Data processing method and processing device

Abstract

The application provides a data processing method and device. In the technical scheme, the radar device divides first fast time dimension-slow time dimension data obtained by each receiving antenna in N receiving antennas into L second fast time dimension-slow time dimension data along the fast time dimension; then performs 2DFFT on each second fast time dimension-slow time dimension data to obtain L*N first distance Doppler diagram data, extracts target data on a first target distance and a first target speed from each first distance Doppler diagram data to obtain L*N target data; finally, L groups of data are determined from the L*N target data, wherein each group of data includes N target data, and the N target data correspond to N different receiving antennas. The method provided by the application can improve the accuracy of the detected target objects on the first target distance and the first target speed without reducing the angle resolution of the radar device.

Description

Technical Field

[0001] This application relates to the field of radar technology, and in particular to a data processing method and processing device. Background Technology

[0002] Vehicle-mounted radar equipment is primarily used to detect road conditions within the vehicle's current driving area, playing a crucial role in assisting vehicles to avoid obstacles and perceive their surroundings. Target detection within vehicle-mounted radar equipment mainly involves acquiring information such as the distance between the target point and the vehicle, the target point's speed, and the angle between the target point and the vehicle.

[0003] One method for target detection using vehicle-mounted radar equipment is as follows: Acquire fast-time dimension and slow-time dimension data from each of the N receiving antennas of the radar. Perform a two-dimensional discrete fast Fourier transform (2DFFT) on each fast-time dimension and slow-time dimension data to obtain N range-doppler maps (RD-Maps). Each range-doppler map includes velocity and range information. Then, based on the N RD-Maps, use non-coherent accumulation (NCI) and constant false alarm rate (CFAR) to perform target detection. The super-resolution algorithm (CFAR) determines the target distance and target velocity (only objects at this target distance and target velocity are considered targets to be detected, and there may be multiple target points corresponding to this target distance and target velocity). It then extracts the target distance and target velocity data from each RD-Map data set to form a set of data containing N data points. Based on this set of data, multiple different sets of data are obtained, where each set of data contains M data points out of the N data points, where M is less than N. Finally, the multiple sets of data are input into the super-resolution algorithm to detect the angles of possible target points at the target distance and target velocity.

[0004] However, the above process results in low accuracy of the detected target points in terms of target distance and target velocity.

[0005] Therefore, improving the accuracy of detecting target objects in terms of target distance and target speed has become an urgent technical problem to be solved. Summary of the Invention

[0006] This application provides a data processing method and processing apparatus that can improve the accuracy of detecting targets in terms of first target distance and first target velocity.

[0007] In a first aspect, this application provides a data processing method, comprising: acquiring first fast time dimension-slow time dimension data collected by each of N receiving antennas of a radar device, wherein the length of the fast time dimension in each first fast time dimension-slow time dimension data is M1; 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, wherein the length of the fast time dimension in each second fast time dimension-slow time dimension data is M2, wherein 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-fast time dimension in the first fast time dimension-slow time dimension data collected by each receiving antenna, and M2 is less than M1; processing the data of each receiving antenna... A two-dimensional discrete Fourier transform is performed on each of the L second fast-time-slow-time dimension data corresponding to the antenna to obtain L first range Doppler map data corresponding to each receiving antenna; the first target data in terms of first target distance and first target velocity is obtained from each of the L first range Doppler map data corresponding to each receiving antenna; L sets of data are determined based on the L*N first target data corresponding to the N receiving antennas, each set of data including N first target data, and the N first target data correspond one-to-one with N different receiving antennas; the L sets of data are processed using a preset super-resolution algorithm.

[0008] In this embodiment, the radar device 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. Thus, each receiving antenna will have L second fast time dimension-slow time dimension data, and for the N receiving antennas, there will be a total of L*N second fast time dimension-slow time dimension data. Then, a two-dimensional discrete fast Fourier transform is performed on each of the L*N second fast time dimension-slow time dimension data to obtain L*N first range Doppler map data. Target data for the first target range and the first target velocity are extracted from each first range Doppler map data, resulting in L*N target data. Finally, the L*N target data are formed into L groups of data, where each group of data includes N target data, and these N target data correspond to N different receiving antennas.

[0009] Understandably, in existing technologies, after acquiring N target data points corresponding to the first target distance and velocity across N receiving antennas, a radar device first groups these N data points into a single set. Then, based on this single set, it obtains multiple sets of data, each containing only M target data points from the N data points, where M is less than N. Finally, a preset super-resolution algorithm is used to process these multiple sets of data. It can be seen that in existing technologies, when the radar device processes multiple sets of data using the preset super-resolution algorithm, each set does not fully utilize the data collected by the radar device's N receiving antennas. It should be understood that not fully utilizing the data collected by the radar device's N receiving antennas essentially reduces the radar device's antenna aperture, leading to a decrease in the radar device's angular resolution and consequently, lower accuracy in detecting target points at the first target distance and velocity. In this embodiment, since each set of data input into the preset super-resolution algorithm includes N target data, and since these N target data correspond to N different receiving antennas, it is equivalent to maintaining the original antenna aperture of the radar equipment. Therefore, the angular resolution of the radar equipment will not be reduced, which improves the accuracy of detecting target objects in terms of target distance and target speed.

