Vehicle-mounted radar signal processing method, device, equipment and medium

Abstract

The embodiment of the invention discloses a vehicle-mounted radar signal processing method and device, equipment and a medium. The method comprises the following steps: acquiring a vehicle-mounted millimeter wave radar signal, and determining a two-dimensional spectrum feature of the vehicle-mounted millimeter wave radar signal; determining a plurality of target superposition dissimilation spectrums based on the two-dimensional spectrum features, and determining an initial trace point according to the plurality of target superposition dissimilation spectrums; and filtering the initial trace point according to a set modulation spectrum feature and an angle spectrum feature to obtain a target trace point. According to the technical scheme, multi-target superposition dissimilatory spectrum and trace point filtering operation is performed by analyzing the spectrum characteristics, and output of false trace points is reduced while dislocation and swallowing problems are improved, so that the stability of the track and the system performance is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of vehicle-mounted radar technology, and in particular to a vehicle-mounted radar signal processing method, device, equipment and medium. Background Art

[0002] With the improvement of people's living standards and the development of science and technology, cars have become a means of transportation for everyone. The high-precision and long-distance measurement performance of radar systems has enabled vehicle-mounted radar systems to be applied and developed in many aspects.

[0003] The processing process of vehicle-mounted radar is mainly divided into two parts: signal processing process and point track data processing process. Among the many links of signal processing, spectrum leakage caused by signal truncation is an inherent problem of signal processing. Directly equating leakage with target output will not only cause target point redundancy, seriously increase system time consumption and be detrimental to the real-time performance of the system, but also introduce some false targets. These false targets cannot be eliminated by simple logic, which in turn affects the stability of subsequent tracks and deteriorates system performance. Therefore, clustering operations are required at the signal processing end.

[0004] However, in a multi-target environment, the spectra of different targets are accumulated together to cause spectrum distortion. At this time, the robustness of the traditional clustering algorithm deteriorates, and either the peak point deviates from the real point, or the target point is lost due to spectrum coupling, affecting system performance. Summary of the invention

[0005] The present invention provides a vehicle-mounted radar signal processing method, device, equipment and medium, which analyzes the spectrum characteristics to perform multiple target superposition alienation spectrum and point trace filtering operations, thereby improving the misalignment and annexation problems while reducing the output of false point traces to ensure the stability of the track and system performance.

[0006] According to one aspect of the present invention, a vehicle-mounted radar signal processing method is provided, comprising:

[0007] Acquire a vehicle-mounted millimeter-wave radar signal, and determine a two-dimensional spectrum feature of the vehicle-mounted millimeter-wave radar signal;

[0008] Determine multiple target superimposed alienation spectra based on the two-dimensional spectrum characteristics, and determine initial point traces according to the multiple target superimposed alienation spectra;

[0009] The initial trace is filtered according to the set modulation spectrum characteristics and angle spectrum characteristics to obtain the target trace.

[0010] According to another aspect of the present invention, there is provided a vehicle-mounted radar signal processing device, comprising:

[0011] A spectrum feature determination module, used to obtain a vehicle-mounted millimeter-wave radar signal and determine a two-dimensional spectrum feature of the vehicle-mounted millimeter-wave radar signal;

[0012] An initial point trace determination module, used to determine multiple target superimposed alienation spectra based on the two-dimensional spectrum characteristics, and determine the initial point trace according to the multiple target superimposed alienation spectra;

[0013] The point trace filtering module is used to filter the initial point trace according to the set modulation spectrum characteristics and angle spectrum characteristics to obtain the target point trace.

[0014] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0015] at least one processor; and

[0016] a memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the vehicle-mounted radar signal processing method described in any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the vehicle-mounted radar signal processing method described in any embodiment of the present invention when executed.

[0019] The technical solution of the embodiment of the present invention determines the two-dimensional spectrum characteristics of the vehicle-mounted millimeter-wave radar signal by acquiring the vehicle-mounted millimeter-wave radar signal; determines multiple target superimposed alienation spectra based on the two-dimensional spectrum characteristics, and determines the initial point trace according to the multiple target superimposed alienation spectra; and filters the initial point trace according to the set modulation spectrum characteristics and angle spectrum characteristics to obtain the target point trace. This technical solution analyzes the spectrum characteristics to perform multiple target superimposed alienation spectra and point trace filtering operations, while improving the misalignment and annexation problems and reducing the output of false point traces to ensure the stability of the track and system performance.

