Data processing method and device

Abstract

A data processing method and device can be applied to the fields of detection, intelligent driving and the like. The millimeter wave radar obtains first radar data of a current frame (301), performs zero setting on the first radar data according to an interference filtering threshold corresponding to the current frame to obtain second radar data (302), and performs energy inversion according to the second radar data to obtain an amplitude typical value of the current frame (303). And determining an amplitude typical value of the next frame according to the amplitude typical value of the current frame and the amplitude information of the historical frame (304), and determining an interference filtering threshold of the next frame according to the amplitude typical value of the next frame and a preset detection threshold (305). The method can reduce system time consumption and improve threshold accuracy.

Description

Data processing method and device Technical Field

[0001] The present application relates to the field of millimeter wave radar technology, and in particular to a data processing method and device. Background Art

[0002] With the development and advancement of sensor technology, intelligent vehicle detection sensors have become a research hotspot in the field of assisted driving. Intelligent vehicles (smart vehicles for short) provide everyone with a more convenient and safe driving experience. For example, by installing radar sensors on smart vehicles, they can obtain environmental information around the vehicle at all times of the day and night, especially environmental information at long distances, to achieve assisted driving. On-board millimeter-wave radar has become an important sensor for assisted driving. With the large-scale commercial use and popularization of millimeter-wave radar, the increased penetration rate of on-board radar and the limited frequency range will lead to mutual interference. That is, the signal of the other radar is mixed into the radar receiver of the vehicle, causing interference to the radar itself, resulting in missed targets, shortened detection distance, false alarms, etc.

[0003] To reduce interference, the current approach is to estimate the interference detection threshold for the received echo signals after the millimeter-wave radar completes transmission. This threshold is then used to traverse all analog-to-digital converter (ADC) data to suppress interference. However, this approach requires estimating the interference detection threshold only after receiving the echo signal, increasing system time consumption. Furthermore, when estimating the target noise floor in the presence of distributed interference, the interference can be coupled, leading to an estimated interference threshold that is too high and resulting in missed detections.

[0004] Summary of the Invention

[0005] The present application provides a data processing method and apparatus for reducing system time consumption and improving threshold accuracy.

[0006] A first aspect of the present application provides a data processing method, the method comprising: obtaining first radar data of a current frame; setting the first radar data to zero according to an interference filtering threshold of the current frame to obtain second radar data; performing energy inversion based on the second radar data to obtain an amplitude typical value of the current frame; determining an amplitude typical value of a next frame according to the amplitude typical value of the current frame and amplitude information of historical frames; and determining an interference filtering threshold of the next frame according to the amplitude typical value of the next frame and a preset detection threshold.

[0007] In the above aspect, when obtaining the first radar data of the current frame, the interference filtering threshold corresponding to the current frame can be used to set the first radar data to zero. The data after setting to zero is the second radar data. Then, energy inversion can be performed on the second radar data to infer the typical amplitude value of the current frame. Then, based on the amplitude information of the historical frames, the typical amplitude value of the next frame is determined according to the amplitude changes of the historical frames. The typical amplitude value of the next frame is processed according to the preset detection threshold to obtain the interference filtering threshold of the next frame. In particular, since the interference filtering threshold of each frame of radar data has been determined in the previous frame after receiving the frame, the received radar data can be directly filtered and subsequently processed, reducing system time consumption. In addition, the interference filtering threshold is determined in combination with the amplitude information of the historical frames, which can improve the threshold accuracy.

[0008] In one possible implementation, the above steps of performing energy inversion based on the second radar data to infer the typical amplitude value of the current frame include: accumulating energy of the second radar data to obtain the target peak point amplitude and the energy inversion amplitude corresponding to the target peak point amplitude; and determining the typical amplitude value of the current frame based on the target peak point amplitude and the energy inversion amplitude.

[0009] In the above possible implementation manner, after determining the peak point amplitude in the second radar data, the data dimension where the peak point is located can be determined. The energy change amplitude is different in different data dimensions of the signal. Therefore, after determining the dimension of the peak point, the energy inversion amplitude corresponding to the peak point can be mapped. The target peak point amplitude is subtracted from the energy inversion amplitude to obtain the typical value of the current frame amplitude. Based on the typical value of the energy cumulative inversion amplitude, the accuracy of the typical value is improved.

[0010] In a possible implementation, the above step of accumulating energy of the second radar data includes: accumulating radar data of the current frame in the multi-antenna channel, where the accumulated radar data includes the second radar data.

[0011] In the above possible implementation manner, the millimeter-wave radar can receive the echo signal through multiple antenna channels. When performing energy inversion on the second radar data, since the second radar data is data collected through a single channel, the impact on energy is not obvious. In the embodiment of the present application, the method of energy accumulation can be to accumulate the radar data collected in the current frame on multiple antenna channels to improve the accuracy of the typical value.

[0012] In one possible implementation, after setting the first radar data to zero based on the interference filtering threshold of the current frame to obtain the second radar data, the method further includes: converting the second radar data into a two-dimensional matrix, where the two-dimensional matrix includes a sample dimension, a chirp dimension, and a Doppler diversity dimension.

[0013] In the above possible implementation, after obtaining the second radar data, the second radar data can be converted into a two-dimensional matrix. Specifically, zero points in the second radar data are set to 1, and non-zero points in the second radar data are set to 0. This reduces the amount of data and facilitates counting the number of zero points. This two-dimensional matrix can also be called a 0-1 matrix. The two-dimensional matrix includes a sample dimension, a chirp dimension, and a Doppler diversity dimension. Each column of the two-dimensional matrix represents a sample dimension, and each row of the two-dimensional matrix represents a chirp dimension. Each chirp dimension can be divided into multiple Doppler diversity dimensions. Converting the second radar data into a 0-1 matrix saves computational effort.

[0014] In one possible implementation, after setting the first radar data to zero based on the interference filtering threshold of the current frame to obtain the second radar data, the method further includes: counting the total number of zero points in the two-dimensional matrix; determining the zero point distribution characteristics of the Doppler diversity dimension in the two-dimensional matrix; after determining the interference filtering threshold of the next frame based on the amplitude typical value and the preset detection threshold of the next frame, the method further includes: determining the interference threshold difference based on the interference filtering threshold of the current frame and the interference filtering threshold of the next frame; and performing weighted averaging on the total number of zero points, the interference threshold difference, and the zero point distribution characteristics to obtain interference suppression credibility.