[0010] In conjunction with the first aspect, in one 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 second range Doppler map data corresponding to each receiving antenna; performing noncoherent accumulation (NCI) on the N second range Doppler map data corresponding one-to-one with the N receiving antennas to obtain a third range Doppler map; acquiring second target data on the third range Doppler map, wherein the second target data is data that meets preset conditions; and determining the distance information and velocity information of the second target data on the third range Doppler map as the first target distance and the first target velocity, respectively.

[0011] In conjunction with the first aspect, in one possible implementation, the method further includes: performing noncoherent accumulation (NCI) on L*N first range Doppler image data corresponding to the N receiving antennas to obtain a fourth range Doppler image; acquiring third target data on the fourth range Doppler image, wherein the third target data is data that meets preset conditions; and determining the distance information and velocity information of the third target data on the fourth range Doppler image as the first target distance and the first target velocity, respectively.

[0012] In conjunction with the first aspect, in one possible implementation, the method further includes: acquiring L second fast-time-slow-time dimension data corresponding to any one of the N receiving antennas; performing noncoherent accumulation (NCI) on the L second fast-time-slow-time dimension data corresponding to the arbitrary receiving antenna to obtain a fifth range Doppler map; acquiring fourth target data on the fifth range Doppler map, wherein the fourth target data is data that meets preset conditions; and determining the distance information and velocity information of the fourth target data on the fifth range Doppler map as the first target distance and the first target velocity, respectively.

[0013] In conjunction with the first aspect, in one possible implementation, the preset conditions include: the data that satisfies the preset conditions is the peak data in the distance Doppler graph data.

[0014] Secondly, this application provides a data processing apparatus, comprising: an acquisition module, configured to acquire first fast-time dimension-slow-time dimension data collected by each of N receiving antennas of a radar device, wherein the length of the fast time dimension in each first fast-time dimension-slow-time dimension data is 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, wherein 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 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, wherein M2 is less than M1; the processing module is further configured to process the data collected by each receiving antenna... The acquisition module is further configured to acquire first target data in terms of first target distance and first target velocity from each of the L first range Doppler data corresponding to each of the L first range Doppler data corresponding to each receiving antenna; the processing module is further configured to determine L sets of data based on the L*N first target data corresponding to the N receiving antennas, each set of data including N first target data, and the N first target data corresponding to N different receiving antennas; the processing module is further configured to process the L sets of data using a preset super-resolution algorithm.

[0015] In conjunction with the second aspect, in one 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 to obtain the second range Doppler map data corresponding to each receiving antenna; the processing module is further configured to: perform noncoherent accumulation (NCI) on the N second range Doppler map data corresponding one-to-one with the N receiving antennas to obtain a third range Doppler map; the acquisition module is further configured to: acquire the second target data on the third range Doppler map, wherein the second target data is data that meets preset conditions; the processing module is further configured to: determine the distance information and velocity information of the second target data on the third range Doppler map as the first target distance and the first target velocity, respectively.

[0016] In conjunction with the second aspect, in one possible implementation, the processing module is further configured to: perform noncoherent accumulation (NCI) on the L*N first range Doppler image data corresponding to the N receiving antennas to obtain a fourth range Doppler image; the acquisition module is further configured to: acquire third target data on the fourth range Doppler image, wherein the third target data is data that meets preset conditions; the processing module is further configured to: determine the distance information and velocity information of the third target data on the fourth range Doppler image as the first target distance and the first target velocity, respectively.

[0017] In conjunction with the second aspect, in one possible implementation, the acquisition module is further configured to: acquire L second fast-time-slow-time dimension data corresponding to any one of the N receiving antennas; the processing module is further configured to: perform noncoherent accumulation (NCI) on the L second fast-time-slow-time dimension data corresponding to the arbitrary receiving antenna to obtain a fifth range Doppler image; the acquisition module is further configured to: acquire fourth target data on the fifth range Doppler image, wherein the fourth target data is data that meets preset conditions; the processing module is further configured to: determine the distance information and velocity information of the fourth target data on the fifth range Doppler image as the first target distance and the first target velocity, respectively.

[0018] In conjunction with the second aspect, in one possible implementation, the data that satisfies the preset conditions is the peak data in the distance Doppler graph data.

[0019] Thirdly, this application provides an autonomous driving device comprising the means as described in the second aspect or any of its possible implementations.

[0020] For example, the autonomous driving device is a vehicle.

[0021] Fourthly, this application provides a data processing apparatus, comprising: a memory and a processor; the memory being used to store program instructions; the processor being used to invoke the program instructions in the memory to execute the method as described in the first aspect or any of the possible implementations thereof.