[0020] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 is a flow chart of a vehicle-mounted radar signal processing method provided according to Embodiment 1 of the present invention;

[0023] Figure 2 is a flow chart of a vehicle-mounted radar signal processing method provided according to Embodiment 2 of the present invention;

[0024] Figure 3 This is an example diagram of amplitude statistics provided according to the second embodiment of the present invention;

[0025] Figure 4 is a structural schematic diagram of a vehicle-mounted radar signal processing device provided according to Embodiment 3 of the present invention;

[0026] Figure 5 It is a schematic diagram of the structure of an electronic device provided according to Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first", "second", "initial" and "target" in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] Embodiment 1

[0030] Figure 1 This is a flow chart of a vehicle-mounted radar signal processing method provided according to the first embodiment of the present invention. This embodiment is applicable to the case of processing vehicle-mounted radar signals. The method can be executed by a vehicle-mounted radar signal processing device. The vehicle-mounted radar signal processing device can be implemented in the form of hardware and / or software. The vehicle-mounted radar signal processing device can be configured in an electronic device with data processing capabilities. Figure 1 As shown, the method includes:

[0031] S110: Acquire a vehicle-mounted millimeter-wave radar signal, and determine a two-dimensional spectrum feature of the vehicle-mounted millimeter-wave radar signal.

[0032] Among them, the vehicle-mounted millimeter-wave radar signal can be understood as a time domain sampling signal obtained by sampling the vehicle-mounted millimeter-wave radar within a specified time. The two-dimensional spectrum feature can be a spectrum feature obtained by performing a two-dimensional Fourier transform on the time domain signal of the vehicle-mounted millimeter-wave radar. The two-dimensional spectrum feature in this embodiment can be a spectrum feature including a speed dimension and a distance dimension.

[0033] In this embodiment, a time domain signal obtained by sampling the vehicle-mounted millimeter-wave radar within a specified time period can be obtained, and the time domain signal is subjected to a two-dimensional Fourier transform to obtain a corresponding two-dimensional spectrum feature.

[0034] S120, determining multiple target superimposed alienation spectra based on the two-dimensional spectrum characteristics, and determining initial point traces according to the multiple target superimposed alienation spectra.

[0035] Among them, the multiple target superposition alienation spectrum can be understood as the spectrum of spectrum alienation caused by the superposition of the spectrum corresponding to multiple targets in the multi-target scene. The initial point trace can be understood as the initial point trace corresponding to each target object obtained by marking the multiple target superposition alienation spectra and then merging and releasing the points with the mark as a reference. In this embodiment, the initial point trace can include the point traces corresponding to different target objects identified by the radar.

[0036] In this embodiment, the specific method of determining multiple target superimposed alienation spectra based on two-dimensional spectrum features can be to perform threshold detection based on two-dimensional spectrum features to determine the cross clustering range and perform two-dimensional spectrum feature analysis, and determine multiple target superimposed alienation spectra through the size and number of spectrum amplitudes in the spectrum features. In this embodiment, the specific method of determining the initial point trace based on multiple target superimposed alienation spectra can be to mark the determined multiple target superimposed alienation spectra, and perform a release operation on the point trace swallowing point of the signal recognition object according to the mark as a reference, so as to obtain the initial point trace of the signal recognition object. In this embodiment, multiple amplitudes can be determined by analysis based on two-dimensional spectrum features, which can also be understood as multiple peaks.

[0037] In this embodiment, spectrum leakage after spectrum accumulation in multi-target scenarios will cause spectrum alienation. This phenomenon will not only cause false point output, but also cause peak point weakening and multi-target swallowing, resulting in intermittent frames or even continuous frames where target corresponding point traces are lost, affecting track stability. Therefore, in this embodiment, two-dimensional spectrum feature analysis can be added on the basis of cross clustering through two-dimensional spectrum features, and the single-target peak spectrum and the multi-target amplitude superposition alienated spectrum are distinguished through spectrum features, and the alienated spectrum units are marked, and the swallowed points are released with the mark as a reference, so as to determine the initial point traces of signal recognition objects in multiple target scenarios.

[0038] S130 , filtering the initial traces according to the set modulation spectrum characteristics and angle spectrum characteristics to obtain target traces.