[0015] In the above possible implementation, after obtaining the two-dimensional matrix, the millimeter-wave radar can also count the total number of zero points set to zero in the two-dimensional matrix and determine the zero point distribution characteristics based on the distribution of zero points in the Doppler diversity dimension. After determining the interference filtering threshold for the next frame, the interference threshold difference between the interference filtering threshold for the current frame and the interference filtering threshold for the next frame can be determined. A weighted average of the total number of zero points, the zero point distribution characteristics, and the interference threshold difference is then taken to determine the interference suppression confidence level. The interference suppression confidence level indicates the current interference suppression effect. A lower interference suppression confidence level indicates a worse interference suppression effect, and a higher interference suppression confidence level indicates a better interference suppression effect. The interference suppression confidence level can be used to evaluate the accuracy of the interference filtering threshold to assist in adjusting the interference filtering threshold and improve its accuracy.

[0016] In a possible implementation, the step of determining the zero point distribution characteristic of the Doppler diversity dimension in the two-dimensional matrix includes: determining the zero point distribution characteristic according to a proportional relationship between data of multiple Doppler diversity dimensions in the two-dimensional matrix.

[0017] In the above possible implementation manner, the millimeter-wave radar can determine the zero point distribution characteristics based on the proportional relationship between the data of multiple Doppler diversity dimensions in the chirp dimension. Specifically, each Doppler diversity dimension divided by the chirp dimension has its own number of zero points. The zero point distribution characteristics can be determined by comparing the zero points of the Doppler diversity dimension. The interference suppression effect is further evaluated based on the zero point distribution characteristics of the Doppler diversity dimension, thereby improving the accuracy of the interference suppression credibility.

[0018] In one possible implementation, after converting the second radar data into a two-dimensional matrix, the method further includes: counting the total number of zero points in the two-dimensional matrix; determining a noise floor difference value in the second radar data; after determining an interference filtering threshold for the next frame based on a typical amplitude value of the next frame and a preset detection threshold, the method further includes: determining an interference threshold difference based on the interference filtering threshold of the current frame and the interference filtering threshold of the next frame; and performing a weighted average of the total number of zero points, the interference threshold difference, and the noise floor difference value to obtain interference suppression credibility.

[0019] In the above possible implementation, after obtaining the two-dimensional matrix, the millimeter-wave radar can also count the total number of zeros in the two-dimensional matrix and the noise floor difference value in the second radar data. After determining the interference filtering threshold for the next frame, the interference threshold difference between the interference filtering threshold of the current frame and the interference filtering threshold of the next frame can be determined. A weighted average of the total number of zeros, the noise floor difference value, and the interference threshold difference is then taken to determine the interference suppression confidence level. The interference suppression confidence level indicates the current interference suppression effect. A lower interference suppression confidence level indicates a worse interference suppression effect, and a higher interference suppression confidence level indicates a better interference suppression effect. The interference suppression confidence level can be used to evaluate the accuracy of the interference filtering threshold to assist in adjusting the interference filtering threshold and improve its accuracy.

[0020] In a possible implementation, the above step of determining the noise floor difference value in the second radar data includes: determining a measurement noise floor of the second radar data; and determining the noise floor difference value according to the measurement noise floor and a reference noise floor.

[0021] In the above possible implementation manner, the millimeter-wave radar can determine the noise floor difference value by first measuring the noise floor in the second radar data as the measurement noise floor, and then obtaining the reference noise floor of the millimeter-wave radar. The reference noise floor can be predetermined. The reference noise floor is the noise floor information measured in a darkroom that excludes the influence of other factors. Based on the relationship between the measurement noise floor and the reference noise floor, the noise floor difference value can be determined, thereby improving the feasibility of the solution.

[0022] In one possible implementation, the above step of counting the total number of zero points in the two-dimensional matrix includes: determining the number of zero points in the sample dimension, the number of zero points in the chirp dimension, and the number of zero points in the Doppler diversity dimension in the two-dimensional matrix; and performing weighted averaging on the number of zero points in the sample dimension, the number of zero points in the chirp dimension, and the number of zero points in the Doppler diversity dimension to obtain the total number of zero points.

[0023] In the above possible implementations, based on the sample dimension, chirp dimension, and Doppler diversity dimension of the two-dimensional matrix, the total number of zero points in the embodiments of the present application can be determined by first determining the number of zero points in each of the sample dimension, chirp dimension, and Doppler diversity dimension, and then performing a weighted average of the zero points in each dimension based on the weight corresponding to the sample dimension, the chirp dimension, and the Doppler diversity dimension. The total number of zero points can then be determined. This multi-dimensional weighted summation can improve the accuracy of the total number of zero points.

[0024] In a possible implementation, before performing energy inversion based on the second radar data to infer the amplitude typical value of the current frame, the method further includes: converting the second radar data into a frequency domain representation.

[0025] In the above possible implementation manner, before performing energy inversion, the millimeter-wave radar needs to convert the echo signal into a frequency domain representation through a fast Fourier transform, that is, convert the second radar data into a frequency domain representation, where the second radar data can be processed through one or more fast Fourier transforms to improve the feasibility of the solution.

[0026] A second aspect of the present application provides a data processing device that can implement the method of the first aspect or any possible implementation of the first aspect. The device includes corresponding units or modules for executing the above-mentioned method. The units or modules included in the device can be implemented through software and / or hardware. The device can be, for example, a network device, or a chip, chip system, or processor that supports the network device to implement the above-mentioned method. It can also be a logic module or software that can implement all or part of the functions of the network device.

[0027] A third aspect of the present application provides a computer device, comprising: a processor coupled to a memory, the memory configured to store instructions, wherein when the instructions are executed by the processor, the computer device implements the method of the first aspect or any possible implementation of the first aspect. The computer device may be, for example, a network device, or a chip or chip system that supports the network device in implementing the method.