[0022] Fifthly, this application provides a computer-readable medium storing program code for computer execution, the program code including instructions for performing the methods described in the first aspect or any of the possible implementations thereof.

[0023] Sixthly, this application provides a computer program product including computer program code, which, when run on a computer, causes the computer to implement the method described in the first aspect or any of its possible implementations. Attached Figure Description

[0024] Figure 1 This is a schematic diagram illustrating an application scenario provided in the embodiments of this application;

[0025] Figure 2 A structural schematic diagram of a radar device for target detection provided in an embodiment of this application;

[0026] Figure 3 A flowchart illustrating the data processing method provided in the embodiments of this application;

[0027] Figure 4 A structural diagram illustrating how a first fast time dimension-slow time dimension data is divided into L second fast time dimension-slow time dimension data, as provided in an embodiment of this application.

[0028] Figure 5 A schematic diagram illustrating the process of obtaining L first distance Doppler image data provided in this application embodiment;

[0029] Figure 6 A structural schematic diagram illustrating the extraction of data on the distance and velocity of the first target provided in an embodiment of this application;

[0030] Figure 7 A structural diagram illustrating the determination of L sets of data based on L*N first target data, provided in an embodiment of this application;

[0031] Figure 8 A structural schematic diagram of a data processing apparatus provided in one embodiment of this application;

[0032] Figure 9 This is a structural schematic diagram of a data processing apparatus provided for another embodiment of this application. Detailed Implementation

[0033] With the rapid development of science and technology, intelligent driving technology has received increasing attention, leading to the emergence of various intelligent driving devices, such as autonomous vehicles and drones.

[0034] A crucial function of any intelligent driving device is the ability to perceive its external environment to prevent collisions with other objects or people. Target recognition and detection using radar has become an important means for intelligent driving devices to obtain their position and environmental information relative to their surroundings, and it is now widely used in the field of intelligent driving.

[0035] Figure 1 This is a schematic diagram illustrating an application scenario provided in an embodiment of this application. For example... Figure 1 As shown, radar device 101 transmits a detection signal to a target area via its transmitting antenna. If the target area includes a target object 102, the target object 102 will reflect an echo signal. Correspondingly, the receiving antenna in radar device 101 receives the echo signal reflected by the target object 102. Specifically, if radar device 101 includes N receiving antennas, each receiving antenna receives the echo signal reflected by the target object 102. Then, radar device 101 performs target detection based on the N echo signals received by the N receiving antennas. When performing target detection based on the echo signals, radar device 101 mainly determines the distance between the target object 102 and radar device 101, the speed of the target object 102, and the angle information between the target object 102 and the vehicle-mounted radar device 101 based on the echo signals.

[0036] Optionally, the radar device 101 can be applied to scenarios such as unmanned driving, autonomous driving, intelligent driving, or connected driving where target detection is performed through detection signals.

[0037] Optionally, the target 102 may be an obstacle or pedestrian, etc., located within the measurement range of the radar device 101.

[0038] For example, the radar device 101 described in this application may be a millimeter-wave radar, lidar, ultrasonic radar, etc., which does not constitute a limitation of this application.

[0039] Optionally, the radar device 101 described in this application can also be applied to a terminal. For example, the terminal can be a transportation vehicle or a smart device. The terminal can be a motor vehicle (such as an unmanned vehicle, a smart vehicle, an electric vehicle, a digital car, etc.), a drone, a rail vehicle, a bicycle, a traffic light, etc. The terminal can be a mobile phone, a tablet computer, a laptop computer, a personal digital assistant, a point-of-sale terminal, an in-vehicle computer, an augmented reality device, a virtual reality device, a wearable device, an in-vehicle terminal, etc.

[0040] Specifically, Figure 2 A schematic diagram of the target detection process in the prior art provided in this application.

[0041] like Figure 2 As shown, the target detection process of radar device 101 is as follows: For a radar device including receiving antenna 1, receiving antenna 2, ..., receiving antenna N, the radar device first acquires the fast time dimension-slow time dimension data collected by each of the N receiving antennas, and performs a two-dimensional discrete fast fourier transform (2DFFT) on each fast time dimension-slow time dimension data to obtain N range-doppler map (RD-Map) data, where each range-doppler map data includes velocity information and range information; then, based on the N RD-Map data, the radar device uses non-coherent accumulation (NCI) and constant false alarm rate (CFAR) to determine the target range and target velocity (only objects at this target range and target velocity are considered targets to be detected), and extracts the target range and target velocity data from each RD-Map data. Figure 2 The data (on the black cells in the image) is used to form a set of data containing N data points. Since there may be multiple targets with the same target distance and velocity in the corresponding RD-Map data, the super-resolution algorithm will continue to process the data. Because the super-resolution algorithm must be based on multiple sets of data, after obtaining a set of data containing N data points, multiple different sets of data will be obtained based on this set. Each set of data will contain M data points out of the N data points, where M is less than N. Finally, the multiple sets of data will be input into the super-resolution algorithm to detect the angles of possible targets with the target distance and velocity.