[0039] Among them, the set modulation spectrum feature can be a pre-set modulation spectrum feature. The setting and adjustment spectrum feature in this embodiment can be set according to the actual needs of the vehicle-mounted millimeter-wave radar. Exemplarily, the setting and adjustment spectrum feature of this embodiment can be a spectrum feature generated by MIMO modulation. Among them, MIMO (Multiple-Input Multiple-Output) refers to the use of multiple antennas to simultaneously send and receive multiple data streams on the same channel to improve the data transmission rate. In this embodiment, different spectrum features can be generated according to MIMO modulation according to different vehicle-mounted millimeter-wave radars. For example, three transmitting antennas can be set and empty bands can be set, and the initial point traces can be filtered for false points according to the three large and one small MIMO amplitude characteristics. The angular spectrum feature can be understood as the angular spectrum feature obtained by performing three-dimensional feature extraction on the initial point trace. The target point trace can be understood as the point trace obtained after filtering out the false point traces in the initial point trace.

[0040] In this embodiment, the specific method for filtering the initial point trace according to the set modulation spectrum characteristics and angle spectrum characteristics is to first perform a first false point trace filtering on the initial point trace according to the set modulation spectrum characteristics to obtain a first point trace, and then perform three-dimensional feature extraction on the first point trace obtained after filtering to obtain an angle spectrum feature, and perform a second false point trace filtering on the first point trace based on the angle spectrum feature to obtain the target point trace.

[0041] In this embodiment, optionally, the initial point trace is filtered according to the set modulation spectrum characteristics and angle spectrum characteristics to obtain the target point trace, including: determining the false point traces in the initial point trace according to the set modulation spectrum characteristics, eliminating the false point traces to obtain the first point trace; determining the angle spectrum characteristics of the first point trace; and determining the target point trace based on the angle spectrum characteristics and the angle threshold.

[0042] Among them, the false point trace can be obtained by identifying the initial point trace according to the set adjustment spectrum feature. The first point trace can be a point trace obtained by eliminating the false point traces in the initial point trace. The angle spectrum feature can be a spectrum feature obtained by performing three-dimensional feature extraction on the first point trace. The angle threshold can be pre-set. In this embodiment, the angle threshold can be set according to actual needs.

[0043] In this embodiment, the corresponding frequency division position can be found on the two-dimensional spectrum based on the set frequency division characteristics of MIMO in the set modulation spectrum characteristics, and false points can be identified based on the frequency division position and its corresponding amplitude characteristics, and the identified false points can be eliminated to obtain the first point after eliminating the false points. In this embodiment, the specific method of determining the angular spectrum characteristics of the first point can be to extract the three-dimensional characteristics of the first point through the angle dimension Fourier transform to obtain the angular spectrum characteristics of the first point. In this embodiment, the false points can be further filtered according to the angular spectrum characteristics of the first point and a pre-set angle threshold.

[0044] Specifically, in this embodiment, based on the angle spectrum feature PEAKAMP Angularspectrum The specific method of determining the target point trace by using the angle threshold T3 can be:

[0045]

[0046] In this embodiment, the angle spectrum feature and the angle threshold are compared. If the angle spectrum feature of the point trace in the first point trace is greater than the angle threshold, the point trace is considered to be the target point trace; if the angle spectrum feature of the point trace in the first point trace is less than or equal to the angle threshold, the point trace is considered to be a false point trace.

[0047] In this embodiment, the point traces determined by two-dimensional cross clustering and two-dimensional spectrum feature analysis are set to perform preliminary filtering of false points to obtain the first point trace, and then the first point trace is subjected to three-dimensional or four-dimensional detection to increase the signal differentiation dimension and provide more information to the target recognition link. Therefore, this embodiment only performs three-dimensional feature extraction through angle-dimensional Fourier transform after two-dimensional detection to obtain angle spectrum features. In this embodiment, the three-dimensional or four-dimensional spectrum features can also be used as feature basis for filtering false point traces, and further filter the false point traces. Therefore, this embodiment performs three-dimensional or even four-dimensional spectrum analysis on the basis of two-dimensional spectrum analysis, and judges the angle spectrum features and the angle threshold set according to the empirical value, so as to further filter the false points. At the same time, the angle spectrum corresponding to the point trace greater than the angle threshold is identified by angle resolution target spectrum, so as to output the angle resolution target, so as to solve the problem of loss of points at the same distance and speed caused by the two target spectrum information falling in the same RV unit.

