix, then during specific division, the matrix of K columns including 1 to 450 rows can be divided into the first and second fast time dimension-slow time dimension data, and the matrix of K columns including 51 to 500 rows can be divided Into the second second fastest time dimension-slow time dimension data. It can be seen that after the first fast time dimension-slow time dimension data collected by each receiving antenna is divided, each receiving antenna will correspond to 2 second fast time dimension-slow time dimension data.

It can be understood that in this embodiment, after dividing the first fast time dimension-slow time dimension data collected by each receiving antenna into L second fast time dimension-slow time dimension data, for N receiving antennas, There will be L times N second fast time dimension-slow time dimension data, and the length of the fast time dimension in any two second fast time dimension-slow time dimension data is the same, and any two second fast time dimension-slow time dimension data are the same. The lengths of the slow time dimensions in fast time dimension-slow time dimension data are the same. That is, it can also be considered as: L times N second fast time dimension-slow time dimension data. The sizes of any two second fast time dimension-slow time dimension data are the same.

S303. Perform a two-dimensional discrete Fourier transform on each of the L second fast time dimension-slow time dimension data corresponding to each receiving antenna, and obtain the data corresponding to each receiving antenna. Corresponding L first range Doppler map data.

Usually, in order to achieve target detection, radar equipment will perform 2DFFT transformation on the fast time dimension - slow time dimension data collected by each receiving antenna after obtaining the fast time dimension - slow time dimension data collected by each receiving antenna. The distance Doppler map (RD-Map) data corresponding to the fast time dimension-slow time dimension data, where the distance Doppler map data from the RD-Map data can be considered to include the distance information and speed of multiple candidates. information.

In this embodiment, after dividing the first fast time dimension-slow time dimension data collected by each receiving antenna into L second fast time dimension-slow time dimension data, the L second fast time dimension-slow time dimension data are Each second fastest time dimension-slow time dimension data in the dimensional data is subjected to 2DFFT transformation to obtain the RD-Map data corresponding to each second fastest time dimension-slow time dimension data. In this embodiment, the RD-Map data obtained by performing 2DFFT transformation on the second fast time dimension-slow time dimension data is also called the first range Doppler map data.

Exemplarily, FIG. 5 is a schematic diagram of a process for obtaining L first range Doppler map data provided by an embodiment of the present application. In this example, take L equal to 3 as an example.

As shown in Figure 5, each dotted box in the fast time dimension-slow time dimension data represents a second fastest time dimension-slow time dimension data. When the fast time dimension-slow time dimension data is divided into three second fastest time dimensions, After the time dimension-slow time dimension data, the three second fast time dimension-slow time dimension data are respectively subjected to 2DFFT transformation to obtain three first range Doppler map data, in which each first range Doppler The graph data can reflect the distance information between each candidate among multiple candidates and the radar device and the speed information of each candidate.

It can be understood that in this embodiment, each of the L second fast time dimension-slow time dimension data corresponding to each receiving antenna is subjected to two-dimensional discrete Fourier analysis. After leaf transformation, for N receiving antennas, there will be L times N (L*N) first range Doppler map data.

S304: Obtain the first target distance and the first target data on the first target speed in each of the L first range Doppler map data corresponding to each receiving antenna.

In this embodiment, the distance information and speed information corresponding to the unit where the target object may be determined by the radar device are also called the first target distance and the first target speed respectively.

In this embodiment, there is no restriction on how the radar device determines the first target distance and the first target speed.

For example, in a first possible implementation manner, determining the first target distance and the first target speed includes: performing a two-dimensional discrete Fourier transform on the first fast time dimension-slow time dimension data collected by each receiving antenna, Obtain the second range Doppler map data corresponding to each receiving antenna; perform NCI on the N second range Doppler map data corresponding to the N receiving antennas to obtain the third range Doppler map; obtain the third range Doppler map data. The second target data on the three-range Doppler map, the second target data is data that meets the preset conditions; the distance information and speed information of the second target data on the third range Doppler map are determined respectively is the first target distance and the first target speed.

