Radar echo noise reduction method, device and equipment and computer readable storage medium

Abstract

The invention provides a radar echo noise reduction method, device and equipment and a computer readable storage medium, and relates to the technical field of radar noise reduction, and the method comprises the steps: carrying out the subtraction of a radar echo signal and a fitting signal corresponding to the radar echo signal, and obtaining a radar difference signal; determining the Fourier spectrum and the S spectrum of the radar difference signal, and performing band-pass filtering on the S spectrum to obtain a filtered signal; wherein the center frequency of the band-pass filtering is the average value of the frequency spectrum peak frequency of the Fourier frequency spectrum and the S frequency spectrum; decomposing the filtering signal to obtain a modal component of the filtering signal; and according to the fuzzy entropy and the autocorrelation coefficient of each modal component, determining a plurality of target modal components from each modal component, and synthesizing each target modal component into a radar echo noise reduction signal. According to the technical scheme provided by the invention, the radar echo signal can be subjected to noise reduction.

Description

Technical Field

[0001] This application relates to the technical field of radar noise reduction, and in particular, to a method, device, equipment, and computer-readable storage medium for radar echo noise reduction. Background Art

[0002] In the ranging signal processing of radar products, direct Fourier transform analysis is usually used for echo signals, and the distance is calculated according to the "maximum power principle" by selecting the frequency corresponding to the energy peak spectral line in the frequency domain.

[0003] However, there are many noises that cause the instability of the echo signal spectrum in the actual working environment. For example, high-frequency random noise has the characteristics of strong randomness and no fixed pattern. If the noise cannot be reduced, it is easy to have the situation that the noise drowns the echo signal, resulting in missed detection and false alarms. Summary of the Invention

[0004] In view of this, the embodiments of this application provide a method, device, equipment, and computer-readable storage medium for radar echo noise reduction, which are used to reduce the noise of radar echo signals.

[0005] To achieve the above object, in a first aspect, the embodiments of this application provide a method for radar echo noise reduction, and the method includes:

[0006] Subtract the radar echo signal from the fitting signal corresponding to the radar echo signal to obtain a radar difference signal;

[0007] Determine the Fourier spectrum and the S spectrum of the radar difference signal, and perform band-pass filtering on the S spectrum to obtain a filtered signal; wherein, the center frequency of the band-pass filtering is the average value of the spectral peak frequencies of the Fourier spectrum and the S spectrum;

[0008] Decompose the filtered signal to obtain the modal components of the filtered signal;

[0009] Determine a plurality of target modal components from each of the modal components according to the fuzzy entropy and autocorrelation coefficient of each of the modal components, and synthesize each of the target modal components into a radar echo noise reduction signal.

[0010] In a possible implementation manner of the first aspect, the fitting signal is obtained by performing fitting calculation on the radar echo signal using the least square method.

[0011] In a possible implementation manner of the first aspect, the decomposing the filtered signal to obtain the modal components of the filtered signal includes:

[0012] Determine the envelope of the filtered signal, obtain the maximum values of the envelope and arrange them in descending order;

[0013] Determine the frequency point corresponding to the maximum value within the target range, take the midpoint of adjacent frequency points as the boundary points, and use the boundary points to divide the envelope into intervals;

[0014] For each interval, perform inverse Fourier transform on the Fourier spectrum corresponding to the interval using wavelet functions and scaling functions respectively to obtain the modal components of the filtered signal.

[0015] In a possible implementation manner of the first aspect, the fuzzy entropy of the target modal component is greater than the entropy threshold, and the autocorrelation coefficient of the target modal component is greater than the correlation coefficient threshold.

[0016] In a possible implementation manner of the first aspect, the passband range of the band-pass filtering is composed of the interval between the lowest frequency and the highest frequency, the lowest frequency is not less than 0.6F, the highest frequency is not greater than 1.4F, and F represents the center frequency of the band-pass filter.

[0017] In a possible implementation manner of the first aspect, the envelope is obtained by using polynomial fitting interpolation method, and the autocorrelation coefficient is the Pearson correlation coefficient.