[0048] Through such a setting in the present embodiment, after preliminary filtering of false points in all outputs based on the MIMO characteristics of the radar system, angle dimension detection is performed, and further false point filtering is performed based on the angle dimension spectrum, thereby improving the misalignment and annexation problems while reducing the output of false points and ensuring the stability of the track and system.

[0049] In this embodiment, optionally, false points in the initial points are determined according to set modulation spectrum characteristics, and the false points are eliminated to obtain a first point, including: determining the frequency division characteristics and amplitude characteristics of the set modulation spectrum characteristics; determining the false points in the initial points according to the frequency division characteristics and the amplitude characteristics, and eliminating the false points to obtain the first point.

[0050] The frequency division feature may be a feature of multiple frequency bands set according to MIMO technology. In this embodiment, multiple antennas may be used to transmit and receive signals simultaneously on multiple frequency bands according to MIMO technology. The amplitude feature may be understood as the MIMO amplitude on multiple frequency bands set to set the modulation spectrum feature.

[0051] In this embodiment, the corresponding frequency division position can be found on the two-dimensional spectrum based on the set frequency division characteristics of MIMO in the set modulation spectrum characteristics, and the corresponding amplitude characteristics at each frequency division position are judged to determine the false point trace. Exemplarily, in this embodiment, MIMO can be set to use three transmitting antennas and be provided with empty bands. The point trace of each signal identification object will have three frequency division points, and one of the four-frequency division settings will be an empty band; if the amplitude corresponding to the three frequency division points is greater than the amplitude corresponding to the empty band, the point trace of the signal identification object can be considered to be a real point trace, and if the amplitude corresponding to the three frequency division points is less than or equal to the amplitude corresponding to the empty band, then the point trace of the signal identification object is considered to be a false point trace, and the determined false point traces are eliminated, and the point traces of each signal identification object after elimination, that is, the first point trace, can be obtained.

[0052] In this embodiment, since it is impossible to guarantee that the signal identification object is 100% the target even if multiple swallowing points are released based on the determination of multiple target superposition alienation spectra, after clustering and two-dimensional analysis processing, false points are eliminated by setting the spectrum characteristics generated by MIMO modulation, thereby increasing the point track verification features and reducing the impact of false point output on track stability.

[0053] The technical solution of the embodiment of the present invention determines the two-dimensional spectrum characteristics of the vehicle-mounted millimeter-wave radar signal by acquiring the vehicle-mounted millimeter-wave radar signal; determines multiple target superimposed alienation spectra based on the two-dimensional spectrum characteristics, and determines the initial point trace according to the multiple target superimposed alienation spectra; and filters the initial point trace according to the set modulation spectrum characteristics and angle spectrum characteristics to obtain the target point trace. This technical solution performs multiple target superimposed alienation spectra and point trace filtering operations by analyzing the spectrum characteristics, thereby reducing the output of false point traces while improving the misalignment and annexation problems, so as to ensure the stability of the track and system performance.

[0054] Embodiment 2

[0055] Figure 2 This is a flow chart of a vehicle-mounted radar signal processing method provided according to the second embodiment of the present invention. This embodiment is optimized based on the above embodiment. The specific optimization is: based on the two-dimensional spectrum characteristics, multiple target superimposed alienation spectra are determined, and the initial point trace is determined according to the target superimposed alienation spectrum, including: determining the minimum clustering range based on the spectrum characteristics; within the minimum clustering range, multiple target superimposed alienation spectra are determined according to the size and number of spectrum amplitudes; multiple target superimposed alienation spectra are marked, and the mark is used as a reference to release the point trace annexation point to obtain the initial point trace. Figure 2 As shown, the method includes:

[0056] S210: Acquire a vehicle-mounted millimeter-wave radar signal, and determine a two-dimensional spectrum feature of the vehicle-mounted millimeter-wave radar signal.

[0057] S220: Determine a minimum clustering range based on the frequency spectrum feature.