In this embodiment, N second range Doppler map data in one-to-one correspondence are obtained by performing 2DFFT transformation on the original first fast time dimension-slow time dimension data collected by N receiving antennas, and then the N second range Doppler map data are obtained The distance information and speed information corresponding to the data that meets the preset conditions on the range Doppler map (the third range Doppler map) obtained after NCI processing of the second range Doppler map data are determined as the first target distance and speed information respectively. First target speed.

For example, in the second possible implementation manner, determining the first target distance and the first target speed includes: obtaining L second fast time dimension-slow time dimension data corresponding to any one of the N receiving antennas; Perform non-coherent accumulation NCI on the L second fast time dimension-slow time dimension data corresponding to any receiving antenna to obtain the fifth range Doppler map; obtain the fourth target data on the fifth range Doppler map, so The fourth target data is data that satisfies preset conditions; the distance information and speed information of the fourth target data on the fifth range Doppler map are determined as the first target distance and the first target speed respectively.

In this embodiment, the range Doppler map obtained by NCI processing the L second fast time dimension-slow time dimension data corresponding to the first fast time dimension-slow time dimension data collected by any receiving antenna (th The distance information and speed information corresponding to the data meeting the preset conditions on the five-range Doppler diagram) are determined as the first target distance and the first target speed respectively. It can be understood that in this implementation, when performing NCI processing, it is equivalent to using the L second fast time dimension-slow time dimension data corresponding to one receiving antenna as representatives to determine the first target distance and the first target speed. .

For example, in a third possible implementation manner, determining the first target distance and the first target speed includes: performing non-coherent accumulation NCI on L*N first range Doppler map data corresponding to N receiving antennas, Obtain the fourth range Doppler map; obtain the third target data on the fourth range Doppler map, the third target data is data that meets the preset conditions; convert the third target data on the fourth range Doppler map The distance information and speed information on the map are respectively determined as the first target distance and the first target speed.

In this embodiment, the range Doppler map (the fourth range Doppler map) obtained by performing NCI processing on the L*N first range Doppler map data corresponding to the N receiving antennas satisfies the preset conditions. The distance information and speed information corresponding to the data (third target data) are respectively determined as the first target distance and the first target speed.

It can be understood that compared with the first implementation method or the second implementation method, this implementation method can increase the energy intensity of the acquired third target data, improve the signal-to-noise ratio, and thereby improve the capability of subsequent signal processing algorithms. .

Optionally, the above-mentioned data satisfying the preset conditions is, for example, peak data in a range Doppler map.

In this embodiment, after the radar device determines the first target distance and the first target speed, it needs to extract from each of the L*N first range Doppler map data. Output the data on the first target distance and the first target speed.

Exemplarily, FIG. 6 is a schematic structural diagram of extracting data on the first target distance and the first target speed provided by an embodiment of the present application. As shown in Figure 6, for a radar device including N receiving antennas, after S302 and S303, each receiving antenna will correspond to L first range Doppler map data. In this example, the first RD is used respectively. -Map, the second RD-Map... up to the L-th RD-Map, then in this embodiment, after determining the first target distance and the first target speed, it is necessary to obtain and Data on first target distance and first target speed. For example, the data on each black cell in Figure 6 is data on the first target distance and the first target speed.

S305: Determine L groups of data based on L*N first target data corresponding to N receiving antennas. Each group of data in the L groups of data includes N first target data, and the N first target data are consistent with N Different receiving antennas correspond one to one.

It should be understood that for a radar device including N receiving antennas, the first target distance in each of the L first range Doppler map data corresponding to each receiving antenna is obtained. After the first target data on the first target speed, there will be a total of L*N first target data.

In this embodiment, L groups of data are formed based on the L*N first target data, where each group of data includes N first target data, and the N first target data are combined with N different receiving antennas. One correspondence.

