Method and device for realizing radar performance detection

Abstract

The invention discloses a method and device for realizing radar performance detection, and the method comprises the steps: extracting waveform feature information from an echo signal after analog-to-digital conversion and / or an echo signal after fast Fourier transform through the automatic detection of waveform data, observing more possible abnormities from a time domain and / or a frequency domain, and improving the detection accuracy. And the detection of the radar performance is more comprehensive. When an abnormal condition occurs, a guarantee is provided for positioning an abnormal root cause. In the embodiment of the invention, data acquisition, function verification and verification result generation are all automatically completed, detection criteria, namely judgment conditions, can be quantified, and compared with a manual research and judgment mode, resource investment is reduced, detection efficiency is improved, and it is ensured that the detection result is more accurate.

Description

Technical Field

[0001] The present application relates to but is not limited to detection technology, and in particular to a method and device for realizing radar performance detection. Background Art

[0002] Millimeter-wave radar is a commonly used sensor used to detect target objects and obtain information about their characteristics and location. When the radar is working, it emits radio waves. When these radio waves interact with the target object, they are scattered by the target and then received and processed by the radar to form an echo signal. The echo signal contains information about the target object, such as its distance, speed, size, and material properties. In millimeter-wave radar: the echo signal is affected by the characteristics of the target, such as the shape and reflection characteristics of the target, which will produce a specific pattern or modulation in the echo signal, allowing the radar system to identify the nature of the target; the echo signal is also affected by the operating characteristics of the radar itself, including the transmission frequency, antenna radiation pattern, etc., which will produce specific characteristics in the echo signal; the echo signal is usually continuous, high-frequency data, containing a lot of information, and these dense echo signals contain richer information than the final sparse detection results.

[0003] In the related technology, the method of directly observing the test results to judge whether the measured target distance, speed, and direction meet the expectations only verifies the radar performance based on the final test results, which cannot fully reflect the performance of the radar, and it is difficult to locate the root cause through abnormal data when an abnormality occurs. Moreover, this manual interpretation method is time-consuming and labor-intensive. Analyzing and understanding this data may require more time and work, and requires complex signal processing and analysis. Summary of the invention

[0004] The present application provides a method and device for realizing radar performance detection, which can automatically and fully detect the performance of the radar and provide a guarantee for locating the root cause through abnormal data.

[0005] An embodiment of the present invention provides a method for implementing radar performance detection, including:

[0006] Obtain a configuration file for detecting radar performance;

[0007] Extracting waveform feature information from the echo signal after analog-to-digital conversion and / or the echo signal after fast Fourier transformation according to the obtained configuration file;

[0008] The correctness of the waveform is judged based on the extracted waveform feature information and the judgment conditions in the configuration file.

[0009] In an exemplary embodiment, the configuration file is one or more than one.

[0010] In an exemplary embodiment, when the result of the judgment is incorrect, the method further includes: recording the waveform data of the original echo signal and the result of the judgment.

[0011] In an exemplary embodiment, when there are two or more configuration files, the method further includes:

[0012] In the case that the configuration files are not completely traversed, the next configuration file of the configuration files is obtained, and the step of extracting waveform feature information from the echo signal after analog-to-digital conversion and / or the echo signal after fast Fourier transformation according to the obtained configuration files is returned.

[0013] In an exemplary embodiment, the waveform characteristic information includes one or more items;

[0014] The correctness of the waveform is judged according to the extracted waveform feature information and the judgment condition in the configuration file, including:

[0015] When there is a judgment result in any one or any combination of the judgments that does not meet the judgment condition, the result of the judgment on the correctness of the waveform is incorrect; when the judgment results of all the judgments meet the judgment condition, the result of the judgment on the correctness of the waveform is correct.

[0016] In an exemplary embodiment, in the case of extracting waveform characteristic information from the echo signal after analog-to-digital conversion, the extracted waveform characteristic information includes any one or any combination of the following:

[0017] The first waveform characteristic information: the difference between adjacent sampling points

[0018] Second waveform feature information: z-score difference of the same sampling point between different chirps and coefficient of variation

[0019] The third waveform characteristic information includes one or any combination of the following: the mean value of each chirp Relative mean of each chirp The difference between the positive and negative swings of each chirp Relative positive and negative swing difference of each chirp

[0020] Fourth waveform characteristic information: adc flat top area length

[0021] Fifth waveform characteristic information: fill in 0 position

[0022] In an exemplary embodiment, the extracted waveform features include the first waveform feature information; the decision condition includes: the difference between the adjacent sampling points is greater than the first threshold, then the chirp arrangement is abnormal and does not meet the judgment condition; the difference between adjacent sampling points is not greater than the first threshold, the chirp arrangement is normal and the decision condition is met;

[0023] The extracted waveform features include the second waveform feature information; the decision conditions include: the z-score difference of the same sampling point between the different chirps is greater than the second threshold, then the chirp overlap is abnormal and does not meet the judgment condition; the z-score difference of the same sampling point between different chirps is not greater than the second threshold, then the overlap between the chirps is normal and satisfies the judgment condition; and the coefficient of variation of the same sampling point between the different chirps is greater than the third threshold, then the overlap between the chirps is abnormal and does not meet the judgment condition; the coefficient of variation of the same sampling point between the different chirps is not greater than the third threshold, then the overlap between chirps is normal and the judgment condition is met;

[0024] The extracted waveform features include the third waveform feature information; the decision conditions include one or any combination of the following: the mean of each chirp If the difference is greater than the fourth threshold, the DC bias of the frame data is abnormal and does not meet the judgment condition. The difference between the chirp values ​​is not greater than the fourth threshold, then the DC bias of the frame data is normal and meets the judgment condition; If the difference is greater than the fifth threshold, the DC bias of the frame data is abnormal and does not meet the judgment condition. The difference between the positive and negative swing amplitudes of each chirp is not greater than the fifth threshold, then the DC bias of the frame data is normal and meets the judgment condition; If the difference is greater than the sixth threshold, the DC bias of the frame data is abnormal and does not meet the judgment condition. The difference between the positive and negative swing amplitudes of the chirps is not greater than the sixth threshold, then the DC bias of the frame data is normal and meets the judgment condition; If the difference is greater than the seventh threshold, the DC bias of the frame data is abnormal and does not meet the judgment condition. The difference between them is not greater than the seventh threshold, then the DC bias of the frame data is normal and meets the judgment condition;

[0025] The extracted waveform features include the fourth waveform feature information; the decision conditions include: the length of the flat-top area in the single chirp is greater than the eighth threshold, whether the chirp is saturated or not, and does not meet the judgment condition; the length of the flat-top area in the single chirp is not greater than the eighth threshold, the chirp is not saturated and the decision condition is met.