[0028] The fourth aspect of the present application provides a computer-readable storage medium, which stores instructions. When the instructions are executed by a processor, the method provided by the first aspect or any possible implementation method of the first aspect is implemented.

[0029] In a fifth aspect, the present application provides a computer program product, which includes computer program code. When the computer program code is executed on a computer, it implements the method provided in the first aspect or any possible implementation method of the first aspect.

[0030] In a sixth aspect, the present application provides a terminal device, which may be a vehicle or an electronic component, and includes the apparatus in the second aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] FIG1 is a schematic diagram of an application of a millimeter wave radar provided in an embodiment of the present application;

[0032] FIG2 is a schematic structural diagram of a millimeter-wave radar provided in an embodiment of the present application;

[0033] FIG3 is a flow chart of a data processing method provided in an embodiment of the present application;

[0034] FIG4 is a schematic diagram of a threshold inversion process provided in an embodiment of the present application;

[0035] FIG5 is a schematic diagram of a flow chart of determining the reliability of interference suppression according to an embodiment of the present application;

[0036] FIG6 is a schematic diagram of another flow chart for determining the reliability of interference suppression according to an embodiment of the present application;

[0037] FIG7 is a functional structure diagram of a processor implementation provided by an embodiment of the present application;

[0038] FIG8 is a schematic diagram of a processing flow of an interference nulling and statistics module provided in an embodiment of the present application;

[0039] FIG9 is a schematic diagram of a processing flow of an interference threshold calculation and evaluation module provided in an embodiment of the present application;

[0040] FIG10 is a schematic structural diagram of a data processing device provided in an embodiment of the present application;

[0041] FIG11 is a schematic structural diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0042] The present application provides a data processing method and apparatus for reducing system time consumption and improving threshold accuracy.

[0043] The following describes the embodiments of the present application in conjunction with the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the present application, rather than all the embodiments. Those skilled in the art will appreciate that with the development of technology and the emergence of new scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0044] The terms "first," "second," and the like in the specification and claims of this application and in the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" and "having," as well as any variations thereof, are intended to cover non-exclusive inclusions, e.g., a process, method, system, product, or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to these processes, methods, products, or apparatus.

[0045] The following first introduces relevant terms and concepts that may be involved in the embodiments of this application.

[0046] Millimeter-wave radar: Also known as a millimeter-wave radar device, a detector, or a detection device, it works by emitting a detection signal and receiving an echo signal reflected from the target to detect the corresponding target. The target can be a living organism such as a human or animal, or an object such as a vehicle or obstacle. Millimeter-wave radar's detection signal operates in the millimeter wave band.

[0047] In related technologies, millimeter-wave radars are installed in vehicles for target detection, and the accuracy of target detection is ensured through the following methods: special encoding and modulation of the detection signal emitted by the millimeter-wave radar; setting a constraint function for the echo signal based on the characteristics of its own echo signal; or utilizing the millimeter-wave radar network between multiple vehicles to achieve judgment on the same target, etc.

[0048] Energy inversion: After multiple Fast Fourier Transform (FFT) operations on the radar's raw echo data, the accumulated target energy can enhance the mapping between target energy, accumulated energy, and typical energy. Inversion, or reverse deduction, assumes that the target energy, the accumulation method, and parameter information are known to reversely deduce the typical energy of the original echo.

[0049] Figure 1 shows an application diagram of a millimeter-wave radar according to an embodiment of the present application. As shown in Figure 1, the millimeter-wave radar can be installed in various radar carriers, including motor vehicles, drones, rail cars, bicycles, traffic lights, speed measuring devices, network equipment (such as base stations and terminal equipment in various systems), smart terminals (such as smart transportation equipment, smart home equipment, robots), etc.

[0050] Figure 2 shows a schematic diagram of the structure of a millimeter-wave radar according to an embodiment of the present application. As shown in Figure 2, the millimeter-wave radar may include: a transmitting antenna 21, a directional coupler 22, an oscillator 23, a mixer 24, an analog-to-digital converter (ADC) 25, a receiving antenna 26, a processor 27, a controller 28, and other devices. Controller 28 may be located within the millimeter-wave radar; however, controller 28 may also be located outside the millimeter-wave radar. For example, controller 28 may be located within a radar carrier, such as a motor vehicle equipped with a millimeter-wave radar as shown in Figure 1. This application does not limit this.

[0051] Oscillator 23 generates a detection signal, such as a linear frequency modulated continuous wave (LFM). A portion of the detection signal is output to mixer 24 as a local oscillator (LO) signal via directional coupler 22, while a portion is transmitted via transmit antenna 21. Simultaneously, the millimeter-wave radar receives an echo signal reflected from a target in front of the millimeter-wave radar via receive antenna 26. The echo signal is mixed with the LO signal in mixer 24 to generate an intermediate frequency (IF) signal. This IF signal contains target information, which can be relative parameters between the target and the millimeter-wave radar, such as at least one of the target's detection speed, detection range, and detection angle. The IF signal (for example, an IF signal that has been passed through a low-pass filter and amplified; signals above the maximum IF frequency are filtered out by the low-pass filter (the low-pass filter is not shown)) is transmitted to ADC 25 for sampling. The sampled ADC data is input to processor 27, which processes the ADC data (for example, by performing fast Fourier transform (FFT) or spectrum analysis) to obtain target information. The data is then output to controller 28 for control of a radar vehicle, such as a motor vehicle.

[0052] Automotive millimeter-wave radar has become an important sensor for assisted driving. With the large-scale commercial use and popularization of millimeter-wave radar, the increased penetration rate of automotive radar and the limited frequency range will lead to mutual interference. That is, the signal of the opponent's radar is mixed into the radar receiver of the vehicle, causing interference with the vehicle's own radar, resulting in missed targets, shortened detection distance, false alarms and other effects.

[0053] To reduce interference, the current approach is to estimate the interference detection threshold for the received echo signals after the millimeter-wave radar completes transmission. This threshold is then used to traverse all analog-to-digital converter (ADC) data to suppress interference. However, this approach requires estimating the interference detection threshold only after receiving the echo signal, increasing system time consumption. Furthermore, when estimating the target noise floor in the presence of distributed interference, the interference can be coupled, leading to an estimated interference threshold that is too high and resulting in missed detections.