[0042] However, the above process results in low accuracy in detecting targets at the same distance and speed. Angular resolution refers to the ability to distinguish targets at the same distance and speed.

[0043] Analysis revealed the following reasons for the low accuracy in detecting targets at the same distance and speed: The angular resolution of radar equipment is related to the antenna aperture. Generally, the larger the radar antenna aperture, the higher the angular resolution. However, in the current target detection process, when obtaining multiple sets of data based on an original set of N data points, the obtained sets only include the original M data points. That is, each set of data input into the super-resolution algorithm only uses data from the radar's M receiving antennas, not the data collected by all N receiving antennas. It should be understood that not fully utilizing the data collected by the radar's N receiving antennas is essentially equivalent to reducing the radar's antenna aperture. A reduced antenna aperture decreases the radar's ability to distinguish targets at the same distance and speed, i.e., reduces angular resolution. This further reduces the accuracy of the output for targets at the same distance and speed. For example, a radar that could normally distinguish two targets at the same distance and speed (outputting two angle values) might only output one angle value due to the reduced angular resolution, resulting in inaccurate target detection.

[0044] In view of this, this application provides a data processing method and processing apparatus that can improve the accuracy of detecting targets in terms of first target distance and first target velocity.

[0045] Figure 3 This is a flowchart illustrating a data processing method provided in one embodiment of this application. Figure 3 As shown, the method in this embodiment includes steps S301, S302, S303, S304, S305, and S306. This data processing method can be... Figure 1 The radar device 101 shown is in operation.

[0046] S301, acquire the first fast time dimension-slow time dimension data collected by each of the N receiving antennas of the radar device, where the length of the fast time dimension in each first fast time dimension-slow time dimension data is M1.

[0047] It should be understood that when radar equipment transmits signals (also known as transmitting linear frequency modulated signals or chirp signals) using a transmitting antenna, if the transmitted signal acts on a target object, the target object will reflect the signal back to the radar equipment. In this embodiment, the transmitted signal is also referred to as the original transmitted signal, and the signal reflected back to the radar after passing through the target object is also referred to as the echo signal.

[0048] Specifically, if the radar equipment includes N receiving antennas, each receiving antenna will receive the echo signal. Furthermore, after receiving the echo signal, each receiving antenna will perform a conjugate multiplication of the echo signal and the original transmitted signal, i.e., deskewing, to convert the high-frequency signal into a low-frequency signal. Then, after passing through a low-pass filter, the original fast-time dimension-slow-time dimension data corresponding to each receiving antenna can be obtained.

[0049] In this embodiment, the original fast-time dimension-slow-time dimension data collected by each receiving antenna is also referred to as the first fast-time dimension-slow-time dimension data, and the length of the fast-time dimension for each first fast-time dimension-slow-time dimension data is M1. For a detailed explanation of the concept and characteristics of fast-time dimension-slow-time dimension data, please refer to the descriptions in related technologies; they will not be repeated here.

[0050] It is understandable 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, as is the length of the slow time dimension in any two first fast time dimension - slow time dimension data.

[0051] S302, 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 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 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 less than M1.

[0052] In this embodiment, after the radar device acquires the first fast time dimension-slow time dimension data collected by each receiving antenna, it divides the first fast time dimension-slow time dimension data collected by each receiving antenna along the fast time dimension, so that each receiving antenna can correspond to L fast time dimension-slow time dimension data.

[0053] 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 length of the fast time dimension in each divided fast time dimension-slow time dimension data is M2, then M2 should be less than the length of the fast time dimension M1 in the first fast time dimension-slow time dimension data.

[0054] In this embodiment, each of the divided fast time dimension-slow time dimension data is referred to as the second fast time dimension-slow time dimension data.

[0055] 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 length of the slow time dimension in any two of the L second fast time dimension-slow time dimension data is the same, 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.

[0056] As an example, Figure 4 This is a structural diagram illustrating how data in a first fast time dimension-slow time dimension is divided into L second fast time dimension-slow time dimension data, as provided in an embodiment of this application. This embodiment uses L equal to 3 as an example. Figure 4 As shown, 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, and the length of the slow time dimension is 4, which can be considered a 9x4 matrix, then when dividing the data into three second fast-time dimension-slow-time dimensions along the fast time dimension, the first three rows of the first fast-time dimension-slow-time dimension data can be divided into the first second fast-time dimension-slow-time dimension data, the middle three rows into the second second fast-time dimension-slow-time dimension data, and the last three rows 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 three second fast-time dimension-slow-time dimension data.

[0057] It should be noted that the above Figure 4 The numbers in this document are merely examples and do not constitute a limitation of this application.