In specific implementation, as shown in Figure 7, the radar equipment can form the first set of data from the first target data in the first RD-Map that corresponds one-to-one to receiving antenna 1, receiving antenna 2, and receiving antenna N. The first target data in the second RD-Map, which corresponds one-to-one to receiving antenna 1, receiving antenna 2, and up to receiving antenna N, constitutes the second set of data. By analogy, the first target data in the second RD-Map corresponds to receiving antenna 1, receiving antenna 2, and up to receiving antenna N. The first target data in the L-th RD-Map with N one-to-one correspondence constitutes the L-th group of data.

S306: Use the preset super-resolution algorithm to process the L group of data.

In this embodiment, after L sets of data are obtained, the L sets of data can be input into the super-resolution algorithm to detect possible angles of the target object at the first target distance and the first target speed.

It can be understood that in the existing technology, after the radar equipment obtains the N target data corresponding to the first target distance and the first target speed corresponding to the N receiving antennas, the N target data will first be formed into a group. data; then obtain multiple sets of data based on this set of data, where each set of data in the obtained multiple sets of data only includes M target data out of N target data, M is less than N; finally, use the preset Super-resolution algorithms process multiple sets of data. It can be seen that in the existing technology, when the radar equipment uses the preset super-resolution algorithm to process multiple sets of data, each set of data does not fully use the data collected by the N receiving antennas of the radar equipment. It should be understood that not fully using the data collected by the N receiving antennas of the radar equipment is essentially equivalent to reducing the antenna diameter of the radar equipment, which will lead to a reduction in the angular resolution of the radar equipment, further reducing the detected first target distance. and the accuracy of the target point on the first target speed is lower. In this embodiment, since each set of data input to the preset super-resolution algorithm includes N target data, and since the N target data correspond to N different receiving antennas, it is equivalent to maintaining the original The antenna diameter of some radar equipment will not reduce the angular resolution of the radar equipment, that is, it will improve the accuracy of detected target distance and target speed.

Figure 8 is a schematic structural diagram of a data processing device provided by an embodiment of the present application. Specifically, as shown in Figure 8, the data processing device includes: an acquisition module 801 and a processing module 802.

Among them, the acquisition module 801 is used to acquire the first fast time dimension-slow time dimension data collected by each of the N receiving antennas of the radar device, and the fast time in each first fast time dimension-slow time dimension data. The length of the dimension is M1; the processing module 802 is used to divide the first fast time dimension-slow time dimension data collected by each receiving antenna into L second fast time dimension-slow time dimension data, each second The length of the fast time dimension in the fast time dimension-slow time dimension data is M2, and the length of the slow time dimension in each second fast time dimension-slow time dimension data is the same as the first time dimension collected by each receiving antenna. The lengths of the slow time dimensions in the fast time dimension-slow time dimension data are the same, and M2 is smaller than M1; the processing module 802 is also used to calculate the L second fast time dimension-slow time dimension corresponding to each receiving antenna. Each second fast time dimension-slow time dimension data in the data undergoes a two-dimensional discrete Fourier transform to obtain L first range Doppler map data corresponding to each receiving antenna; the acquisition module 801 , and is also used to obtain the first target distance and the first target speed in each of the L first range Doppler map data corresponding to each receiving antenna. Target data; the processing module 802 is also configured to determine L groups of data based on L*N first target data corresponding to the N receiving antennas, where each group of data in the L groups of data includes N first target data, The N first target data correspond to N different receiving antennas one-to-one; the processing module 802 is also configured to process the L groups of data using a preset super-resolution algorithm.

In a possible implementation, the processing module 802 is further configured to: perform a two-dimensional discrete Fourier transform on the first fast time dimension-slow time dimension data collected by each receiving antenna, and obtain the first fast time dimension-slow time dimension data collected by each receiving antenna. The second range Doppler map data corresponding to the N receiving antennas; the processing module 802 is also used to perform non-coherent accumulation NCI on the N second range Doppler map data corresponding to the N receiving antennas. , obtain a third range Doppler map; the acquisition module 801 is also used to: obtain second target data on the third range Doppler map, where the second target data is data that satisfies preset conditions; The processing module 802 is further configured to determine the distance information and speed information of the second target data on the third range Doppler map as the first target distance and the first target speed respectively.