[0054] To solve the above problems, an embodiment of the present application provides a data processing method, which is described as follows.

[0055] Please refer to FIG3 , which is a flow chart of a data processing method provided in an embodiment of the present application. The method includes:

[0056] Step 301: The millimeter-wave radar obtains first radar data of the current frame.

[0057] In this embodiment, after transmitting a detection signal, the millimeter-wave radar may receive an echo signal reflected from the detection target. The millimeter-wave radar may process each frame of echo signals. Specifically, for the current frame, the millimeter-wave radar may select the echo signal of the current frame as the first radar data. Since detection signals from other radars may be present, the millimeter-wave radar may also receive detection signals from other radars in the current frame. Therefore, the first radar data may include other detection signals as interference in addition to the echo signal.

[0058] Step 302: The millimeter-wave radar sets the first radar data to zero according to the interference filtering threshold of the current frame to obtain second radar data.

[0059] In this embodiment, since the echo signal received by the millimeter-wave radar in the current frame is not necessarily reflected from the previous detection signal, the echo signal received in the current frame needs to be filtered. In this embodiment, the first radar data obtained in step 301 can be set to zero using the interference filtering threshold corresponding to the current frame. That is, signals in the first radar data that exceed the interference filtering threshold are set to 0. The zeroed first radar data becomes the second radar data. The interference filtering threshold for the current frame can be provided by other estimation methods, a preset initial value, or a method provided in accordance with the embodiments of this application, and is not limited here.

[0060] For example, during the processing of the first frame of data after power-on, the initialized interference filtering threshold TdicThr0 can be called to perform interference detection and zeroing operations on the first frame of received data AdcSampleData. For non-first frame data after power-on, the interference filtering threshold finvTdicThr passed from the previous frame can be used as the interference filtering threshold TdicThr of the current frame, and interference detection operations can be performed while transmitting and receiving echo signals: if abs(AdcSampleData[i][j][k][q])>TdicThr AdcSampleData[i][j][k][q]=0;

[0061] Where: i∈[1,I]; j∈[1,J]; k∈[1,K]; q∈[1,Q], I is the number of range sampling points; J is the number of chirps; K is the number of time division multiplexing (testing data management / technical data management, TDM); Q is the number of receiving antenna channels.

[0062] As a possible design, after obtaining the second radar data, the second radar data can also be converted into a two-dimensional matrix. That is, the zero points in the second radar data are set to 1, and the non-zero points in the second radar data are set to 0. This reduces the amount of data and facilitates the counting of the number of zero points. This two-dimensional matrix can also be called a 0-1 matrix. The two-dimensional matrix includes a sample dimension, a chirp dimension, and a Doppler division multiple access (DDM) dimension. Each column of the two-dimensional matrix is ​​a sample dimension, each row of the two-dimensional matrix is ​​a chirp dimension, and each chirp dimension can be divided into multiple DDM dimensions.

[0063] Step 303: The millimeter-wave radar performs energy inversion based on the second radar data to obtain a typical amplitude value of the current frame.

[0064] In this embodiment, the echo data will have amplitude variations due to energy influence. Because the energy affected is the known target energy, energy inversion can be performed by determining the current signal amplitude and removing the influence of the known target energy. Specifically, the millimeter-wave radar can determine the signal amplitude in the second radar data and, after removing the influence of the known target energy, obtain the typical amplitude value for the current frame, i.e., the amplitude value that occurs most often in the current frame.

[0065] In one possible design, the millimeter-wave radar can perform energy inversion by first accumulating the energy of the second radar data, determining the target peak point amplitude of the second radar data, and the energy inversion amplitude corresponding to the target peak point amplitude. Then, the typical amplitude value of the current frame can be determined based on the relationship between the target peak point amplitude and the energy inversion amplitude. Specifically, after determining the peak point amplitude in the second radar data, the data dimension where the peak point is located can be determined. The energy change amplitude in different data dimensions of the signal is different. Therefore, after determining the dimension of the peak point, the energy inversion amplitude corresponding to the peak point can be mapped. The target peak point amplitude minus the energy inversion amplitude can be used to obtain the typical amplitude value of the current frame.

[0066] In one possible design, the millimeter-wave radar can receive echo signals through multiple antenna channels. When performing energy inversion on the second radar data, since the second radar data is data collected through a single channel, the impact on energy is not obvious. In the embodiment of the present application, the method of energy accumulation can be to accumulate radar data collected in the current frame through multiple antenna channels, and the accumulated radar data includes the second radar data.

[0067] For example, the millimeter-wave radar can use the radar data accumulated by multiple signals to perform energy inversion, and can determine the peak point amplitude NCI_Eng. Then, according to the dimension of the peak point where the peak point amplitude is located, that is, the sample dimension, chirp dimension and DDM dimension, according to the correspondence between the dimension and the energy inversion amplitude, the energy inversion amplitude NCI_Coff corresponding to the NCI_Eng is determined. Then, the amplitude typical value ADC of the current frame is Amp : ADC Amp =NCI_Eng-NCI_Coff

[0068] The millimeter wave radar can be based on the typical value of the amplitude ADC of the current frame Amp Combined with the historical frame amplitude information, the amplitude typical value ADC_Pred of the next frame is estimated, and the interference filtering threshold finvTdicThr of the next frame is ADC Pred +TdicDetThr, TdicDetThr is the detection threshold.

[0069] In one possible design, before performing energy inversion, the millimeter-wave radar needs to convert the echo signal into a frequency domain representation through FFT, that is, convert the second radar data into a frequency domain representation, where the second radar data can be processed through one or more FFTs, which is not limited here.