[0058] For example, suppose 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 a 500-row, K-column matrix. In specific partitioning, the matrix containing rows 1 to 400 and K columns can be divided into the first second fast-time dimension-slow-time dimension data; the matrix containing rows 51 to 450 and K columns can be divided into the second second fast-time dimension-slow-time dimension data; and the matrix containing rows 101 to 500 and K columns can be divided into the third second fast-time dimension-slow-time dimension data. It can be seen that after partitioning the first fast-time dimension-slow-time dimension data collected by each receiving antenna, each receiving antenna will correspond to three second fast-time dimension-slow-time dimension data.

[0059] For example, suppose 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 a 500-row, K-column matrix. In specific partitioning, the matrix containing rows 1 to 450 (K columns) can be divided into the first second fast-time dimension-slow-time dimension data, and the matrix containing rows 51 to 500 (K columns) can be divided into the second second fast-time dimension-slow-time dimension data. It can be seen that after partitioning the first fast-time dimension-slow-time dimension data collected by each receiving antenna, each receiving antenna will correspond to two second fast-time dimension-slow-time dimension data.

[0060] It is 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 multiplied by 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, as is the length of the slow time dimension in any two second fast-time dimension-slow-time dimension data. That is, it can also be considered that the size of any two second fast-time dimension-slow-time dimension data in L multiplied by N second fast-time dimension-slow-time dimension data is the same.

[0061] 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 to obtain L first distance Doppler map data corresponding to each receiving antenna.

[0062] Typically, in order to achieve target detection, radar equipment performs a 2DFFT transformation on the fast-time dimension-slow-time dimension data collected by each receiving antenna after acquiring the data. This results in a range-Doppler map (RD-Map) corresponding to the fast-time dimension-slow-time dimension data collected by each receiving antenna. The range-Doppler map data from this RD-Map can be considered to include the range and velocity information of multiple candidate objects.

[0063] 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, a 2DFFT transformation is performed on each of the L second fast-time dimension-slow-time dimension data to obtain the RD-Map data corresponding to each second fast-time dimension-slow-time dimension data. In this embodiment, the RD-Map data obtained after performing a 2DFFT transformation on the second fast-time dimension-slow-time dimension data is also called the first range Doppler map data.

[0064] For example, Figure 5This is a schematic diagram illustrating a process for obtaining L first distance Doppler image data points, provided as an embodiment of this application. In this example, L is taken as equal to 3.

[0065] like Figure 5 As shown, each dashed box in the fast time dimension-slow time dimension data represents a second fast time dimension-slow time dimension data. After dividing the fast time dimension-slow time dimension data into three second fast time dimension-slow time dimension data, a 2DFFT transformation is performed on each of the three second fast time dimension-slow time dimension data to obtain three first range Doppler image data. Each first range Doppler image data can reflect the distance information between each candidate and the radar device and the velocity information of each candidate among multiple candidates.

[0066] It is understood that in this embodiment, after performing a two-dimensional discrete Fourier transform on each of the L second fast time dimension-slow time dimension data corresponding to each receiving antenna, there will be L multiplied by N (L*N) first distance Doppler map data for N receiving antennas.

[0067] S304, acquire the first target data in terms of first target distance and first target velocity in each of the L first range Doppler data corresponding to each receiving antenna.

[0068] In this embodiment, the distance information and velocity information corresponding to the unit that the radar device determines may contain a target are also referred to as the first target distance and the first target velocity, respectively.

[0069] In this embodiment, there are no restrictions on how the radar equipment determines the distance and speed of the first target.

[0070] For example, in a first possible implementation, determining the first target distance and the first target velocity 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 second range Doppler map data corresponding to each receiving antenna; performing N-dimensional discrete Fourier transform (NCI) on the N second range Doppler map data corresponding one-to-one with the N receiving antennas to obtain a third range Doppler map; acquiring the second target data on the third range Doppler map, wherein the second target data is data that meets preset conditions; and determining the distance information and velocity information of the second target data on the third range Doppler map as the first target distance and the first target velocity, respectively.

[0071] In this embodiment, N corresponding second range Doppler image data are obtained by performing 2DFFT transformation on the original first fast time dimension-slow time dimension data collected by N receiving antennas. Then, the distance information and velocity information corresponding to the data that meet the preset conditions on the range Doppler image (third range Doppler image) obtained after N second range Doppler image data are processed by NCI, respectively, are determined as the first target distance and the first target velocity.

[0072] For example, in the second possible implementation, determining the first target distance and the first target velocity includes: acquiring L second fast-time dimension-slow-time dimension data corresponding to any one of the N receiving antennas; performing non-coherent accumulation (NCI) on the L second fast-time dimension-slow-time dimension data corresponding to any one receiving antenna to obtain a fifth range Doppler map; acquiring fourth target data on the fifth range Doppler map, wherein the fourth target data is data that meets preset conditions; and determining the distance information and velocity information of the fourth target data on the fifth range Doppler map as the first target distance and the first target velocity, respectively.