[0026] The extracted waveform features include the fifth waveform feature information; the decision conditions include: the position where the value 0 appears in the valid data of the analog-to-digital conversion and the theoretical zero-filling position If the value of the current analog-to-digital conversion data is different, the position of the zero padding of the current analog-to-digital conversion data is abnormal and does not meet the judgment condition; the position where the value of 0 appears in the valid data of the analog-to-digital conversion and the theoretical zero padding position If they are the same, the position of the zero padding of the current analog-to-digital conversion data is normal, and the judgment condition is met.

[0027] In an exemplary embodiment, the analog-to-digital converted data is obtained by directly collecting the echo signal, or by inversely transforming one-dimensional fast Fourier transform (1D-FFT) data.

[0028] In an exemplary embodiment, for the case of extracting waveform feature information from the echo signal after fast Fourier transformation, the echo signal after fast Fourier transformation includes: an echo signal after 1D-FFT and / or an echo signal after two-dimensional fast Fourier transformation 2D-FFT.

[0029] In an exemplary embodiment, in the case of extracting waveform feature information from the echo signal after 1D-FFT, the extracted waveform feature information includes any one or any combination of the following:

[0030] The sixth waveform characteristic information: the difference between the distance corresponding to the peak value of the distance dimension and the target distance

[0031] Seventh waveform feature information: maximum amplitude difference between chirps

[0032] Eighth waveform characteristic information: maximum phase difference between chirps

[0033] In an exemplary instance, the extracted waveform features include the sixth waveform feature information; the judgment condition includes: if the difference between the distance between the peak of the non-leakage area of ​​the 1D-FFT of each chirp and the actually set target distance is greater than the ninth threshold, the 1D-FFT is abnormal and does not meet the judgment condition; if the difference between the distance between the peak of the non-leakage area of ​​the 1D-FFT of each chirp and the actually set target distance is not greater than the ninth threshold, the 1D-FFT is normal and meets the judgment condition;

[0034] The extracted waveform features include the seventh waveform feature information; the judgment condition includes: if the maximum amplitude difference of the distance unit where the target is located between different chirps is greater than the tenth threshold, then the amplitude difference between the chirps is abnormal and does not meet the judgment condition; if the maximum amplitude difference of the distance unit where the target is located between different chirps is not greater than the tenth threshold, then the amplitude difference between the chirps is normal and meets the judgment condition;

[0035] The extracted waveform features include the eighth waveform feature information; the judgment condition includes: if the maximum phase difference of the distance units where the targets between different chirps are located is greater than the eleventh threshold, then the phase difference between the chirps is abnormal and does not meet the judgment condition; if the maximum phase difference of the distance units where the targets between different chirps are located is not greater than the preset eleventh threshold, then the phase difference between the chirps is normal and meets the judgment condition.

[0036] In an exemplary embodiment, the 1D-FFT data is obtained by directly collecting the echo signal, or by performing 1D-FFT transformation on the analog-to-digital converted data.

[0037] In an exemplary embodiment, in the case of extracting waveform feature information from the echo signal after 2D-FFT, the extracted waveform feature information includes any one or any combination of the following:

[0038] Ninth waveform characteristic information: the difference between the speed dimension peak value corresponding to the distance and the target speed

[0039] Tenth waveform feature information: Maximum difference in target amplitude between receiving channels

[0040] Eleventh waveform characteristic information: noise floor fluctuation range

[0041] Waveform 12: Maximum difference in noise floor between receiving channels

[0042] Thirteenth waveform characteristic information: SNR information of the target spectrum peak and other spectrum peaks except the target spectrum peak in the 2D-FFT plane

[0043] In an exemplary embodiment, the extracted waveform features include the ninth waveform feature information; the judgment condition includes: if the speed value corresponding to the speed unit where the peak value of the non-leakage area of ​​the 2D-FFT of each chirp is located and the actual target speed difference is greater than the twelfth threshold, then the 2D-FFT is abnormal and does not meet the judgment condition; if the speed value corresponding to the speed unit where the peak value of the non-leakage area of ​​the 2D-FFT of each chirp is located and the actual target speed difference is not greater than the preset twelfth threshold, then the 2D-FFT is normal and meets the judgment condition;

[0044] The extracted waveform features include the tenth waveform feature information; the judgment condition includes: if the amplitude difference of the distance unit and the speed unit where the targets of each receiving channel are located is greater than the thirteenth threshold, then there is obvious receiving channel imbalance and the judgment condition is not met; if the amplitude difference of the distance unit and the speed unit where the targets of each receiving channel are located is not greater than the preset thirteenth threshold, then there is no obvious receiving channel imbalance and the judgment condition is met.

[0045] The extracted waveform features include the eleventh waveform feature information; the judgment condition includes: if the difference of the noise floors in different areas is greater than the fourteenth threshold, the noise floor flatness is abnormal and the judgment condition is not met; if the difference of the noise floors in different areas is not greater than the fourteenth threshold, the noise floor flatness is normal and the judgment condition is met;

[0046] The extracted waveform features include the twelfth waveform feature information; the judgment condition includes: if the average noise difference of each of the receiving channels is greater than the fifteenth threshold, then there is obvious receiving channel imbalance and the judgment condition is not met; if the average noise difference of each of the receiving channels is not greater than the fifteenth threshold, then there is no obvious receiving channel imbalance and the judgment condition is met.