In a possible implementation, the processing module 802 is further configured to perform non-coherent accumulation NCI on the L*N first range Doppler map data corresponding to the N receiving antennas to obtain a fourth range. Doppler map; the acquisition module 801 is also used to: acquire third target data on the fourth range Doppler map, where the third target data is data that meets preset conditions; the processing module 802 It is also used to: determine the distance information and speed information of the third target data on the fourth range Doppler map as the first target distance and the first target speed respectively.

In a possible implementation, the acquisition module 801 is also used to: acquire L second fast time dimension-slow time dimension data corresponding to any one of the N receiving antennas; the processing module 802 is also used to perform non-coherent accumulation NCI on the L second fast time dimension-slow time dimension data corresponding to any one of the receiving antennas to obtain the fifth range Doppler map; the acquisition module 801 is also used to: Obtain the fourth target data on the fifth range Doppler map, and the fourth target data is data that meets preset conditions; the processing module 802 is also used to: convert the fourth target data in the The distance information and speed information on the fifth range Doppler map are respectively determined as the first target distance and the first target speed.

In a possible implementation, the data satisfying the preset conditions is peak data in range Doppler map data.

Figure 9 is a schematic structural diagram of a data processing device provided by another embodiment of the present application. The device shown in Figure 9 can be used to perform the method described in any of the aforementioned embodiments.

As shown in Figure 9, the device 900 in this embodiment includes: a memory 901, a processor 902, a communication interface 903 and a bus 904. Among them, the memory 901, the processor 902, and the communication interface 903 realize communication connections between each other through the bus 904.

The memory 901 may be a read only memory (ROM), a static storage device, a dynamic storage device or a random access memory (RAM). The memory 901 can store programs. When the program stored in the memory 901 is executed by the processor 902, the processor 902 is used to execute various steps of the method shown in Figure 3.

The processor 902 may be a general central processing unit (CPU), a microprocessor, an application specific integrated circuit (ASIC), or one or more integrated circuits for executing related programs to Implement the method shown in Figure 3 of this application.

The processor 902 may also be an integrated circuit chip with signal processing capabilities. During the implementation process, each step of the method in FIG. 3 according to the embodiment of the present application can be completed by instructions in the form of hardware integrated logic circuits or software in the processor 902 .

The above-mentioned processor 902 can also be a general-purpose processor, a digital signal processor (digital signal processing, DSP), an application specific integrated circuit (ASIC), an off-the-shelf programmable gate array (field programmable gate array, FPGA) or other programmable logic devices, Discrete gate or transistor logic devices, discrete hardware components. Each method, step and logical block diagram disclosed in the embodiment of this application can be implemented or executed. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

The steps of the method disclosed in conjunction with the embodiments of the present application can be directly implemented by a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software module can be located in random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, registers and other mature storage media in this field. The storage medium is located in the memory 901. The processor 902 reads the information in the memory 901 and completes the functions required to be performed by the units included in the device of the present application in combination with its hardware. For example, it can perform various steps/functions of the embodiment shown in Figure 3.

The communication interface 903 may use, but is not limited to, a transceiver device such as a transceiver to implement communication between the device 900 and other devices or communication networks.

Bus 904 may include a path that carries information between various components of device 900 (eg, memory 901, processor 902, communication interface 903).

It should be understood that the device 900 shown in the embodiment of the present application may be an electronic device, or may also be a chip configured in the electronic device.

The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on the computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, e.g., the computer instructions may be transferred from a website, computer, server, or data center Transmit to another website, computer, server or data center through wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access, or a data storage device such as a server or a data center that contains one or more sets of available media. The usable media may be magnetic media (eg, floppy disk, hard disk, tape), optical media (eg, DVD), or semiconductor media. The semiconductor medium may be a solid state drive.