[0073] In this embodiment, the distance and velocity information corresponding to the data satisfying preset conditions on the obtained range Doppler map (fifth range Doppler map) are determined as the first target distance and the first target velocity, respectively, by performing NCI processing on the L second fast-time-slow-time-dimensional data corresponding to the first fast-time-dimensional data collected by any one receiving antenna. It can be understood that in this implementation, during NCI processing, the L second fast-time-slow-time-dimensional data corresponding to one receiving antenna are used as representatives to determine the first target distance and the first target velocity.

[0074] For example, in the third possible implementation, determining the first target distance and the first target velocity includes: performing noncoherent accumulation (NCI) on L*N first range Doppler image data corresponding to N receiving antennas to obtain a fourth range Doppler image; acquiring third target data on the fourth range Doppler image, wherein the third target data is data that meets preset conditions; and determining the distance information and velocity information of the third target data on the fourth range Doppler image as the first target distance and the first target velocity, respectively.

[0075] In this embodiment, the distance information and velocity information corresponding to the data (third target data) that meet the preset conditions on the range Doppler map (fourth range Doppler map) obtained by performing NCI processing on the L*N first range Doppler map data corresponding to N receiving antennas are respectively determined as the first target distance and the first target velocity.

[0076] It is understandable that, compared with the first or second implementation method, this implementation method can improve the energy intensity of the acquired third target data, increase the signal-to-noise ratio, and thus enhance the capabilities of subsequent signal processing algorithms.

[0077] Optionally, the data that meets the preset conditions is, for example, the peak data in the distance Doppler plot.

[0078] In this embodiment, after the radar device determines the first target distance and the first target velocity, it is necessary to extract the data on the first target distance and the first target velocity from each of the L*N first range Doppler image data.

[0079] For example, Figure 6 This is a structural schematic diagram illustrating the extraction of data on the distance and velocity of a first target according to an embodiment of this application. Figure 6 As shown, a radar device including N receiving antennas, after passing through S302 and S303, will have each receiving antenna corresponding to L first range Doppler map data. In this example, these are represented by the 1st RD-Map, the 2nd RD-Map, ... up to the Lth RD-Map. Therefore, in this embodiment, after determining the first target range and the first target velocity, it is necessary to obtain data related to the first target range and the first target velocity from each RD-Map. For example, Figure 6 The data on each black cell in the diagram represents the distance and velocity of the first target.

[0080] S305, determine L groups of data based on L*N first target data corresponding to N receiving antennas, wherein each group of data in the L groups includes N first target data, and the N first target data correspond one-to-one with N different receiving antennas.

[0081] It should be understood that for a radar device with N receiving antennas, after acquiring the first target data in terms of the first target range and the first target velocity in each of the L first range Doppler data corresponding to each receiving antenna, there will be a total of L*N first target data.

[0082] In this embodiment, L sets of data are formed based on the L*N first target data, wherein each set of data includes N first target data, and the N first target data correspond one-to-one with N different receiving antennas.

[0083] In specific implementation, such as Figure 7As shown, the radar equipment can form a first group of data from the first target data in the first RD-Map corresponding to receiving antenna 1, receiving antenna 2, up to receiving antenna N; form a second group of data from the first target data in the second RD-Map corresponding to receiving antenna 1, receiving antenna 2, up to receiving antenna N; and so on, forming a Lth group of data from the first target data in the Lth RD-Map corresponding to receiving antenna 1, receiving antenna 2, up to receiving antenna N.

[0084] S306 processes the L groups of data using a preset super-resolution algorithm.

[0085] In this embodiment, after obtaining L sets of data, the L sets of data can be input into the super-resolution algorithm to detect the angle of the target object that may exist in the first target distance and the first target velocity.

[0086] In this embodiment, the radar device 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. Thus, each receiving antenna will have L second fast time dimension-slow time dimension data, and for the N receiving antennas, there will be a total of L*N second fast time dimension-slow time dimension data. Then, a two-dimensional discrete fast Fourier transform is performed on each of the L*N second fast time dimension-slow time dimension data to obtain L*N first range Doppler map data. Target data for the first target range and the first target velocity are extracted from each first range Doppler map data, resulting in L*N target data. Finally, the L*N target data are formed into L groups of data, where each group of data includes N target data, and these N target data correspond to N different receiving antennas.

[0087] Understandably, in existing technologies, after acquiring N target data points corresponding to the first target distance and velocity across N receiving antennas, a radar device first groups these N data points into a single set. Then, based on this single set, it obtains multiple sets of data, each containing only M target data points from the N data points, where M is less than N. Finally, a preset super-resolution algorithm is used to process these multiple sets of data. It can be seen that in existing technologies, when the radar device processes multiple sets of data using the preset super-resolution algorithm, each set does not fully utilize the data collected by the radar device's N receiving antennas. It should be understood that not fully utilizing the data collected by the radar device's N receiving antennas essentially reduces the radar device's antenna aperture, leading to a decrease in the radar device's angular resolution and consequently, lower accuracy in detecting target points at the first target distance and velocity. In this embodiment, since each set of data input into the preset super-resolution algorithm includes N target data, and since these N target data correspond to N different receiving antennas, it is equivalent to maintaining the original antenna aperture of the radar equipment. Therefore, the angular resolution of the radar equipment will not be reduced, which improves the accuracy of detecting target objects in terms of target distance and target speed.