[0047] In an exemplary embodiment, the 2D-FFT data is obtained by directly collecting the echo signal, or by performing 2D-FFT transformation on the analog-to-digital converted data or the 1D-FFT data.

[0048] An embodiment of the present application also provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to execute any of the above methods for implementing radar performance detection.

[0049] An embodiment of the present application further provides a device for implementing radar performance detection, including a memory and a processor, wherein the memory stores the following instructions that can be executed by the processor: used to execute the steps of any of the above-mentioned methods for implementing radar performance detection.

[0050] The embodiment of the present application further provides a device for realizing radar performance detection, comprising: a preprocessing module, a processing module, and an analysis module; wherein:

[0051] A pre-processing module, used for obtaining a configuration file for detecting radar performance;

[0052] A processing module, used for extracting waveform feature information from the echo signal after analog-to-digital conversion and / or the echo signal after fast Fourier transformation according to the obtained configuration file;

[0053] The analysis module is used to judge the correctness of the waveform according to the extracted waveform feature information and the judgment conditions in the configuration file.

[0054] In an exemplary embodiment, the extracted waveform feature information is one or more than one item; and also includes a recording module for recording the original waveform data of the echo signal and the result of the judgment when any of the judgment items does not meet the judgment condition.

[0055] In an exemplary embodiment, the configuration files include two or more types; the analysis module is further used to: when the configuration files have not been traversed completely, obtain the next configuration file in the configuration files and return it to the processing module.

[0056] The embodiment of the present application automatically checks the waveform data, extracts waveform feature information from the echo signal after analog-to-digital conversion and / or the echo signal after fast Fourier transformation, observes more possible anomalies from the time domain and / or frequency domain, and makes the detection of radar performance more comprehensive. When an abnormal situation occurs, it provides a guarantee for locating the root cause of the abnormality. Moreover, in the embodiment of the present application, data collection, function verification, and verification result generation are all completed automatically, and the detection criteria, that is, the judgment conditions, can be quantified. Compared with the manual judgment method, it reduces resource investment, improves detection efficiency, and ensures that the detection results are more accurate.

[0057] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The accompanying drawings are used to provide further understanding of the technical solution of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the technical solution of the present application and do not constitute a limitation on the technical solution of the present application.

[0059] Figure 1 A schematic diagram of a flow chart of a method for implementing radar performance detection in an embodiment of the present application;

[0060] Figure 2 This is an example schematic diagram of a complete testing process in an embodiment of the present application;

[0061] Figure 3 Schematic diagram of the structure of the device for realizing radar performance detection in an embodiment of the present application. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solution and advantages of the present application more clear, the embodiments of the present application will be described in detail below with reference to the accompanying drawings. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other arbitrarily without conflict.

[0063] In order to facilitate understanding of the present application, the present application will be described more fully below with reference to the relevant drawings. Embodiments of the present application are provided in the drawings. However, the present application can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive.

[0064] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0065] It is understood that the terms "first" and "second" used in this application are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0066] It can be understood that the “connection” in the following embodiments should be understood as “electrical connection”, “communication connection”, etc. if the connected circuits, modules, units, etc. have electrical signals or data transmission between each other.

[0067] When used herein, the singular forms "a", "an", and "said / the" may also include plural forms, unless the context clearly indicates otherwise. It should also be understood that the terms "include / comprise" or "have" and the like specify the presence of stated features, wholes, steps, operations, components, parts, or combinations thereof, but do not exclude the possibility of the presence or addition of one or more other features, wholes, steps, operations, components, parts, or combinations thereof. At the same time, the term "and / or" used in this specification includes any and all combinations of the relevant listed items.

[0068] When an abnormality occurs in the radar, the final detection result and waveform of the radar are usually abnormal. However, compared with the abnormality of the final detection result of the radar, the abnormality of the echo signal is easier to observe because the echo signal contains more information, and it is also easier to locate the root cause of the abnormality based on the original waveform data. Based on this analysis, the embodiment of the present application realizes automation of radar performance detection by collecting waveform data and analyzing the collected waveform data, and the detection criteria can be quantified. Compared with the manual judgment method, less resource investment, higher detection efficiency and more accurate detection results are required.

[0069] Figure 1 FIG. 1 is a flow chart of a method for implementing radar performance detection in an embodiment of the present application. Figure 1 As shown, it may include:

[0070] Step 100: Obtain a configuration file for detecting radar performance.

[0071] In an exemplary embodiment, configuration parameters for different radars, such as: bandwidth, sampling frequency, waveform feature detection items, judgment conditions for different waveform features, etc. In one embodiment, the configuration file can be one, or the configuration file includes multiple different configuration files (such as configuration file 1, configuration file 2... configuration file n). In the embodiment of the present application, different configuration files can be configured for different radars and detection requirements, and an automated tool is used to generate flash.bin files based on different configuration files. After that, the test board is powered on through a relay, and the flash.bin file is automatically burned into the test board to form a configuration file library. In this way, when it is necessary to test the radar performance, the configuration files can be directly extracted one by one from the configuration file to perform the corresponding test.

[0072] In an exemplary embodiment, before step 100, a test environment will be set up. Generally, when developing a project or releasing a version, it is necessary to ensure that the basic functions of the lower computer and the upper computer are normal and the output results are basically consistent. In this case, a large number of tests are required. The lower computer usually refers to the radar system itself, including radar hardware and control unit, and the upper computer is an external computer and software for remote control, data collection and analysis.