[0070] For example, a flow chart of threshold inversion in an embodiment of the present application can be shown in FIG4 , wherein step 401: using the interference filtering threshold of the nth frame to suppress interference on the ADC data AdcSampleData of the nth frame; step 402: performing 1D-FFT / 2D-FFT signal processing on the AdcSampleData after interference suppression; step 403: inverting the amplitude typical value ADC of the nth frame from the processed data. Amp ; Step 404. According to the typical value of the amplitude of the nth frame ADC Amp Predict the interference filtering threshold finvTdicThr of the n+1th frame; Step 405 . Let n=n+1 in step 401 , and pass the interference filtering threshold finvTdicThr of the n+1th frame to step 401 .

[0071] Step 304: The millimeter-wave radar determines the amplitude typical value of the next frame based on the amplitude typical value of the current frame and the amplitude information of the historical frames.

[0072] In this embodiment, the embodiment of the present application can save the amplitude information of the signal strength of each frame of echo data as historical frame amplitude information. The historical frame amplitude information can reflect the amplitude change law of the signal. After obtaining the typical amplitude value of the current frame, the millimeter wave radar can determine the typical amplitude value of the next frame according to the amplitude change law.

[0073] Step 305: The millimeter-wave radar determines the interference filtering threshold for the next frame based on the amplitude typical value of the next frame and the preset detection threshold.

[0074] In this embodiment, after determining the typical amplitude value of the next frame, the millimeter-wave radar can superimpose the typical amplitude value of the next frame according to the preset detection threshold. The superimposed amplitude value can then be used as the interference filtering threshold required for filtering the echo signal of the next frame. For example, the preset detection threshold can be 10dB, that is, the typical amplitude value is increased by 10dB to serve as the interference filtering threshold for the next frame.

[0075] In one possible design, after obtaining a two-dimensional matrix, the millimeter-wave radar can also count the total number of zero points in the two-dimensional matrix that have been set to zero, and determine the zero point distribution characteristics based on the distribution of zero points in the Doppler diversity dimension. After determining the interference filtering threshold for the next frame, the interference threshold difference between the interference filtering threshold for the current frame and the interference filtering threshold for the next frame can be determined. A weighted average of the total number of zero points, the zero point distribution characteristics, and the interference threshold difference is then taken to determine the interference suppression confidence level. The interference suppression confidence level indicates the current interference suppression effect. A lower interference suppression confidence level indicates a worse interference suppression effect, while a higher interference suppression confidence level indicates a better interference suppression effect.

[0076] In one possible design, based on the sample dimension, chirp dimension and DDM dimension of the two-dimensional matrix, the total number of zero points in the embodiment of the present application can be determined by first determining the number of zero points in each dimension of the sample dimension, chirp dimension and DDM dimension, and then taking the weighted average of the zero points of each dimension based on the weight corresponding to the sample dimension, the weight corresponding to the chirp dimension and the weight corresponding to the DDM dimension, and then the total number of zero points can be determined. For example, the 0-1 matrix TdicZeroedMatrix is ​​traversed and tested, and the weighted points in each dimension of the sample, chirp and DDM dimensions are counted to obtain the total number of zero points tdicZeroedTotalNum.

[0077] In one possible design, the millimeter-wave radar can determine the zero-point distribution characteristics based on the proportional relationship between the data of multiple DDM dimensions in the chirp dimension. Specifically, each DDM dimension divided by the chirp dimension has its own number of zero points, and the zero-point distribution characteristics can be determined according to the zero-point comparison of the DDM dimension. For example, the data in a chirp dimension is divided into 4 DDMs, such as DDM1, DDM2, DDM3, and DDM4, where DDM is the number of zero points in the DDM dimension. The zero-point distribution characteristics can be determined by comparing the 4 DDMs with each other. In simple terms, assuming that (DDM1 / DDM2)>3, or (DDM1 / DDM3)>3, or (DDM1 / DDM4)>3, it can be known that the dimension point DDM1 has been filtered too much relative to DDM2, and has excessively interfered with and suppressed DDM1, which can indicate that the current interference suppression credibility is not high. For example, assuming that DDM1 is [1, 1, 1, 1] and DDM2 is [0, 1, 0, 0], then DDM1=4, DDM2=1, and (DDM1 / DDM2)>3, the current interference suppression reliability is not high.

[0078] Exemplarily, the interference threshold difference finvAdcDiff is calculated as finvAdcDiff = finvTdicThr – TdicThr, where finvTdicThr is the interference filtering threshold inverted in the current frame, and TdicThr is the interference filtering threshold used in the current frame. Combined with the total number of zero points tdicZeroedTotalNum and the zero point distribution characteristic tdicZeroeDdmThr, the interference suppression credibility finvAdcCoff can be determined: finvAdcCoff = finvAdcDiffCoff·finvAdcDiff+tdicZeroedTotalNumCoff·tdicZeroedTotalNum+ tdicZeroeDdmThrCoff·tdicZeroeDdmThr,

[0079] Wherein: finvAdcDiffCoff represents the weight coefficient of the interference threshold difference finvAdcDiff; tdicZeroedTotalNumCoff represents the weight coefficient of the total number of zero points tdicZeroedTotalNum; tdicZeroeDdmThrCoff represents the weight coefficient of the zero point distribution characteristic tdicZeroeDdmThr.

[0080] Exemplarily, a flow chart for determining the credibility of interference suppression can be shown in FIG5 , where step 501 is to reduce the data volume of the AdcSampleData after interference suppression and process it into a two-dimensional matrix; step 502 is to perform zero-point statistics on the two-dimensional matrix to determine the total number of zero points set to zero and the zero-point distribution characteristics; step 503 is to determine the interference threshold difference based on the interference filtering threshold of the current frame and the interference filtering threshold of the next frame; step 504 is to perform weighted averaging on the total number of zero points, the zero-point distribution characteristics and the interference threshold difference to calculate the credibility of interference suppression.

[0081] In another possible design, after obtaining the two-dimensional matrix, the millimeter-wave radar can also count the total number of zero points in the two-dimensional matrix and the noise floor difference value in the second radar data. After determining the interference filtering threshold for the next frame, the interference threshold difference between the interference filtering threshold of the current frame and the interference filtering threshold of the next frame can be determined. A weighted average of the total number of zero points, the noise floor difference value, and the interference threshold difference is then taken to determine the interference suppression confidence level. The interference suppression confidence level indicates the current interference suppression effect. A lower interference suppression confidence level indicates a worse interference suppression effect. Correspondingly, a higher interference suppression feasibility level indicates a better interference suppression effect.