[0088] Figure 8 This is a structural schematic diagram of a data processing apparatus provided in one embodiment of this application. Specifically, as shown... Figure 8 As shown, the data processing device includes an acquisition module 801 and a processing module 802.

[0089] The acquisition module 801 is used to acquire first fast time dimension-slow time dimension data collected by each of the N receiving antennas of the radar device, wherein the length of the fast time dimension in each first fast time dimension-slow time dimension data 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, wherein 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 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, wherein M2 is less than M1; the processing module 802 is also used to process the L second fast time dimension data corresponding to each receiving antenna. The acquisition module 801 is further configured to acquire first target data in terms of first target distance and first target velocity in each of the L first range Doppler data in the L first range Doppler data corresponding to each receiving antenna; the processing module 802 is further configured to determine L sets of data based on the L*N first target data corresponding to the N receiving antennas, each set of data including N first target data, and the N first target data corresponding to N different receiving antennas; the processing module 802 is further configured to process the L sets of data using a preset super-resolution algorithm.

[0090] In one 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 to obtain the second range Doppler map data corresponding to each receiving antenna; the processing module 802 is further configured to: perform noncoherent accumulation (NCI) on the N second range Doppler map data corresponding one-to-one with the N receiving antennas to obtain a third range Doppler map; the acquisition module 801 is further configured to: acquire second target data on the third range Doppler map, wherein the second target data is data that meets preset conditions; the processing module 802 is further configured to: determine the distance information and velocity information of the second target data on the third range Doppler map as the first target distance and the first target velocity, respectively.

[0091] In one possible implementation, the processing module 802 is further configured to: perform noncoherent accumulation (NCI) on the L*N first range Doppler image data corresponding to the N receiving antennas to obtain a fourth range Doppler image; the acquisition module 801 is further configured to: acquire third target data on the fourth range Doppler image, wherein the third target data is data that meets preset conditions; the processing module 802 is further configured to: determine the distance information and velocity information of the third target data on the fourth range Doppler image as the first target distance and the first target velocity, respectively.

[0092] In one possible implementation, the acquisition module 801 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 802 is further configured to: perform noncoherent accumulation (NCI) on the L second fast time dimension-slow time dimension data corresponding to the arbitrary receiving antenna to obtain a fifth range Doppler image; the acquisition module 801 is further configured to: acquire fourth target data on the fifth range Doppler image, wherein the fourth target data is data that meets preset conditions; the processing module 802 is further configured to: determine the distance information and velocity information of the fourth target data on the fifth range Doppler image as the first target distance and the first target velocity, respectively.

[0093] In one possible implementation, the data that satisfies the preset conditions is the peak data in the distance Doppler graph data.

[0094] Figure 9 This is a structural schematic diagram of a data processing apparatus provided for another embodiment of this application. Figure 9 The apparatus shown can be used to perform the method described in any of the foregoing embodiments.

[0095] like Figure 9 As shown, the device 900 in this embodiment includes a memory 901, a processor 902, a communication interface 903, and a bus 904. The memory 901, processor 902, and communication interface 903 are interconnected via the bus 904.

[0096] The memory 901 can 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, and when the program stored in the memory 901 is executed by the processor 902, the processor 902 performs the execution... Figure 3 The steps of the method shown.

[0097] The processor 902 may be a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, used to execute relevant programs to implement this application. Figure 3 The method shown.

[0098] The processor 902 can also be an integrated circuit chip with signal processing capabilities. In its implementation, the embodiments of this application... Figure 3 Each step of the method can be accomplished through integrated logic circuits in the hardware of the processor 902 or through instructions in software form.

[0099] The processor 902 described above can also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0100] The steps of the method disclosed in the embodiments of this application can be directly manifested as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory 901. The processor 902 reads the information in memory 901 and, in conjunction with its hardware, completes the functions required by the units included in the device of this application. For example, it can execute... Figure 3 The various steps / functions of the illustrated embodiment.

[0101] The communication interface 903 can use, but is not limited to, transceivers to enable communication between the device 900 and other devices or communication networks.

[0102] Bus 904 may include a pathway for transmitting information between various components of device 900 (e.g., memory 901, processor 902, communication interface 903).

[0103] It should be understood that the device 900 shown in the embodiments of this application may be an electronic device, or it may be a chip configured in an electronic device.

[0104] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as 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 a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0105] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0106] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or multiple items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0107] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0108] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0109] Those skilled in the art will understand that, for the sake of convenience and brevity, 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 repeated here.

[0110] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0111] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0112] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0113] If the aforementioned functions are implemented as 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 this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory, random access memory, magnetic disks, or optical disks.