[0073] In one embodiment, if Figure 2 As shown in the figure, a relatively complete testing process can include: First, propose test requirements. Generally, comprehensive testing is required when there are major changes in SDK or SIL (such as driver updates, additions and deletions of functions) or when the official version is released. SDK is a software development kit that usually contains a set of tools, libraries, documents, and sample codes to help developers create specific types of applications or software. Software-In-the-Loop (SIL) is a technology used for embedded system development and testing. It is used to simulate and verify the behavior and performance of embedded software before the actual hardware. Then, the test environment is built. The test of radar performance usually needs to be carried out in a darkroom, and necessary auxiliary tools such as a turntable, a corner reflector, and a radar simulator need to be assembled according to the test requirements. Next, the test data is collected. According to the test requirements, such as time domain analog-to-digital conversion (ADC) and / or fast Fourier transform (FFT) data, and serial port output records of commonly used test commands are collected. After that, the test data is analyzed, that is, based on the collected data, it is determined whether the basic commands and basic waveforms of the SDK are normal, and the collected ADC or FFT data is fed back to the SIL for consistency comparison. Finally, the test results are fed back. If all the test items pass, then the official version can be released; otherwise, the test results are fed back to the developers to deal with the problems caused by the failed test items.

[0074] In order to minimize external electromagnetic interference, ensure the accuracy and repeatability of the test, and realize the automated detection of echo waveforms in radar performance testing, it is usually carried out in a darkroom. The test target is a corner reflector or radar simulator fixed in front of the radar, such as 2-4 meters, to simulate the actual radar scene. The test radar board and the corresponding data acquisition board are connected to the host computer through communication interfaces such as serial ports and Ethernet to achieve control and data acquisition. These interfaces are used to send control commands to the radar board for specific tests and data acquisition. The host computer contains necessary compilation tools, data acquisition tools, etc. to ensure effective data processing and analysis. In this way, the host computer can send commands to control the radar board and collect radar echo data. After the test environment is set up, go to step 100.

[0075] Step 101: extracting waveform feature information from the echo signal after analog-to-digital conversion and / or the echo signal after fast Fourier transformation according to the obtained configuration file.

[0076] In an exemplary embodiment, in the case of extracting waveform feature information from the echo signal after analog-to-digital conversion, the extracted waveform feature information may include any one or any combination of the following:

[0077] The first waveform characteristic information is the difference between adjacent sampling points For stationary target echo data collected in a darkroom environment, a single chirp should theoretically be a smooth sine curve. When the data arrangement is abnormal, the time domain waveform of a single chirp is discontinuous. Therefore, by calculating the difference between adjacent sampling points The difference between adjacent sampling points can be The size of the chirp is used to determine whether there is an abnormality in the chirp arrangement.

[0078] The second waveform feature information is the z-score difference of the same sampling point between different chirps and coefficient of variation For stationary target echo data collected in a darkroom environment, the chirps of the same frame of data in the time division multiplexing (TDM) mode and the chirps of the same frame of data in the Doppler mode (DDM) mode after grouping by phase modulation period should basically overlap. The difference in z-score of the same sampling point between different chirps can be used to determine the chirp overlap. Is it large or the coefficient of variation Whether to judge whether there is an abnormality in the overlap between chirps.

[0079] The third waveform feature information includes one or any combination of the following: the mean value of each chirp Relative mean of each chirp The difference between the positive and negative swings of each chirp Relative positive and negative swing difference of each chirp Under normal circumstances, there should not be a significant difference between the positive and negative swings of ADC data. Relative mean of each chirp The difference between the positive and negative swings of each chirp and / or the relative positive and negative swing differences of each chirp Whether the DC bias of the frame data is abnormal can be determined by checking whether it is too large.

[0080] The fourth waveform characteristic information is the length of the adc flat top area For stationary target echo data collected in a darkroom environment, a single chirp should theoretically be a smooth sine curve. When saturation occurs, the peak area will be clipped. The length of the flat-top area in a single chirp can be calculated. and by the length of the flat-top region in a single chirp Whether the chirp is saturated can be determined by whether it is too large.

[0081] The fifth waveform characteristic information is the 0-filled position Under normal circumstances, the length of the effective data of the ADC data should be equal to the actual number of sampling points, and the value greater than the number of sampling points and the distance between the number of FFT points should be 0. This can be done by comparing the position where the value is 0 and the theoretical zero-filling position. Whether the zero-filling position of the current ADC data is abnormal can be determined by checking whether the zero-filling position of the current ADC data is abnormal.

[0082] In one embodiment, the ADC data may be obtained by directly collecting the echo signal, or may be obtained by inversely transforming one-dimensional fast Fourier transform (1D-FFT) data.

[0083] In an exemplary embodiment, in the case of extracting waveform feature information from an echo signal after fast Fourier transformation, the echo signal after fast Fourier transformation may include: an echo signal after 1D-FFT and / or an echo signal after two-dimensional fast Fourier transformation (2D-FFT).

[0084] In one embodiment, in the case of extracting waveform feature information from the echo signal after 1D-FFT, the extracted waveform feature information may include any one or any combination of the following:

[0085] The sixth waveform characteristic information is the difference between the distance corresponding to the distance dimension peak and the target distance After the ADC data passes through 1D-FFT, the target distance can be evaluated. The peak value of the 1D-FFT of each chirp can be detected. Whether the 1D-FFT is abnormal can be determined by whether the difference between the distance of the peak of the detected non-leakage area and the actual set target distance is too large.

[0086] The seventh waveform feature information is the maximum amplitude difference between chirps The amplitudes of stationary target echo data collected in a darkroom environment, the distance unit of the target in 1D-FFT data in TDM mode, and the distance unit of the target in 1D-FFT data in DDM mode after grouping by phase modulation period should be basically equal between chirps. Whether the amplitude difference between chirps is abnormal can be judged by calculating whether the maximum amplitude difference of the distance unit of the target between different chirps is too large.

[0087] The eighth waveform feature information is the maximum phase difference between chirps The phases of stationary target echo data collected in a darkroom environment, the distance unit of the target in 1D-FFT data in TDM mode, and the distance unit of the target in 1D-FFT data in DDM mode after grouping by phase modulation period should be basically equal between chirps. Whether the phase difference between chirps is abnormal can be judged by calculating whether the maximum phase difference of the distance unit of the target between different chirps is too large.

[0088] In one embodiment, the 1D-FFT data may be obtained by directly collecting the echo signal, or by performing a 1D-FFT transformation on the ADC data.