[0018] In a possible implementation manner of the first aspect, before obtaining the fitting curve of the radar echo signal, the method further includes:

[0019] Collect the radar echo signal.

[0020] In the second aspect, an embodiment of the present application provides a radar echo noise reduction device, and the device includes:

[0021] A difference module, configured to subtract the radar echo signal from the fitting signal corresponding to the radar echo signal to obtain a radar difference signal;

[0022] A filtering module, configured to determine the Fourier spectrum of the radar difference signal and the S spectrum, perform band-pass filtering on the S spectrum to obtain a filtered signal; wherein, the center frequency of the band-pass filtering is the average value of the spectral peak frequencies of the Fourier spectrum and the S spectrum;

[0023] A decomposition module, configured to decompose the filtered signal to obtain the modal components of the filtered signal;

[0024] A synthesis module, configured to determine a plurality of target modal components from each of the modal components according to the fuzzy entropy and autocorrelation coefficient of each of the modal components, and synthesize each of the target modal components into a radar echo noise reduction signal.

[0025] As an optional implementation manner of the embodiment of the present application, the fitting signal is obtained by performing fitting calculation on the radar echo signal using the least squares method.

[0026] As an optional implementation manner of an embodiment of the present application, the decomposition module is specifically configured to:

[0027] Determine the envelope of the filtered signal, obtain the maximum values of the envelope and arrange them in descending order;

[0028] Determine the frequency points corresponding to the maximum values within the target range, take the midpoint of adjacent frequency points as boundary points, and use the boundary points to divide the envelope into intervals;

[0029] For each interval, respectively perform inverse transformation on the Fourier spectrum corresponding to the interval by using a wavelet function and a scaling function to obtain the modal components of the filtered signal.

[0030] As an optional implementation manner of an embodiment of the present application, the fuzzy entropy of the target modal component is greater than the entropy threshold, and the autocorrelation coefficient of the target modal component is greater than the correlation coefficient threshold.

[0031] As an optional implementation manner of an embodiment of the present application, the passband range of the band-pass filtering is constituted by an interval between the lowest frequency and the highest frequency, the lowest frequency is not less than 0.6F, the highest frequency is not greater than 1.4F, and F represents the center frequency of the band-pass filter.

[0032] As an optional implementation manner of an embodiment of the present application, the envelope is obtained by using a polynomial fitting interpolation method, and the autocorrelation coefficient is a Pearson correlation coefficient.

[0033] As an optional implementation manner of an embodiment of the present application, the radar echo noise reduction device further includes an acquisition module, and the acquisition module is used to acquire the radar echo signal before acquiring the fitting curve of the radar echo signal.

[0034] In a third aspect, an embodiment of the present application provides a radar echo noise reduction device, including: a memory and a processor, the memory is used to store a computer program; the processor is used to execute the method described in the first aspect or any implementation manner of the first aspect when calling the computer program.

[0035] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method described in the first aspect or any implementation manner of the first aspect is implemented.

[0036] In a fifth aspect, an embodiment of the present application further provides a computer program product, when the computer program product runs on a radar echo noise reduction device, the radar echo noise reduction device is caused to execute the method described in the first aspect or any implementation manner of the first aspect.

[0037] The radar echo noise reduction method, device, equipment, and computer-readable storage medium provided by the embodiments of the present application, wherein the method includes: subtracting the radar echo signal from the fitting signal corresponding to the radar echo signal to obtain a radar difference signal, determining the Fourier spectrum and S spectrum of the radar difference signal, performing band-pass filtering on the S spectrum to obtain a filtered signal, where the center frequency of the band-pass filtering is the average of the spectral peak frequencies of the Fourier spectrum and the S spectrum, decomposing the filtered signal to obtain the modal components of the filtered signal, determining multiple target modal components from the modal components according to the fuzzy entropy and autocorrelation coefficient of each modal component, and synthesizing the target modal components into a radar echo noise reduction signal, thereby realizing the noise reduction of the radar echo signal. Description of the Drawings

[0038] Figure 1 It is a schematic flowchart of a radar echo noise reduction method provided by the embodiments of the present application;

[0039] Figure 2 It is a schematic flowchart of obtaining the modal components of the filtered signal provided by the embodiments of the present application;