[0082] Exemplarily, a flowchart for determining another interference suppression credibility can be shown in Figure 6, step 601. Reduce the data volume of AdcSampleData after interference suppression and process it into a two-dimensional matrix; step 602. Perform zero point statistics on the two-dimensional matrix to determine the total number of zero points set to zero; step 603. Determine the noise floor difference value based on the measured noise floor of the current frame and the reference noise floor; step 604. Determine the interference threshold difference based on the interference filtering threshold of the current frame and the interference filtering threshold of the next frame; step 605. Perform weighted average of the total number of zero points, the noise floor difference value and the interference threshold difference to calculate the interference suppression credibility.

[0083] Among them, the millimeter-wave radar can determine the noise floor difference value by first measuring the noise floor in the second radar data as the measurement noise floor, and then obtaining the reference noise floor of the millimeter-wave radar. The reference noise floor can be predetermined. The reference noise floor is the noise floor information measured in a darkroom that excludes the influence of other factors. The noise floor difference value can be determined based on the relationship between the measurement noise floor and the reference noise floor.

[0084] In one possible implementation, the functional structure diagram of the processor implemented in Figure 2 can be shown in Figure 7, where the processor includes an interference zeroing and statistics module 71, a 1D-FFT module 72, a 2D-FFT module 73, an algorithm (ALG) module 74, a point cloud reporting module 75, and an interference threshold calculation and evaluation module 76. The interference zeroing and statistics module 71 is used to receive ADC data, perform interference zeroing on the ADC data, and then count the total number of zero points and the zero point distribution characteristics of the ADC data after interference zeroing. The ADC data after interference zeroing can be processed by the 1D-FFT module 72 and the 2D-FFT module 73, and then the ALG module 74 performs signal preprocessing or target detection, etc. to generate point cloud intensity information, which is then reported by the point cloud reporting module 75. Among them, the interference threshold calculation and evaluation module 76 can perform energy inversion based on the data processed in the ALG module 74, predict the reference amplitude of the next frame based on the inverted ADC amplitude typical value, then increase the detection threshold to generate the interference filtering threshold of the next frame, and then calculate the interference suppression credibility of the interference filtering threshold of the current frame.

[0085] Exemplarily, the processing flow of the interference zeroing and statistics module 71 can be shown in Figure 8, step 801. Interference threshold transmission, that is, receiving the interference filtering threshold transmitted by the interference threshold calculation and evaluation module 76 of the previous frame; step 802. Interference detection zeroing, that is, performing interference zeroing on the ADC data of the current frame according to the interference filtering threshold transmitted in the previous frame; step 803. Interference weighted statistics, that is, counting the total number of weighted zero points in each dimension of the ADC data after the interference zeroing.

[0086] Exemplarily, the processing flow of the interference threshold calculation and evaluation module 76 can be shown in Figure 9, step 901. Amplitude inversion, that is, using the NCI information of the ADC data accumulated in the ALG module 74 to perform energy inversion to invert the ADC amplitude typical value of the current frame; step 902. Amplitude prediction, that is, predicting the amplitude typical value of the next frame based on the ADC amplitude typical value of the current frame and the amplitude information of the historical frames; step 903. Interference threshold calculation, generating the interference filtering threshold of the next frame based on the amplitude typical value of the next frame in combination with the check threshold; step 904. Interference suppression evaluation, that is, generating the interference suppression credibility of the current frame based on the difference between the interference filtering thresholds of the current frame and the next frame and the zeroing result of the current frame.

[0087] In an embodiment of the present application, when the millimeter-wave radar obtains the first radar data of the current frame, it can use the interference filtering threshold corresponding to the current frame to zero the first radar data. The data after zeroing is the second radar data. Then, it can perform energy inversion on the second radar data to infer the typical amplitude value of the current frame. Then, based on the amplitude information of the historical frames and the amplitude changes of the historical frames, it can determine the typical amplitude value of the next frame. The typical amplitude value of the next frame is processed according to the preset detection threshold to obtain the interference filtering threshold of the next frame. In particular, since the interference filtering threshold of each frame of radar data has been determined in the previous frame after receiving the frame, the received radar data can be directly filtered and subsequently processed, reducing system time consumption. In addition, the interference filtering threshold is determined in combination with the amplitude information of the historical frames, which can improve the threshold accuracy.

[0088] The data processing method is described above, and the device for executing the method is described below.

[0089] Please refer to FIG10 , which is a schematic diagram of the structure of a data processing device provided in an embodiment of the present application. The device 100 includes:

[0090] Processing unit 1001 is configured to obtain first radar data of a current frame, set the first radar data to zero based on an interference filtering threshold of the current frame to obtain second radar data, perform energy inversion based on the second radar data to obtain an amplitude typical value of the current frame, determine an amplitude typical value of a next frame based on the amplitude typical value of the current frame and amplitude information of historical frames, and determine an interference filtering threshold for the next frame based on the amplitude typical value of the next frame and a preset detection threshold.

[0091] The processing unit 1001 is configured to execute steps 301 to 304 in the method embodiment of FIG3 .

[0092] Optionally, the processing unit 1001 is specifically configured to:

[0093] Performing energy accumulation on the second radar data to obtain a target peak point amplitude and an energy inversion amplitude corresponding to the target peak point amplitude;

[0094] The typical amplitude value of the current frame is determined according to the target peak point amplitude and the energy inversion amplitude.

[0095] Optionally, the processing unit 1001 is specifically configured to:

[0096] Accumulate radar data of a current frame in the multi-antenna channel, where the accumulated radar data includes second radar data.

[0097] Optionally, the processing unit 1001 is further configured to:

[0098] The second radar data is converted into a two-dimensional matrix, where the two-dimensional matrix includes a sample dimension, a chirp dimension, and a Doppler diversity dimension.