[0089] In one embodiment, in the case of extracting waveform feature information from an echo signal after 2D-FFT, the extracted waveform feature information may include any one or any combination of the following:

[0090] The ninth waveform characteristic information is the difference between the speed dimension peak value corresponding distance and the target speed The 1D-FFT data can be used to evaluate the target speed after 2D-FFT, and the peak value of the 2D-FFT of each chirp can be detected. Whether the 2D-FFT is abnormal can be determined by detecting whether the speed value corresponding to the speed unit where the peak value of the non-leakage area of ​​the 2D-FFT of each chirp is located and the difference between the actual set target speed is too large.

[0091] The tenth waveform feature information is the maximum difference in target amplitude between receiving channels Under normal circumstances, there should be no obvious difference in the target energy measured by different receiving channels. Whether there is obvious imbalance in the receiving channels can be determined by calculating whether the amplitude differences of the distance units and speed units where the targets of each receiving channel are located are too large.

[0092] The eleventh waveform characteristic information is the noise floor fluctuation range Under normal circumstances, the noise floor plane obtained by 2D-FFT should be relatively smooth. The noise floor of different areas of the 2D-FFT plane obtained by block estimation can be calculated to see whether the difference in the noise floor of different areas is too large to determine whether the flatness of the noise floor is abnormal.

[0093] The characteristic information of the twelfth waveform is the maximum difference in noise floor between receiving channels Under normal circumstances, there should be no significant difference in the target energy measured by different receiving channels. Whether there is a significant imbalance in the receiving channels can be determined by calculating whether the difference in the average noise of each receiving channel is too large.

[0094] The thirteenth waveform feature information is the SNR information of the target spectrum peak and other spectrum peaks except the target spectrum peak in the 2D-FFT plane. Under normal circumstances, the 2D-FFT plane should only have the target spectrum peak. The SNR information of the target spectrum peak and other spectrum peaks except the target spectrum peak can be calculated. Determine whether there are other larger spectral peaks besides the target spectral peak, so as to judge whether there is abnormal spurious in the current data.

[0095] In one embodiment, the 2D-FFT data may be obtained by directly collecting the echo signal, or by performing a 2D-FFT transformation on the ADC data or the 1D-FFT data.

[0096] Step 102: Determine the correctness of the waveform based on the extracted waveform feature information and the determination conditions in the configuration file.

[0097] In an exemplary embodiment, if any one of the following judgments or any combination thereof does not satisfy the judgment condition, the result of judging the correctness of the waveform is incorrect; if all the judgments satisfy the judgment condition, the result of judging the correctness of the waveform is correct:

[0098] If the extracted waveform features include the first waveform feature information, that is, the difference between adjacent sampling points The judgment conditions include: the difference between adjacent sampling points If the difference between adjacent sampling points is greater than the preset first threshold, the chirp arrangement is abnormal and does not meet the judgment condition. If it is not greater than the preset first threshold, the chirp arrangement is normal and meets the judgment condition.

[0099] If the extracted waveform features include the second waveform feature information, that is, the z-score difference of the same sampling point between different chirps and coefficient of variation The decision conditions include: the difference in z-score of the same sampling point between different chirps If the chirp overlap is greater than the preset second threshold, the chirp overlap is abnormal and does not meet the judgment condition; the z-score difference of the same sampling point between different chirps is not greater than the preset second threshold, then the overlap between chirps is normal and meets the judgment condition; and the coefficient of variation of the same sampling point between different chirps If the chirp overlap is greater than the preset third threshold, the chirp overlap is abnormal and does not meet the judgment condition; the coefficient of variation of the same sampling point between different chirps If it is not greater than the preset third threshold, the overlap between chirps is normal and the decision condition is met.

[0100] If the extracted waveform features include the third waveform feature information, that is, one or any combination of the following: the mean of each chirp Relative mean of each chirp The difference between the positive and negative swings of each chirp Relative positive and negative swing difference of each chirp The decision conditions include one or any combination of the following: 1) The mean of each chirp If the difference is greater than the preset fourth threshold, the DC bias of the frame data is abnormal and does not meet the judgment condition; the mean value of each chirp The difference between them is not greater than the preset fourth threshold, then the DC bias of the frame data is normal and meets the judgment condition; 2) The relative mean of each chirp If the difference is greater than the preset fifth threshold, the DC bias of the frame data is abnormal and does not meet the judgment condition; the relative mean of each chirp The difference between the positive and negative swing amplitudes of each chirp is not greater than the preset fifth threshold, then the DC bias of the frame data is normal and meets the judgment condition; 3) The difference between the positive and negative swing amplitudes of each chirp is If the difference is greater than the preset sixth threshold, the DC bias of the frame data is abnormal and does not meet the judgment condition; the difference between the positive and negative swings of each chirp The difference between the positive and negative swing amplitudes of each chirp is not greater than the preset sixth threshold, then the DC bias of the frame data is normal and meets the judgment condition; 4) The relative positive and negative swing amplitude differences of each chirp If the difference is greater than the preset seventh threshold, the DC bias of the frame data is abnormal and does not meet the judgment condition; the relative positive and negative swing differences of each chirp If the difference between them is not greater than the preset seventh threshold, the DC bias of the frame data is normal and meets the judgment condition.

[0101] If the extracted waveform features include the fourth waveform feature information, that is, the length of the adc flat top area The decision conditions include: the length of the flat-top area in a single chirp If the length of the flat-top area in a single chirp is greater than the preset eighth threshold, the chirp is saturated and does not meet the judgment condition. If the chirp is not greater than the preset eighth threshold, the chirp is not saturated and the decision condition is met.

[0102] If the extracted waveform features include the fifth waveform feature information, i.e., the zero-filled position feature The judgment conditions include: the position where the value is 0 in the ADC valid data and the theoretical zero-filling position If the value of 0 in the ADC valid data is different from the theoretical zero-filling position, the current ADC data zero-filling position is abnormal and does not meet the judgment condition; If they are the same, the current ADC data zero padding position is normal and meets the judgment condition.