[0040] Figure 3 It is a waveform diagram of the radar echo signal of the noise reduction experiment provided by the embodiments of the present application;

[0041] Figure 4A It is a waveform schematic diagram of IMF1-IMF4 of the noise reduction experiment provided by the embodiments of the present application;

[0042] Figure 4B It is a waveform schematic diagram of IMF5-IMF8 of the noise reduction experiment provided by the embodiments of the present application;

[0043] Figure 5 It is a comparison diagram of the autocorrelation coefficient of IMF1-IMF10 and the correlation coefficient threshold of the noise reduction experiment provided by the embodiments of the present application;

[0044] Figure 6 It is a waveform diagram of the synthesized signal after adopting this solution in the noise reduction experiment provided by the embodiments of the present application;

[0045] Figure 7 It is a waveform diagram of the synthesized signal after adopting EMD in the noise reduction experiment provided by the embodiments of the present application;

[0046] Figure 8 It is a waveform diagram of the synthesized signal after adopting EWT in the noise reduction experiment provided by the embodiments of the present application;

[0047] Figure 9 It is a schematic structural diagram of the radar echo noise reduction device provided by the embodiments of the present application;

[0048] Figure 10Schematic diagram of the structure of the radar echo noise reduction device provided by the embodiment of the present application. Detailed implementation manners

[0049] The embodiments of the present application will be described below with reference to the accompanying drawings in the embodiments of the present application. The terms used in the implementation manners part of the embodiments of the present application are only used to explain the specific embodiments of the present application, rather than aiming to limit the present application. These several specific embodiments below can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments.

[0050] Radar echo signals have complex frequency domain characteristics, and the signals have the characteristics of transience and diversity. There are increasingly high requirements for the denoising of radar echo signals and time-frequency analysis capabilities. In this regard, those skilled in the art have proposed many methods for radar echo processing, including: aiming at the technical problem that there are still residual clutter dots in the radar echo signal, selecting features that reflect the differences between target dots and clutter dots, and using the elastic error backpropagation algorithm to train the model to reduce the impact of noise on radar dot recognition; using wavelet threshold denoising to process the intermediate frequency signal of microwave ranging radar, but this method is affected by the selection of wavelet basis functions; aiming at the problem that lidar is easily affected by noise interference and the ranging result has a large error, performing Kalman filtering on the peak-to-peak position difference of the fast Fourier transform and mapping it to the real spatial distance to suppress noise and reduce the ranging error; using the Wigner-Hough time-frequency analysis method to ensure that the intermediate frequency signal has a clearer main frequency characteristic, thereby suppressing the noise interference of cross distribution.

[0051] It can be seen from this that due to the application environment of radar products being easily affected by various noise factors, how to effectively extract (or generate) modal components containing characteristic information and suppress interference factors has become an urgent problem to be solved in this field.

[0052] In the prior art, the empirical wavelet transform (EWT) is usually used to generate modal components. EWT is a time-frequency analysis method for extracting significant modes of signals based on wavelet transform and empirical mode decomposition (EMD). Although EWT and EMD have certain advantages in processing non-linear signals, there are also problems such as mode mixing, selection of wavelet basis functions, over-segmentation or under-segmentation of spectrum segmentation.

[0053] In view of this, the embodiment of the present application provides a radar echo noise reduction method, which can reduce the noise of radar echo signals.

[0054] When specifically implementing the radar echo noise reduction method, it can be realized according to Figure 1 the method shown.Figure 1 The flowchart of the radar echo noise reduction method provided by the embodiment of this application is shown as Figure 1 follows. The method includes the following steps:

[0055] Step S110: Subtract the fitting signal corresponding to the radar echo signal from the radar echo signal to obtain a radar difference signal.

[0056] After obtaining the radar echo signal, the radar echo signal can be sampled to obtain the sampled radar echo signal. The sampling intensity can be 1024 or 2048, and the specific intensity can be selected according to actual needs. After completing the sampling, the radar echo signal can be subtracted from the fitting signal corresponding to the radar echo signal to obtain the radar difference signal. Exemplarily, the value of the radar difference signal can be obtained by subtracting the value of the fitting signal from the value of the radar echo signal.