[0058] Among them, the minimum clustering range can be a cross clustering of the detection points obtained by judging the signal-to-noise ratio threshold set based on the spectrum characteristics, and the range determined by the cross clustering. In this embodiment, according to Formula 1, since the Fourier transform of the rectangular window (as shown in Formula 2) is a sinc function (as shown in Formula 3), the signal spectrum leakage caused by the truncation of its two-dimensional spectrum characteristics in the time and space domains is manifested as a cross. Since the cross is the smallest unit of leakage, in this embodiment, each cluster Kang can be obtained by clustering in a cross shape according to the spectrum characteristics to obtain each minimum clustering range.

[0059]

[0060] In this embodiment, optionally, determining the minimum clustering range based on the spectrum characteristics includes: determining the detection point traces based on the spectrum characteristics and the signal-to-noise ratio threshold; and performing cross clustering on the detection point traces to obtain the minimum clustering range.

[0061] The signal-to-noise ratio threshold can be set according to actual needs. The signal-to-noise ratio threshold in this embodiment can be set according to the noise floor of the radar. Different radars usually have different determination methods, which are not limited in this embodiment. The detected traces can be understood as trace objects obtained by detecting each signal identification object based on the spectrum characteristics and the signal-to-noise ratio threshold.

[0062] In this embodiment, according to the two-dimensional spectrum characteristics corresponding to the vehicle-mounted radar signal and the preset signal-to-noise ratio threshold, the two-dimensional spectrum characteristics 2dfft, that is, the spectrum characteristics of the distance-speed dimension, are subjected to the signal-to-noise ratio threshold detection. If the two-dimensional spectrum characteristics of each signal identification object in the radar signal are greater than the set signal-to-noise ratio threshold, the signal identification object is considered to be a point object that needs further judgment; if the two-dimensional spectrum characteristics are less than or equal to the set signal-to-noise ratio threshold, the signal identification object is considered to be noise, and no judgment is required. In this embodiment, the detected point traces are clustered as the cross cluster center points to obtain the minimum cluster range.

[0063] In this embodiment, through such a setting, the minimum clustering unit can be selected according to the physical characteristics, thereby reducing the possibility of merging multiple target objects.

[0064] S230: Determine multiple target superimposed alienation spectra within the minimum clustering range according to the size and number of spectrum amplitudes.

[0065] The spectrum amplitude may be amplitude data obtained by performing two-dimensional spectrum analysis on each signal object. In this embodiment, determining the superposition of multiple target alienation spectra based on the size and number of spectrum amplitudes may be performed by checking whether the corresponding amplitude of each unit in the minimum clustering range is a peak value, and then considering whether the energy of these four units is superimposed by multiple other targets, so that marking can be performed.

[0066] In this embodiment, due to the two-dimensional Fourier transform, a cross-shaped cluster is selected to determine each minimum cluster range. Within the minimum cluster range, the amplitude of the two-dimensional spectrum of each signal recognition object itself will be counted in the cross frame to compare the magnitude relationship of each spectrum amplitude, determine the initial abnormal point according to the amplitude of the magnitude relationship, and then count the number of set amplitudes around the initial abnormal point, and finally determine multiple target superimposed alienation spectra. Among them, the set amplitude can be a point greater than the amplitude of the initial abnormal point.

[0067] In this embodiment, optionally, within the minimum clustering range, multiple target superimposed alienation spectra are determined according to the size and number of spectrum amplitudes, including: determining the maximum spectrum amplitude and the mean amplitude within each minimum clustering range; determining the initial amplitude outlier point according to the maximum spectrum amplitude and the mean amplitude; determining the number of amplitudes greater than the initial amplitude outlier point within a set periphery based on the initial amplitude outlier point; and determining multiple target superimposed alienation spectra according to the number of amplitudes and a quantity threshold.

[0068] Among them, the maximum spectrum amplitude can be the maximum spectrum amplitude within the minimum clustering range, that is, the cross clustering frame. The amplitude mean can be understood as the mean of each spectrum amplitude within the minimum clustering range, that is, the cross clustering frame. The initial assignment anomaly point can be an anomaly point determined by comparing the maximum spectrum amplitude and the amplitude mean with the set threshold. In this embodiment, the minimum clustering range, that is, the cross clustering frame, can be understood as the grid corresponding to each spectrum amplitude, and the set surrounding can be understood as the range of the upper grid, the lower grid and the left grid set with the initial amplitude anomaly point as a reference. The number of amplitudes can be understood as the number of amplitudes within the set surrounding determined by the initial amplitude anomaly point that is greater than the amplitude of the initial amplitude anomaly point.