[0103] If the extracted waveform features include the sixth waveform feature information, that is, the difference between the distance corresponding to the distance of the distance dimension peak and the target distance The judgment conditions include: if the difference between the distance between the peak of the non-leakage area of ​​the 1D-FFT of each chirp and the actually set target distance is greater than the preset ninth threshold, the 1D-FFT is abnormal and does not meet the judgment conditions; if the difference between the distance between the peak of the non-leakage area of ​​the 1D-FFT of each chirp and the actually set target distance is not greater than the preset ninth threshold, the 1D-FFT is normal and meets the judgment conditions.

[0104] If the extracted waveform features include the seventh waveform feature information, that is, the maximum amplitude difference between chirps The judgment conditions include: if the maximum amplitude difference of the distance unit where the target is located between different chirps is greater than the preset tenth threshold, the amplitude difference between the chirps is abnormal and does not meet the judgment condition; if the maximum amplitude difference of the distance unit where the target is located between different chirps is not greater than the preset tenth threshold, the amplitude difference between the chirps is normal and meets the judgment condition.

[0105] If the extracted waveform features include the eighth waveform feature information, that is, the maximum phase difference between chirps The judgment conditions include: if the maximum phase difference of the distance unit where the target between different chirps is located is greater than the preset eleventh threshold, the phase difference between the chirps is abnormal and does not meet the judgment conditions; if the maximum phase difference of the distance unit where the target between different chirps is located is not greater than the preset eleventh threshold, the phase difference between the chirps is normal and meets the judgment conditions.

[0106] If the extracted waveform features include the ninth waveform feature information, that is, the difference between the speed dimension peak value corresponding to the distance and the target speed The judgment conditions include: if the difference between the speed value corresponding to the speed unit where the peak value of the non-leakage area of ​​the 2D-FFT of each chirp is located and the actually set target speed is greater than the preset twelfth threshold, then the 2D-FFT is abnormal and does not meet the judgment conditions; if the difference between the speed value corresponding to the speed unit where the peak value of the non-leakage area of ​​the 2D-FFT of each chirp is located and the actually set target speed is not greater than the preset twelfth threshold, then the 2D-FFT is normal and meets the judgment conditions.

[0107] If the extracted waveform features include the tenth waveform feature information, that is, the maximum difference in target amplitude between receiving channels The judgment conditions include: if the amplitude difference of the distance unit and the speed unit where the targets of each receiving channel are located is greater than the preset thirteenth threshold, then there is obvious imbalance in the receiving channels and the judgment condition is not met; if the amplitude difference of the distance unit and the speed unit where the targets of each receiving channel are located is not greater than the preset thirteenth threshold, then there is no obvious imbalance in the receiving channels and the judgment condition is met.

[0108] If the extracted waveform features include the eleventh waveform feature information, that is, the noise floor fluctuation range The judgment conditions include: if the difference in noise floors in different areas is greater than the preset fourteenth threshold, the noise floor flatness is abnormal and the judgment condition is not met; if the difference in noise floors in different areas is not greater than the preset fourteenth threshold, the noise floor flatness is normal and the judgment condition is met.

[0109] If the extracted waveform features include the twelfth waveform feature information, that is, the maximum difference in noise floor between receiving channels The judgment conditions include: if the average noise difference of each receiving channel is greater than the preset fifteenth threshold, then there is obvious receiving channel imbalance and the judgment condition is not met; if the average noise difference of each receiving channel is not greater than the preset fifteenth threshold, then there is no obvious receiving channel imbalance and the judgment condition is met.

[0110] In one embodiment, any of the above decision items does not satisfy the decision condition, and the decision result is incorrect. Step 103 further includes: recording the waveform data of the original echo signal and the decision result.

[0111] In an exemplary embodiment, the configuration file includes two or more different configuration files, and may further include:

[0112] In the case that different types of configuration files have not been traversed completely, another configuration file among the different types of configuration files is obtained and the process returns to continue executing step 101. For example, assuming that the configuration files include three types: configuration file 1, configuration file 2, and configuration file 3, after the judgment is completed according to configuration file 1, the process returns to step 101 to continue judging according to configuration file 2, and after the judgment is completed according to configuration file 2, the process returns to step 101 to continue judging according to configuration file 3.

[0113] The method for realizing radar performance detection provided in the embodiment of the present application, through automatic inspection of waveform data, extracts waveform feature information from the echo signal after analog-to-digital conversion and / or the echo signal after fast Fourier transformation, and observes more possible anomalies from the time domain and / or frequency domain compared to directly observing the detection results, making the detection of radar performance more comprehensive. When an abnormal situation occurs, it provides a guarantee for locating the root cause of the abnormality. Moreover, in the embodiment of the present application, data collection, function verification, and verification result generation are all completed automatically, and the detection criteria, i.e., the judgment conditions, can be quantified. Compared with the manual judgment method, it reduces resource investment, improves detection efficiency, and ensures that the detection results are more accurate.

[0114] The present application also provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to execute any of the above methods for implementing radar performance detection.

[0115] The present application further provides a device for implementing radar performance detection, including a memory and a processor, wherein the memory stores the following instructions that can be executed by the processor: used to execute the steps of any of the above-mentioned methods for implementing radar performance detection.

[0116] Figure 3 FIG. 1 is a schematic diagram of the structure of the device for realizing radar performance detection in an embodiment of the present application. Figure 3 As shown, it may include: a preprocessing module, a processing module, and an analysis module; wherein,

[0117] A pre-processing module, used for obtaining a configuration file for detecting radar performance;

[0118] A processing module, used for extracting waveform feature information from the echo signal after analog-to-digital conversion and / or the echo signal after fast Fourier transformation according to the obtained configuration file;

[0119] The analysis module is used to judge the correctness of the waveform according to the extracted waveform feature information and the judgment conditions in the configuration file.

[0120] In an exemplary embodiment, if the extracted waveform feature information includes one or more items, it is necessary to include one or more decision conditions correspondingly, thereby including one or more decisions; it can also include a recording module for recording the waveform data of the original echo signal and the result of the decision when any decision item does not meet the decision condition.