[0057] In some embodiments, the least squares method can be used to perform fitting calculation on the radar echo signal to obtain the fitting signal, so as to smoothly connect each sampling point, smooth out the random fluctuations and noises in the radar echo signal, reduce clutter interference and DC bias interference, and improve the reliability and accuracy of the data.

[0058] Step S120: Determine the Fourier spectrum and S spectrum of the radar difference signal, and perform band-pass filtering on the S spectrum to obtain a filtered signal.

[0059] After obtaining the radar difference signal, the Fourier transform and S transform can be performed on the radar difference signal respectively to obtain the corresponding Fourier spectrum F(ω), ω∈[0, π] and S spectrum S(f, τ), and calculate the energy curve E of the S spectrum i S .

[0060]

[0061] where τ is the time parameter, f is the frequency parameter, and f i is the frequency of the i-th frequency point after S transform; S(f i , τ) is the S spectrum value corresponding to this frequency point.

[0062] After obtaining the energy curve, the S-spectrum wave frequency peak can be obtained according to the energy curve, and the S-spectrum can be band-pass filtered according to the S-spectrum wave frequency peak. Among them, the center frequency of the band-pass filter can be the average value of the Fourier spectrum and the spectrum peak frequency of the S-spectrum. In some embodiments, the band-pass range of the band-pass filter can be composed of an interval between the lowest frequency and the highest frequency. The lowest frequency can be not less than 0.6F, and the highest frequency can be not greater than 1.4F, where F represents the center frequency of the band-pass filter. Exemplarily, the average value of the Fourier spectrum and the spectrum peak frequency of the S-spectrum can be used as the center spectrum power F, and the frequencies corresponding to the power peaks that drop by 40% on both the left and right sides centered on the center spectrum power can be used as the cut-off frequencies. Through this filtering operation, it is convenient to extract the required characteristic information in the S-spectrum, effectively reduce noise, and improve the quality and clarity of the signal.

[0063] The Fourier transform can provide the overall spectral characteristics of the signal, while the S-transform can provide more refined local spectral characteristics. In the embodiments of the present application, by combining the Fourier spectrum and the S-spectrum, the advantages of the Fourier transform and the S-transform in frequency analysis and time analysis can be fully utilized, so that the frequency components of the radar echo signal and their changes over time can be obtained more comprehensively, thereby improving the noise reduction effectiveness and accuracy of the radar echo signal.

[0064] Step S130: Decompose the filtered signal to obtain the modal components of the filtered signal.

[0065] In some embodiments, step S130 may further include the following steps:

[0066] Step S131: Determine the envelope of the filtered signal, obtain the maximum values of the envelope and arrange them in descending order.

[0067] Exemplarily, the envelope can be obtained by polynomial fitting interpolation method, or by methods such as cubic spline interpolation method. In the embodiments of the present application, the envelope is obtained by polynomial fitting interpolation method, which can not only accurately fit the data points of the radar echo signal, but also reduce the computational complexity and improve the operation speed.

[0068] After obtaining the envelope of the filtered signal, all the maximum values of the envelope can be obtained through a peak detection algorithm, a local extreme search algorithm or an adaptive threshold method. The number of maximum values is denoted as m, where m is a positive integer, and the maximum value set can be expressed as {S1, S2,..., S m m}. Then, the maximum values can be arranged in descending order, that is, S1≥S2≥...≥S m m, where S1 represents the largest maximum value, and S m m represents the smallest maximum value.

[0069] Step S132: Determine the frequency point corresponding to the maximum value within the target range, take the midpoint of adjacent frequency points as the boundary points, and use the boundary points to divide the envelope into intervals.

[0070] ε is the relative amplitude ratio, ε ∈ (0, 1), and its range can be set according to actual needs. After a given ε value is determined, the maximum value within the range of S k ≤(S m +ε(S1 - S m )) & S k ≥(S m +ε(S1 - S m )) can be determined, and the frequency point corresponding to the maximum value is determined. Among them, S m +ε(S1 - S m ) is the reference value. Then, the midpoint of adjacent frequency points can be used as the boundary point ω n , n ≤ m, and the envelope is divided into intervals by using the boundary point ω n to obtain multiple frequency intervals.