[0069] In this embodiment, amplitude statistics can be performed within each minimum clustering range, that is, within the cross frame, by determining the difference between the maximum spectrum amplitude and the mean of the spectrum amplitude, and comparing the difference with a difference threshold to determine the initial amplitude outlier point, and then based on the number of values ​​greater than the amplitude of the initial outlier point within a set periphery where the initial amplitude outlier point is located, the number is compared with a number threshold to determine multiple target superimposed alienation spectra.

[0070] Specifically, the amplitude statistics example in this embodiment is shown in FIG. Figure 3 As shown, the setting range can be the dotted box range marked by number 2. The blank grid can be the detection point trace determined by the signal-to-noise ratio threshold, and the detection point trace is used as the center point for cross clustering. The point traces above, below, left, and right of the blank grid are all point traces greater than the signal-to-noise ratio threshold. The maximum amplitude value in the four grids above, below, left, and right is compared with the mean of the four grids above, below, left, and right to determine the initial amplitude abnormal point, and the initial amplitude abnormal point is marked as number 1. Then, when the peak detection is performed again, the initial amplitude abnormal point 1 will not be considered, and the amplitudes of the three grids above, below, and right will be considered whether they are all lower than the amplitude of the blank grid. If so, the blank grid will be output as the peak point.

[0071] In this embodiment, after determining that the initial abnormal point is digital mark 1, the judgment of formula 5 is performed: the amplitude judgment is performed within the setting surrounding of digital mark 1, that is, Figure 3The range of the dotted box marked with the number 2 is judged to determine whether the amplitude of each grid within the setting is greater than the amplitude of the initial abnormal point, and the number of grids within the setting whose amplitude is greater than the amplitude of the initial abnormal point is counted. large , the number of amplitudes NUM large Compare with the quantity threshold T2, if the amplitude number NUM large If it is greater than the quantity threshold T2, it is determined that the minimum clustering unit is a multi-target amplitude superposition spectrum; wherein the quantity threshold can be set according to actual needs. Exemplarily, the quantity threshold in this embodiment can be 3.

[0072]

[0073] In this embodiment, through such a setting, multiple target superimposed alienation spectra are determined according to the spectrum amplitude and quantity, so that the multi-target amplitude alienation spectrum is marked, and the confidence of the peak judgment is reduced to avoid the big eating the small.

[0074] In this embodiment, optionally, the initial amplitude outlier point is determined based on the maximum spectrum amplitude and the mean amplitude, including: determining the amplitude difference between the maximum spectrum amplitude and the mean amplitude; if the amplitude difference is greater than the amplitude threshold, determining the detected point trace corresponding to the maximum spectrum amplitude as the initial amplitude outlier point.

[0075] The amplitude difference may be the difference obtained by subtracting the maximum spectrum amplitude from the mean amplitude. In this embodiment, the amplitude statistics may be performed in each minimum clustering range, i.e., in the cross frame, according to formula 4 to determine the maximum spectrum amplitude AMP. max The average value of the sum and spectrum amplitude AMP mean , by determining the maximum spectrum amplitude AMP max and amplitude average AMP mean If the amplitude difference between the two values ​​is greater than the set amplitude threshold T1, the detected point trace corresponding to the maximum spectrum amplitude is determined as the initial amplitude abnormal point; if the amplitude difference is less than or equal to the set amplitude threshold T1, the detected point trace corresponding to the maximum spectrum amplitude is determined as a non-amplitude abnormal point. Wherein T1 is the amplitude threshold, which can be set according to actual needs. Exemplarily, it can be set to 2dB in this embodiment.

[0076]

[0077] In this embodiment, through such a setting, the amplitude outliers can be preliminarily detected in the smallest clustering unit according to the amplitude statistical mean and maximum value, and then multiple target superimposed alienation spectra can be determined based on the amplitude outliers, thereby improving the accuracy of determining the multiple target superimposed alienation spectra.

[0078] S240, marking the superimposed alienation spectra of multiple targets, and using the marks as references to release point traces and annex points to obtain initial point traces.

[0079] In this embodiment, the determined multiple target superimposed alienation spectra are marked. By identifying the multiple target superimposed alienation spectra, clustering is not performed using the multiple target superimposed alienation spectra as reference points. Other target point traces of the multiple target superimpositions are swallowed up and released, thereby obtaining the initial point traces.