[0121] In an exemplary embodiment, the configuration files include two or more types; the analysis module is further used to: when the configuration files are not completely traversed, obtain the next configuration file in the configuration files and return it to the processing module.

[0122] The device for realizing radar performance detection provided in the embodiment of the present application automatically checks the waveform data, extracts waveform feature information from the echo signal after analog-to-digital conversion and / or the echo signal after fast Fourier transformation, and observes more possible anomalies from the time domain and / or frequency domain compared to directly observing the detection results, making the detection of radar performance more comprehensive. When an abnormal situation occurs, it provides a guarantee for locating the root cause of the abnormality. Moreover, in the embodiment of the present application, data collection, function verification, and verification result generation are all completed automatically, and the detection criteria, i.e., the judgment conditions, can be quantified. Compared with the manual judgment method, it reduces resource investment, improves detection efficiency, and ensures that the detection results are more accurate.

[0123] Although the embodiments disclosed in this application are as above, the contents described are only embodiments adopted to facilitate understanding of this application and are not intended to limit this application. Any technician in the field to which this application belongs can make any modifications and changes in the form and details of implementation without departing from the spirit and scope disclosed in this application, but the scope of patent protection of this application shall still be based on the scope defined in the attached claims.

Claims

1. A method for realizing radar performance detection, characterized in that: include: Obtain a configuration file for testing radar performance; Extracting waveform feature information from the echo signal after analog-to-digital conversion and / or the echo signal after fast Fourier transformation according to the obtained configuration file; The correctness of the waveform is judged based on the extracted waveform feature information and the judgment conditions in the configuration file.

2. The method according to claim 1, wherein the configuration file is one or more than one.

3. The method according to claim 1, when the result of the judgment is incorrect, further comprising: The waveform data of the original echo signal and the result of the judgment are recorded.

4. The method according to claim 3, when there are two or more configuration files; the method further comprises: In the case that the configuration files are not completely traversed, the next configuration file in the configuration files is obtained, and the step of extracting waveform feature information from the echo signal after analog-to-digital conversion and / or the echo signal after fast Fourier transformation according to the obtained configuration files is returned.

5. The method according to claim 1, 3 or 4, wherein: The waveform characteristic information includes one or more items; The correctness of the waveform is judged according to the extracted waveform feature information and the judgment condition in the configuration file, including: When there is a judgment result in any one or any combination of the judgments that does not meet the judgment condition, the result of the judgment on the correctness of the waveform is incorrect; when the judgment results of all the judgments meet the judgment condition, the result of the judgment on the correctness of the waveform is correct.

6. The method according to claim 1, 3 or 4, wherein: In the case of extracting waveform characteristic information from the echo signal after analog-to-digital conversion, the extracted waveform characteristic information includes any one or any combination of the following: The first waveform characteristic information: the difference between adjacent sampling points Second waveform feature information: z-score difference of the same sampling point between different chirps and coefficient of variation The third waveform characteristic information includes one or any combination of the following: the mean value of each chirp Relative mean of each chirp The difference between the positive and negative swings of each chirp Relative positive and negative swing difference of each chirp Fourth waveform characteristic information: adc flat top area length Fifth waveform characteristic information: fill in 0 position 7. The method according to claim 6, wherein: The extracted waveform features include the first waveform feature information; The decision conditions include: the difference between the adjacent sampling points is greater than the first threshold, then the chirp arrangement is abnormal and does not meet the judgment condition; the difference between adjacent sampling points is not greater than the first threshold, the chirp arrangement is normal and the decision condition is met; The extracted waveform features include the second waveform feature information; the decision conditions include: the z-score difference of the same sampling point between the different chirps is greater than the second threshold, then the chirp overlap is abnormal and does not meet the judgment condition; the z-score difference of the same sampling point between different chirps is not greater than the second threshold, then the overlap between the chirps is normal and satisfies the judgment condition; and the coefficient of variation of the same sampling point between the different chirps is greater than the third threshold, then the overlap between the chirps is abnormal and does not meet the judgment condition; the coefficient of variation of the same sampling point between the different chirps is not greater than the third threshold, then the overlap between chirps is normal and the judgment condition is met; The extracted waveform features include the third waveform feature information; the decision conditions include one or any combination of the following: the mean of each chirp If the difference is greater than the fourth threshold, the DC bias of the frame data is abnormal and does not meet the judgment condition. The difference between the chirp values ​​is not greater than the fourth threshold, then the DC bias of the frame data is normal and meets the judgment condition; If the difference is greater than the fifth threshold, the DC bias of the frame data is abnormal and does not meet the judgment condition. The difference between the positive and negative swing amplitudes of each chirp is not greater than the fifth threshold, then the DC bias of the frame data is normal and meets the judgment condition; If the difference is greater than the sixth threshold, the DC bias of the frame data is abnormal and does not meet the judgment condition. The difference between the positive and negative swing amplitudes of the chirps is not greater than the sixth threshold, then the DC bias of the frame data is normal and meets the judgment condition; If the difference is greater than the seventh threshold, the DC bias of the frame data is abnormal and does not meet the judgment condition. The difference between them is not greater than the seventh threshold, then the DC bias of the frame data is normal and meets the judgment condition; The extracted waveform features include the fourth waveform feature information; the decision conditions include: the length of the flat-top area in the single chirp is greater than the eighth threshold, whether the chirp is saturated or not, and does not meet the judgment condition; the length of the flat-top area in the single chirp is not greater than the eighth threshold, the chirp is not saturated and the decision condition is met; The extracted waveform features include the fifth waveform feature information; the decision conditions include: the position where the value 0 appears in the valid data of the analog-to-digital conversion and the theoretical zero-filling position If the value of the current analog-to-digital conversion data is different, the position of the zero padding of the current analog-to-digital conversion data is abnormal and does not meet the judgment condition; the position where the value of 0 appears in the valid data of the analog-to-digital conversion and the theoretical zero padding position If they are the same, the position of the zero padding of the current analog-to-digital conversion data is normal, and the judgment condition is met.