[0114] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A data processing method, characterized in that, include: Acquire the first fast time dimension-slow time dimension data collected by each of the N receiving antennas of the radar equipment. 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 along the fast time dimension. The length of the fast time dimension in each second fast time dimension-slow time dimension data is M2. 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 less 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 L first distance Doppler map data corresponding to each receiving antenna; Obtain the first target data in terms of first target distance and first target velocity from each of the L first range Doppler image data corresponding to each receiving antenna; Based on L corresponding to the N receiving antennas N first target data determine L groups of data, where each of the L groups of data includes N first target data, and each of the N first target data corresponds one-to-one with N different receiving antennas; The L sets of data are processed using a preset super-resolution algorithm.

2. The method according to claim 1, characterized in that, The method further includes: A two-dimensional discrete Fourier transform is performed 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 N second range Doppler image data corresponding one-to-one with the N receiving antennas are incoherently accumulated (NCI) to obtain the third range Doppler image; Acquire second target data on the third distance Doppler image, wherein the second target data is data that meets preset conditions; The distance and velocity information of the second target data on the third distance Doppler map are respectively determined as the distance and velocity of the first target.

3. The method according to claim 1, characterized in that, The method further includes: The L corresponding to the N receiving antennas The N first distance Doppler image data are incoherently accumulated (NCI) to obtain the fourth distance Doppler image; Acquire the third target data on the fourth distance Doppler image, wherein the third target data is data that meets preset conditions; The distance and velocity information of the third target data on the fourth distance Doppler map are respectively determined as the distance and velocity of the first target.

4. The method according to claim 1, characterized in that, The method further includes: Obtain L second fast time dimension-slow time dimension data corresponding to any one of the N receiving antennas; The L second fast time dimension-slow time dimension data corresponding to any one of the receiving antennas are incoherently accumulated (NCI) to obtain the fifth range Doppler image; Acquire the fourth target data on the fifth distance Doppler image, wherein the fourth target data is data that meets preset conditions; The distance and velocity information of the fourth target data on the fifth distance Doppler map are respectively determined as the first target distance and the first target velocity.

5. The method according to any one of claims 2 to 4, characterized in that, The data that meets the preset conditions is the peak data in the distance Doppler graph data.

6. A data processing apparatus, characterized in that, include: The acquisition module 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, where the length of the fast time dimension in each first 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 time dimension-slow time dimension data along the fast time dimension. 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 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 less than M1. The processing module is further configured to 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 L first distance Doppler map data corresponding to each receiving antenna; The acquisition module is further configured to acquire the first target data in terms of the first target distance and the first target velocity in each of the L first range Doppler data corresponding to each receiving antenna; The processing module is also configured to base its processing on L corresponding to the N receiving antennas. N first target data determine L groups of data, where each of the L groups of data includes N first target data, and each of the N first target data corresponds one-to-one with N different receiving antennas; The processing module is also used to process the L sets of data using a preset super-resolution algorithm.

7. The apparatus 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 distance Doppler map data corresponding to each receiving antenna; The processing module is further configured to: perform non-coherent accumulation (NCI) on the N second range Doppler image data corresponding one-to-one with the N receiving antennas to obtain a third range Doppler image; The acquisition module is further configured to: acquire second target data on the third distance Doppler image, wherein the second target data is data that meets preset conditions; The processing module is further configured to: determine the distance information and velocity information of the second target data on the third distance Doppler map as the first target distance and the first target velocity, respectively.

8. The apparatus according to claim 6, characterized in that, The processing module is further configured to: assign L corresponding to the N receiving antennas The N first distance Doppler image data are incoherently accumulated (NCI) to obtain the fourth distance Doppler image; The acquisition module is further configured to: acquire third target data on the fourth distance Doppler image, wherein the third target data is data that meets preset conditions; The processing module is further configured to: determine the distance information and velocity information of the third target data on the fourth distance Doppler map as the first target distance and the first target velocity, respectively.

9. The apparatus according to claim 6, characterized in that, 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 further configured to: perform noncoherent 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 image; The acquisition module is further configured to: acquire fourth target data on the fifth distance Doppler image, wherein the fourth target data is data that meets preset conditions; The processing module is further configured to: determine the distance information and velocity information of the fourth target data on the fifth distance Doppler map as the first target distance and the first target velocity, respectively.

10. The apparatus according to any one of claims 7 to 9, characterized in that, The data that meets the preset conditions is the peak data in the distance Doppler graph data.

11. An autonomous driving device, characterized in that, It includes the apparatus as described in any one of claims 6 to 10.

12. A data processing apparatus, characterized in that, include: Memory and processor; The memory is used to store program instructions; The processor is used to invoke program instructions in the memory to execute the method as described in any one of claims 1 to 5.

13. A computer-readable medium, characterized in that, The computer-readable medium stores program code for computer execution, the program code including instructions for performing the method as described in any one of claims 1 to 5.

14. A computer program product, said computer program product comprising computer program code, characterized in that, When the computer program code is run on a computer, it causes the computer to implement the method as described in any one of claims 1 to 5.

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