It should be understood that the term "and/or" in this article is only an association relationship describing related objects, indicating that there can be three relationships, for example, A and/or B, which can mean: A alone exists, and A and B exist simultaneously. , there are three cases of B alone, where A and B can be singular or plural. In addition, the character "/" in this article generally indicates that the related objects are an "or" relationship, but it may also indicate an "and/or" relationship. For details, please refer to the previous and later contexts for understanding.

In this application, "at least one" refers to one or more, and "plurality" refers to two or more. "At least one of the following" or similar expressions thereof refers to any combination of these items, including any combination of a single item (items) or a plurality of items (items). For example, at least one of a, b, or c can mean: a, b, c, a-b, a-c, b-c, or a-b-c, where a, b, c can be single or multiple .

It should be understood that in the various embodiments of the present application, the size of the sequence numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its functions and internal logic, and should not be used in the embodiments of the present application. The implementation process constitutes any limitation.

Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented with electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may implement the described functionality using different methods for each specific application, but such implementations should not be considered beyond the scope of this application.

Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, devices and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be described again here.

In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or can be integrated into another system, or some features can be ignored, or not implemented. On the other hand, the coupling or direct coupling or communication connection between each other shown or discussed may be through some interfaces, and the indirect coupling or communication connection of the devices or units may be in electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or they may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

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

If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the existing technology or the part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including Several instructions are used to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory, random access memory, magnetic disk or optical disk and other various media that can store program codes.

The above are only specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application. should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

A data processing method, characterized by including:

Obtain the first fast time dimension-slow time dimension data collected by each of the N receiving antennas of the radar equipment, and the length of the fast time dimension in each first fast time dimension-slow time dimension data is M1;

The first fast time dimension-slow time dimension data collected by each receiving antenna is divided into L second fast time dimension-slow time dimension data, and the fast time in each second fast time dimension-slow time dimension data is The length of the dimension is M2, and the length of the slow time dimension in each second fast time dimension-slow time dimension data is the same as the slow time in the first fast time dimension-slow time dimension data collected by each receiving antenna. The lengths of the dimensions are the same, M2 is smaller than M1;

Perform a two-dimensional discrete Fourier transform on each of the L second fast time dimension-slow time dimension data corresponding to each receiving antenna to obtain the corresponding L first range Doppler map data corresponding to the receiving antenna;

Obtain the first target distance and the first target data on the first target speed in each of the L first range Doppler map data corresponding to each receiving antenna;

L groups of data are determined based on L*N first target data corresponding to the N receiving antennas. Each group of data in the L groups of data includes N first target data, and the N first target data are related to N Different receiving antennas correspond one to one;

The L group of data are processed using a preset super-resolution algorithm.
The method of claim 1, further comprising:

Perform a two-dimensional discrete Fourier transform on the first fast time dimension-slow time dimension data collected by each receiving antenna to obtain the second range Doppler map data corresponding to each receiving antenna;

Perform non-coherent accumulation NCI on the N second range Doppler map data corresponding to the N receiving antennas to obtain a third range Doppler map;

Obtain second target data on the third range Doppler map, where the second target data is data that meets preset conditions;

The distance information and speed information of the second target data on the third range Doppler map are determined as the first target distance and the first target speed respectively.
The method of claim 1, further comprising:

Perform non-coherent accumulation NCI on the L*N first range Doppler map data corresponding to the N receiving antennas to obtain a fourth range Doppler map;

Obtain third target data on the fourth range Doppler map, where the third target data is data that meets preset conditions;

The distance information and speed information of the third target data on the fourth range Doppler map are determined as the first target distance and the first target speed respectively.
The method of claim 1, further comprising:

Obtain L second fast time dimension-slow time dimension data corresponding to any one of the N receiving antennas;

Perform non-coherent accumulation NCI on the L second fast time dimension-slow time dimension data corresponding to any one of the receiving antennas to obtain the fifth range Doppler map;