8. The method according to claim 6, wherein: The analog-to-digital converted data is obtained by directly collecting the echo signal, or by inversely transforming one-dimensional fast Fourier transform (1D-FFT) data.

9. The method according to claim 1, 3 or 4, wherein: As for the case of extracting waveform feature information from the echo signal after fast Fourier transformation, the echo signal after fast Fourier transformation includes: an echo signal after 1D-FFT and / or an echo signal after two-dimensional fast Fourier transformation 2D-FFT.

10. The method according to claim 9, wherein: For the case of extracting waveform feature information from the echo signal after 1D-FFT, the extracted waveform feature information includes any one or any combination of the following: The sixth waveform characteristic information: the difference between the distance corresponding to the peak value of the distance dimension and the target distance Seventh waveform feature information: maximum amplitude difference between chirps Eighth waveform characteristic information: maximum phase difference between chirps 11. The method according to claim 10, wherein: The extracted waveform features include the sixth waveform feature information; The judgment condition includes: if the difference between the distance between the peak of the non-leakage area of ​​the 1D-FFT of each chirp and the actually set target distance is greater than the ninth threshold, the 1D-FFT is abnormal and does not meet the judgment condition; if the difference between the distance between the peak of the non-leakage area of ​​the 1D-FFT of each chirp and the actually set target distance is not greater than the ninth threshold, the 1D-FFT is normal and meets the judgment condition; The extracted waveform features include the seventh waveform feature information; the judgment condition includes: if the maximum amplitude difference of the distance unit where the target is located between different chirps is greater than the tenth threshold, then the amplitude difference between the chirps is abnormal and does not meet the judgment condition; if the maximum amplitude difference of the distance unit where the target is located between different chirps is not greater than the tenth threshold, then the amplitude difference between the chirps is normal and meets the judgment condition; The extracted waveform features include the eighth waveform feature information; the judgment condition includes: if the maximum phase difference of the distance units where the targets between different chirps are located is greater than the eleventh threshold, then the phase difference between the chirps is abnormal and does not meet the judgment condition; if the maximum phase difference of the distance units where the targets between different chirps are located is not greater than the preset eleventh threshold, then the phase difference between the chirps is normal and meets the judgment condition.

12. The method according to claim 10, wherein: The 1D-FFT data is obtained by directly collecting the echo signal, or by performing 1D-FFT transformation on the analog-to-digital converted data.

13. The method according to claim 9, wherein: For the case of extracting waveform feature information from the echo signal after 2D-FFT, the extracted waveform feature information includes any one or any combination of the following: Ninth waveform characteristic information: the difference between the speed dimension peak value corresponding to the distance and the target speed Tenth waveform feature information: Maximum difference in target amplitude between receiving channels Eleventh waveform characteristic information: noise floor fluctuation range The twelfth waveform characteristic information: the maximum difference in noise floor between receiving channels Thirteenth waveform characteristic information: SNR information of the target spectrum peak and other spectrum peaks except the target spectrum peak in the 2D-FFT plane 14. The method according to claim 13, wherein: The extracted waveform features include the ninth waveform feature information; The judgment condition includes: if the speed value corresponding to the speed unit where the peak value of the non-leakage area of ​​the 2D-FFT of each chirp is located and the difference between the actually set target speed is greater than the twelfth threshold, then the 2D-FFT is abnormal and does not meet the judgment condition; if the speed value corresponding to the speed unit where the peak value of the non-leakage area of ​​the 2D-FFT of each chirp is located and the difference between the actually set target speed is not greater than the preset twelfth threshold, then the 2D-FFT is normal and meets the judgment condition; The extracted waveform features include the tenth waveform feature information; the judgment condition includes: if the amplitude difference of the distance unit and the speed unit where the target of each receiving channel is located is greater than the thirteenth threshold, then there is obvious receiving channel imbalance, and the judgment condition is not met; if the amplitude difference of the distance unit and the speed unit where the target of each receiving channel is located is not greater than the preset thirteenth threshold, then there is no obvious receiving channel imbalance, and the judgment condition is met; The extracted waveform features include the eleventh waveform feature information; the judgment condition includes: if the difference of the noise floors in different areas is greater than the fourteenth threshold, the noise floor flatness is abnormal and the judgment condition is not met; if the difference of the noise floors in different areas is not greater than the fourteenth threshold, the noise floor flatness is normal and the judgment condition is met; The extracted waveform features include the twelfth waveform feature information; the judgment condition includes: if the average noise difference of each of the receiving channels is greater than the fifteenth threshold, then there is obvious receiving channel imbalance and the judgment condition is not met; if the average noise difference of each of the receiving channels is not greater than the fifteenth threshold, then there is no obvious receiving channel imbalance and the judgment condition is met.

15. The method according to claim 13, wherein: The 2D-FFT data is obtained by directly collecting the echo signal, or by performing 2D-FFT transformation on the analog-to-digital converted data or the 1D-FFT data.

16. A computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to execute the method for realizing radar performance detection according to any one of claims 1 to 15.

17. A device for implementing radar performance detection, comprising a memory and a processor, wherein: The memory stores the following instructions executable by the processor: used to execute the steps of the method for implementing radar performance detection as described in any one of claims 1 to claim 15.

18. A device for realizing radar performance detection, characterized in that: include: Preprocessing module, processing module, analysis module; among them, A pre-processing module, used for obtaining a configuration file for detecting radar performance; A processing module, used for extracting waveform feature information from the echo signal after analog-to-digital conversion and / or the echo signal after fast Fourier transformation according to the obtained configuration file; The analysis module is used to judge the correctness of the waveform according to the extracted waveform feature information and the judgment conditions in the configuration file.

19. The device according to claim 18, wherein the extracted waveform feature information is one or more than one item; and further comprises a recording module for recording the original waveform data of the echo signal and the result of the judgment when any of the judgment items does not meet the judgment condition.

20. The device according to claim 18 or 19, wherein the configuration files include two or more types; the analysis module is further used to: when the configuration files have not been traversed completely, obtain the next configuration file in the configuration files and return it to the processing module.

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