[0099] Optionally, the processing unit 1001 is further configured to:

[0100] Count the total number of zeros in a two-dimensional matrix;

[0101] Determine the null distribution characteristics of the Doppler diversity dimension in a two-dimensional matrix;

[0102] Determining an interference threshold difference based on the interference filtering threshold of the current frame and the interference filtering threshold of the next frame;

[0103] The total number of zero points, interference threshold difference and zero point distribution characteristics are weighted averaged to obtain the interference suppression reliability.

[0104] Optionally, the processing unit 1001 is specifically configured to:

[0105] The zero point distribution characteristic is determined according to the proportional relationship between data of multiple Doppler diversity dimensions in the two-dimensional matrix.

[0106] Optionally, the processing unit 1001 is further configured to:

[0107] Count the total number of zeros in a two-dimensional matrix;

[0108] determining a noise floor difference value in the second radar data;

[0109] Determining an interference threshold difference based on the interference filtering threshold of the current frame and the interference filtering threshold of the next frame;

[0110] The total number of zero points, interference threshold difference and noise floor difference values ​​are weighted averaged to obtain the interference suppression reliability.

[0111] Optionally, the processing unit 1001 is specifically configured to:

[0112] determining a measurement noise floor of the second radar data;

[0113] The noise floor difference value is determined based on the measured noise floor and the reference noise floor.

[0114] Optionally, the processing unit 1001 is specifically configured to:

[0115] Determining the number of zero points in the sample dimension, the number of zero points in the chirp dimension, and the number of zero points in the Doppler diversity dimension in the two-dimensional matrix;

[0116] The number of zero points in the sample dimension, the number of zero points in the chirp dimension, and the number of zero points in the Doppler diversity dimension are weighted averaged to obtain the total number of zero points.

[0117] Optionally, the processing unit 1001 is further configured to:

[0118] The second radar data is converted into a frequency domain representation.

[0119] Figure 11 shows a possible logical structure diagram of a computer device 110 provided in an embodiment of the present application. The computer device 110 includes: a processor 1101, a communication interface 1102, a storage system 1103 and a bus 1104. The processor 1101, the communication interface 1102 and the storage system 1103 are interconnected via the bus 1104. In an embodiment of the present application, the processor 1101 is used to control and manage the actions of the computer device 110. For example, the processor 1101 is used to execute the steps performed by the millimeter wave radar in the method embodiment of Figure 3. The communication interface 1102 is used to support the computer device 110 to communicate. The storage system 1103 is used to store the program code and data of the computer device 110.

[0120] The processor 1101 may be a central processing unit (CPU), a general-purpose processor (GPOR), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic device (PLD), a transistor logic device (TLD), a hardware component, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor 1101 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like. The bus 1104 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, for example. Buses may be classified as address buses, data buses, control buses, and the like. For ease of illustration, FIG. 11 shows only one thick line, but this does not imply that there is only one bus or only one type of bus.

[0121] The processing unit 1001 in the apparatus 100 is equivalent to the processor 1101 in the computer device 110 .

[0122] The computer device 110 of this embodiment may correspond to the millimeter-wave radar in the method embodiment of FIG. 3 . The communication interface 1102 in the computer device 110 may implement the functions and / or various steps of the millimeter-wave radar in the method embodiment of FIG. 3 . For the sake of brevity, they will not be described in detail here.

[0123] It should be understood that the division of units in the above device is merely a division of logical functions. In actual implementation, they can be fully or partially integrated into one physical entity, or they can be physically separated. Moreover, the units in the device can all be implemented in the form of software called through processing elements; or they can all be implemented in the form of hardware; or some units can be implemented in the form of software called through processing elements, and some units can be implemented in the form of hardware. For example, each unit can be a separately established processing element, or it can be integrated into a certain chip of the device. In addition, it can also be stored in the memory in the form of a program, called by a certain processing element of the device and perform the function of the unit. In addition, all or part of these units can be integrated together, or they can be implemented independently. The processing element described here can also be a processor, which can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each unit above can be implemented by the integrated logic circuit of the hardware in the processor element or in the form of software called through the processing element.

[0124] In one example, the unit in any of the above devices can be one or more integrated circuits configured to implement the above method, such as one or more application specific integrated circuits (ASICs), or one or more digital singnal processors (DSPs), or one or more field programmable gate arrays (FPGAs), or a combination of at least two of these integrated circuit forms. For another example, when the unit in the device can be implemented in the form of a processing element scheduler, the processing element can be a general-purpose processor, such as a CPU or other processor that can call a program. For another example, these units can be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0125] In another embodiment of the present application, a computer-readable storage medium is provided, in which computer-executable instructions are stored. When the processor of the device executes the computer-executable instructions, the device executes the method performed by the millimeter-wave radar in the above method embodiment.

[0126] In another embodiment of the present application, a computer program product is provided, comprising computer-executable instructions stored in a computer-readable storage medium. When a processor of a device executes the computer-executable instructions, the device performs the method performed by the millimeter-wave radar in the above method embodiment.

[0127] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0128] 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 merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0129] The units described as separate components may or may not be physically separate, and 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0130] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0131] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it 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 prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

Claims

1. A data processing method, characterized in that: include: Get the first radar data of the current frame; Setting the first radar data to zero according to the interference filtering threshold of the current frame to obtain second radar data; Performing energy inversion according to the second radar data to obtain a typical amplitude value of the current frame; Determine the amplitude typical value of the next frame according to the amplitude typical value of the current frame and the amplitude information of the historical frames; The interference filtering threshold of the next frame is determined according to the typical amplitude value of the next frame and a preset detection threshold.

2. The method according to claim 1, characterized in that The performing energy inversion according to the second radar data to infer the amplitude typical value of the current frame includes: Performing energy accumulation on the second radar data to obtain a target peak point amplitude and an energy inversion amplitude corresponding to the target peak point amplitude; The typical amplitude value of the current frame is determined according to the target peak point amplitude and the energy inversion amplitude.

3. The method according to claim 2, characterized in that The performing energy accumulation on the second radar data comprises: Accumulate the radar data of the current frame in the multi-antenna channel, where the accumulated radar data includes the second radar data.