Obtain fourth target data on the fifth range Doppler map, where the fourth target data is data that meets preset conditions;

The distance information and speed information of the fourth target data on the fifth range Doppler map are determined as the first target distance and the first target speed respectively.
The method according to any one of claims 2 to 4, characterized in that the data satisfying preset conditions is peak data in range Doppler map data.
A data processing device, characterized in that it includes:

The acquisition module is used to obtain the first fast time dimension-slow time dimension data collected by each of the N receiving antennas of the radar device, and the length of the fast time dimension in each first fast time dimension-slow time dimension data. for M1;

A processing module configured to divide the first fast time dimension-slow time dimension data collected by each receiving antenna into L second fast time dimension-slow time dimension data, each second fast time dimension-slow time dimension The length of the fast time dimension in the data is M2, and the length of each second fast time dimension-slow time dimension in the data is the same as the first fast time dimension-slow time dimension collected by each receiving antenna. The slow time dimensions in the data have the same length, M2 is smaller than M1;

The processing module is also used to perform two-dimensional discrete Fourier transform on each of the L second fast time dimension-slow time dimension data corresponding to each receiving antenna. Transform to obtain L first range Doppler map data corresponding to each receiving antenna;

The acquisition module is also used to acquire the first target distance and the first target speed in each of the L first range Doppler map data corresponding to each receiving antenna. the first target data on;

A processing module, further configured to determine L groups of data based on L*N first target data corresponding to the N receiving antennas, each group of data in the L groups of data including N first target data, and the N The first target data corresponds to N different receiving antennas one-to-one;

The processing module is also configured to process the L groups of data using a preset super-resolution algorithm.
The device according to claim 6, characterized in that the processing module is further configured to: perform a two-dimensional discrete Fourier transform on the first fast time dimension-slow time dimension data collected by each receiving antenna, to obtain The second range Doppler map data corresponding to each receiving antenna;

The processing module is also configured to: non-coherently accumulate NCI the N second range Doppler map data corresponding to the N receiving antennas to obtain a third range Doppler map;

The acquisition module is also configured to: acquire second target data on the third range Doppler map, where the second target data is data that meets preset conditions;

The processing module is further configured to determine the distance information and speed information of the second target data on the third range Doppler map as the first target distance and the first target speed respectively.
The device according to claim 6, wherein the processing module is further configured to perform non-coherent accumulation NCI on L*N first range Doppler map data corresponding to the N receiving antennas, to obtain Fourth range Doppler map;

The acquisition module is also configured to: acquire third target data on the fourth range Doppler map, where the third target data is data that meets preset conditions;

The processing module is further configured to determine the distance information and speed information of the third target data on the fourth range Doppler map as the first target distance and the first target speed respectively.
The device according to claim 6, wherein the acquisition module is further configured to: acquire L second fast time dimension-slow time dimension data corresponding to any one of the N receiving antennas;

The processing module is also used to perform non-coherent accumulation NCI on the L second fast time dimension-slow time dimension data corresponding to any one of the receiving antennas to obtain a fifth range Doppler map;

The acquisition module is also configured to: acquire fourth target data on the fifth range Doppler map, where the fourth target data is data that meets preset conditions;

The processing module is further configured to determine the distance information and speed information of the fourth target data on the fifth range Doppler map as the first target distance and the first target speed respectively.
The device according to any one of claims 7 to 9, characterized in that the data satisfying the preset conditions is peak data in range Doppler map data.
An automatic driving equipment, characterized by comprising the device according to any one of claims 6 to 10.
A data processing device, characterized in that it includes: a memory and a processor;

The memory is used to store program instructions;

The processor is configured to call program instructions in the memory to execute the method according to any one of claims 1 to 5.
A computer-readable medium, characterized in that the computer-readable medium stores program code for computer execution, and the program code includes instructions for performing the method according to any one of claims 1 to 5.
A computer program product, the computer program product includes computer program code, characterized in that when the computer program code is run on a computer, the computer implements the method described in any one of claims 1 to 5 Methods.