[0080] S250 , filtering the initial traces according to the set modulation spectrum characteristics and angle spectrum characteristics to obtain target traces.

[0081] The technical solution of the embodiment of the present invention determines the two-dimensional spectrum characteristics of the vehicle-mounted millimeter-wave radar signal by acquiring the vehicle-mounted millimeter-wave radar signal; determines the minimum clustering range based on the spectrum characteristics; determines multiple target superimposed alienation spectra within the minimum clustering range according to the size and number of spectrum amplitudes; marks the multiple target superimposed alienation spectra, and releases the point trace swallowing point with the mark as a reference to obtain the initial point trace; filters the initial point trace according to the set modulation spectrum characteristics and angle spectrum characteristics to obtain the target point trace. This technical solution performs multiple target superimposed alienation spectra and point trace filtering operations by analyzing the spectrum characteristics, while improving the misalignment and swallowing problems and reducing the output of false point traces to ensure the stability of the track and system performance.

[0082] Embodiment 3

[0083] Figure 4 1 is a schematic diagram of the structure of a vehicle-mounted radar signal processing device provided according to Embodiment 3 of the present invention. Figure 4 As shown, the device comprises:

[0084] The spectrum feature determination module 410 is used to obtain the vehicle-mounted millimeter-wave radar signal and determine the two-dimensional spectrum feature of the vehicle-mounted millimeter-wave radar signal;

[0085] An initial point trace determination module 420 is used to determine multiple target superimposed alienation spectra based on the two-dimensional spectrum characteristics, and determine the initial point trace according to the multiple target superimposed alienation spectra;

[0086] The point trace filtering module 430 is used to filter the initial point trace according to the set modulation spectrum characteristics and angle spectrum characteristics to obtain the target point trace.

[0087] Optionally, the initial point trace determination module 420 includes:

[0088] A cluster range determination unit, used to determine a minimum cluster range based on a spectrum feature;

[0089] A multiple target superimposed alienation spectrum determination unit is used to determine multiple target superimposed alienation spectra within a minimum clustering range according to the size and number of spectrum amplitudes;

[0090] The marking unit is used to mark the superimposed alienation spectra of multiple targets, and to release the point traces and annex the points with the marks as references to obtain the initial point traces.

[0091] Optionally, the cluster range determination unit is specifically used to determine the detection point traces based on the spectrum characteristics and the signal-to-noise ratio threshold; and perform cross clustering on the detection point traces to obtain a minimum cluster range.

[0092] Optionally, multiple target superposition alienation spectrum determination units include:

[0093] An amplitude determination subunit, used for determining a maximum spectrum amplitude and an amplitude mean within each minimum clustering range;

[0094] An initial outlier point determination subunit, used to determine an initial amplitude outlier point according to the maximum amplitude value and the amplitude mean value of the spectrum;

[0095] An amplitude number determination subunit, used to determine the number of amplitudes greater than the initial amplitude abnormal point within a set periphery based on the initial amplitude abnormal point;

[0096] The multiple target superposition determination subunit is used to determine the multiple target superposition alienation spectrum according to the number of amplitudes and the quantity threshold.

[0097] Optionally, the initial outlier determination subunit is specifically used to determine the amplitude difference between the maximum spectrum amplitude and the mean amplitude; if the amplitude difference is greater than the amplitude threshold, the detected point trace corresponding to the maximum spectrum amplitude is determined as the initial amplitude outlier point.

[0098] Optionally, the point trace filtering module 430 includes:

[0099] A first point trace acquisition unit, used for determining false point traces in the initial point traces according to set modulation spectrum characteristics, removing the false point traces, and obtaining a first point trace;

[0100] An angle spectrum feature determination unit, used to determine the angle spectrum feature of the first point trace;

[0101] The target point trace determination unit is used to determine the target point trace based on the angle spectrum feature and the angle threshold.

[0102] Optionally, the first point trace acquisition unit is specifically used to determine the frequency division characteristics and amplitude characteristics of the set modulation spectrum characteristics; determine the false point traces in the initial point traces according to the frequency division characteristics and the amplitude characteristics, and eliminate the false point traces to obtain the first point trace.