4. The method according to any one of claims 1 to 3, characterized in that: After the first radar data is set to zero according to the interference filtering threshold of the current frame to obtain the second radar data, the method further includes: The second radar data is converted into a two-dimensional matrix, where the two-dimensional matrix includes a sample dimension, a chirp dimension, and a Doppler diversity dimension.

5. The method according to claim 4, characterized in that After the first radar data is set to zero according to the interference filtering threshold of the current frame to obtain the second radar data, the method further includes: Counting the total number of zero points in the two-dimensional matrix; Determining a zero point distribution characteristic of the Doppler diversity dimension in the two-dimensional matrix; After determining the interference filtering threshold of the next frame according to the amplitude typical value of the next frame and the preset detection threshold, the method further includes: Determining an interference threshold difference according to the interference filtering threshold of the current frame and the interference filtering threshold of the next frame; A weighted average is performed on the total number of zero points, the interference threshold difference and the zero point distribution characteristic to obtain interference suppression credibility.

6. The method according to claim 5, characterized in that The determining of the zero point distribution characteristics of the Doppler diversity dimension in the two-dimensional matrix includes: The zero point distribution characteristic is determined according to a proportional relationship between data of a plurality of Doppler diversity dimensions in the two-dimensional matrix.

7. The method according to claim 4, characterized in that After converting the second radar data into a two-dimensional matrix, the method further includes: Counting the total number of zero points in the two-dimensional matrix; Determining a noise floor difference value in the second radar data; After determining the interference filtering threshold of the next frame according to the amplitude typical value of the next frame and the preset detection threshold, the method further includes: Determining an interference threshold difference according to the interference filtering threshold of the current frame and the interference filtering threshold of the next frame; The total number of zero points, the interference threshold difference and the background noise difference value are weighted averaged to obtain the interference suppression credibility.

8. The method according to claim 7, characterized in that Determining the noise floor difference value in the second radar data includes: determining a measurement noise floor of the second radar data; The noise floor difference value is determined according to the measured noise floor and a reference noise floor.

9. The method according to any one of claims 5 to 8, characterized in that: The counting of the total number of zero points in the two-dimensional matrix comprises: Determining the number of zero points of the sample dimension, the number of zero points of the chirp dimension, and the number of zero points of the Doppler diversity dimension in the two-dimensional matrix; A weighted average is performed on the number of zero points in the sample dimension, the number of zero points in the chirp dimension, and the number of zero points in the Doppler diversity dimension to obtain the total number of zero points.

10. The method according to any one of claims 1 to 9, characterized in that: Before performing energy inversion according to the second radar data to infer the typical amplitude value of the current frame, the method further includes: The second radar data is converted to a frequency domain representation.

11. A data processing device, characterized in that: include: A processing unit is used to obtain first radar data of a current frame, set the first radar data to zero according to an interference filtering threshold of the current frame to obtain second radar data, perform energy inversion according to the second radar data to obtain an amplitude typical value of the current frame, determine an amplitude typical value of a next frame according to the amplitude typical value of the current frame and amplitude information of historical frames, and determine an interference filtering threshold of the next frame according to the amplitude typical value of the next frame and a preset detection threshold.

12. The device according to claim 11, characterized in that The processing unit is specifically used for: Performing energy accumulation on the second radar data to obtain a target peak point amplitude and an energy inversion amplitude corresponding to the target peak point amplitude; The typical amplitude value of the current frame is determined according to the target peak point amplitude and the energy inversion amplitude.

13. The device according to claim 12, characterized in that The processing unit is specifically used for: Accumulate the radar data of the current frame in the multi-antenna channel, where the accumulated radar data includes the second radar data.

14. The device according to any one of claims 11 to 13, characterized in that: The processing unit is also used for: The second radar data is converted into a two-dimensional matrix, where the two-dimensional matrix includes a sample dimension, a chirp dimension, and a Doppler diversity dimension.

15. The device according to claim 14, characterized in that The processing unit is also used for: Counting the total number of zero points in the two-dimensional matrix; Determining a zero point distribution characteristic of the Doppler diversity dimension in the two-dimensional matrix; Determining an interference threshold difference according to the interference filtering threshold of the current frame and the interference filtering threshold of the next frame; A weighted average is performed on the total number of zero points, the interference threshold difference and the zero point distribution characteristic to obtain interference suppression credibility.

16. The device according to claim 15, characterized in that The processing unit is specifically used for: The zero point distribution characteristic is determined according to a proportional relationship between data of a plurality of Doppler diversity dimensions in the two-dimensional matrix.

17. The device according to claim 14, characterized in that The processing unit is also used for: Counting the total number of zero points in the two-dimensional matrix; Determining a noise floor difference value in the second radar data; Determining an interference threshold difference according to the interference filtering threshold of the current frame and the interference filtering threshold of the next frame; The total number of zero points, the interference threshold difference and the background noise difference value are weighted averaged to obtain the interference suppression credibility.

18. The device according to claim 17, characterized in that The processing unit is specifically used for: determining a measurement noise floor of the second radar data; The noise floor difference value is determined according to the measured noise floor and a reference noise floor.

19. The device according to any one of claims 15 to 18, characterized in that: The processing unit is specifically used for: Determining the number of zero points of the sample dimension, the number of zero points of the chirp dimension, and the number of zero points of the Doppler diversity dimension in the two-dimensional matrix; A weighted average is performed on the number of zero points in the sample dimension, the number of zero points in the chirp dimension, and the number of zero points in the Doppler diversity dimension to obtain the total number of zero points.

20. The device according to any one of claims 11 to 19, characterized in that The processing unit is also used for: The second radar data is converted to a frequency domain representation.

21. A computer device, characterized in that: include: a processor coupled to the memory, The processor is configured to execute instructions stored in the memory, so that the computer device performs the method according to any one of claims 1 to 10.

22. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed by a processor, the method according to any one of claims 1 to 10 is implemented.

23. A computer program product, characterized in that The computer program product includes computer program code, and is characterized in that when the computer program code is run on a computer, the method according to any one of claims 1 to 10 is implemented.

24. A terminal device, characterized in that: The terminal device comprises the apparatus according to any one of claims 11 to 20.

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