[0103] A vehicle-mounted radar signal processing device provided in an embodiment of the present invention can execute a vehicle-mounted radar signal processing method provided in any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0104] Embodiment 4

[0105] Figure 5 1 is a schematic diagram of the structure of an electronic device provided according to Embodiment 4 of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0106] like Figure 5 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0107] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0108] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as a vehicle-mounted radar signal processing method.

[0109] In some embodiments, the vehicle-mounted radar signal processing method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the vehicle-mounted radar signal processing method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the vehicle-mounted radar signal processing method in any other appropriate manner (e.g., by means of firmware).

[0110] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0111] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0112] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0113] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0114] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0115] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0116] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0117] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A vehicle-mounted radar signal processing method, characterized in that: include: Acquire a vehicle-mounted millimeter-wave radar signal, and determine a two-dimensional spectrum feature of the vehicle-mounted millimeter-wave radar signal; Determine multiple target superimposed alienation spectra based on the two-dimensional spectrum characteristics, and determine initial point traces according to the multiple target superimposed alienation spectra; The initial trace is filtered according to the set modulation spectrum characteristics and angle spectrum characteristics to obtain the target trace.

2. The method according to claim 1, characterized in that Determining a plurality of target superimposed alienation spectra based on the two-dimensional spectrum characteristics, and determining an initial point trace according to the target superimposed alienation spectra, including: Determining a minimum clustering range based on the spectral characteristics; Within the minimum clustering range, multiple target superimposed alienation spectra are determined according to the magnitude and number of spectrum amplitudes; The multiple target superimposed alienation spectra are marked, and the mark is used as a reference to release the point trace and swallow up the point to obtain the initial point trace.

3. The method according to claim 2, characterized in that Determining a minimum clustering range based on the spectrum feature includes: Determine the detection point trace based on the frequency spectrum characteristics and the signal-to-noise ratio threshold; Cross clustering is performed with the detected points to obtain the minimum clustering range.

4. The method according to claim 2, characterized in that: Within the minimum clustering range, multiple target superimposed alienation spectra are determined according to the size and number of the spectrum amplitudes, including: Determine the maximum spectrum amplitude and the mean spectrum amplitude within each minimum clustering range; Determining an initial amplitude abnormal point according to the maximum spectrum amplitude and the amplitude mean; Determine the number of amplitudes greater than the initial amplitude abnormal point within a set periphery based on the initial amplitude abnormal point; The multiple target superimposed alienation spectra are determined according to the amplitude number and the quantity threshold.

5. The method according to claim 4, characterized in that Determining an initial amplitude abnormal point according to the spectrum amplitude maximum value and the amplitude mean value includes: Determine the amplitude difference between the maximum amplitude of the spectrum and the mean amplitude; If the amplitude difference is greater than the amplitude threshold, the detected point trace corresponding to the maximum spectrum amplitude is determined as the initial amplitude abnormal point.

6. The method according to claim 1, characterized in that The initial trace is filtered according to the set modulation spectrum characteristics and angle spectrum characteristics to obtain the target trace, including: Determine the false point traces in the initial point traces according to the set modulation spectrum characteristics, remove the false point traces, and obtain the first point trace; Determining the angular spectrum characteristics of the first point trace; The target point track is determined based on the angle spectrum characteristics and the angle threshold.

7. The method according to claim 6, characterized in that Determining false point traces in the initial point traces according to set modulation spectrum characteristics, removing the false point traces, and obtaining a first point trace, including: Determining the frequency division characteristics and amplitude characteristics of the set modulation spectrum characteristics; Determine the false point traces in the initial point traces according to the frequency division characteristics and the amplitude characteristics, remove the false point traces, and obtain the first point traces.

8. A vehicle-mounted radar signal processing device, characterized in that: include: A spectrum feature determination module, used to obtain a vehicle-mounted millimeter-wave radar signal and determine a two-dimensional spectrum feature of the vehicle-mounted millimeter-wave radar signal; An initial point trace determination module, used to determine multiple target superimposed alienation spectra based on the two-dimensional spectrum characteristics, and determine the initial point trace according to the multiple target superimposed alienation spectra; The point trace filtering module is used to filter the initial point trace according to the set modulation spectrum characteristics and angle spectrum characteristics to obtain the target point trace.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the vehicle-mounted radar signal processing method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the vehicle-mounted radar signal processing method according to any one of claims 1 to 7 when executed.

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