[0071] EWT divides the spectrum based on the local maximum value of the Fourier spectrum, and this spectrum division is easily affected by noise. In the embodiments of the present application, by analyzing the characteristics and energy distribution of the radar echo signal from the frequency domain after the S transform, the S spectrum can be better divided using the frequency and energy of the signal, so as to solve the over-segmentation or under-segmentation problems caused by the spectrum division method based on local maximum values when the existing EWT faces signals with relatively complex frequency domain characteristics of radar signals, and reduce the influence of noise.

[0072] Step S133: For each interval, respectively use the wavelet function and the scaling function to perform inverse transformation on the Fourier spectrum corresponding to the interval to obtain the modal components of the filtered signal.

[0073] Exemplarily, after the interval division is completed, a wavelet function ψ n (ω) and a scaling function φ n (ω) can be constructed in each divided interval.

[0074]

[0075] Among them, the transition function β(x) = x 4 (35 - 84x + 70x 2 - 20x 3 ), γ represents the scale factor, which ensures that there is no overlap between adjacent transformations, and N is the length of the collected radar echo signal.

[0076] After decomposing the radar echo signal through wavelet functions and scaling functions, data decomposition can be performed to effectively retain the detailed features of the radar echo signal while removing high-frequency noise, facilitating the extraction of deep-level features of the data. Subsequently, the Fourier inverse transform can be used to calculate F(ω)×ψ n (ω), obtaining f0(t), and using the Fourier inverse transform to calculate to obtain f n (t), thereby obtaining the signal formula after reconstructing the time-domain signal as f(t) = f0(t) + ∑f n (t). By decomposing the radar echo signal through wavelet functions and scaling functions, the radar echo signal can be adaptively decomposed into modal components IMF i (i = 1, 2L, n) of multiple filtered signals distributed according to frequency and energy, hereinafter simply referred to as the IMF vector, which can both retain the main features of the data and suppress the interference of random noise.

[0077] Step S140: Determine multiple target modal components from each modal component according to the fuzzy entropy and autocorrelation coefficient of each modal component, and synthesize each target modal component into a radar echo noise reduction signal.

[0078] Considering that in radar ranging applications, the mean values of echo signals in different scenarios vary greatly, the embodiments of the present application can screen the IMF components to accurately select effective modal components.

[0079] In an optional implementation manner, the fuzzy entropy value can be used as the eigenvalue for screening the IMF components. Exemplarily, the fuzzy membership degree of the modal component can be calculated first, then the fuzzy entropy (Fuzzy Entropy) of the modal component can be calculated, and then the fuzzy entropy can be compared with the entropy threshold.

[0080]

[0081] FuzzyEn(m,r) = [lnφ m (r) - lnφ m+1 (r)]

[0082] where m represents the dimension, is the maximum distance between any two time series i and j in the modal component, r represents the threshold coefficient, and the value range is [0.1, 0.25].

[0083] By comparing the fuzzy entropy with the entropy threshold, the modal components with fuzzy entropy less than the entropy threshold in the modal components are eliminated, obtaining multiple first remaining modal components.

[0084] After obtaining the first remaining modal components, the autocorrelation coefficient ρ of the first remaining modal components can be calculated i, to determine the correlation between the modal components and the original signal (radar echo signal). The range of the autocorrelation coefficient is [0, 1]. The larger the autocorrelation coefficient, the more correlated it indicates. Then, the autocorrelation coefficient can be compared with the set correlation coefficient threshold to eliminate the spurious modal components with an autocorrelation coefficient less than the correlation coefficient threshold in the first remaining modal components, and multiple target modal components are obtained.

[0085] It should be noted that in the embodiments of the present application, the entropy threshold and the correlation coefficient threshold can be set according to actual needs, and the embodiments of the present application do not make special restrictions on this.

[0086] In some embodiments, the autocorrelation coefficient can be the Pearson correlation coefficient or other coefficient calculation methods. For the convenience of description, the embodiments of the present application take the autocorrelation coefficient as the Pearson correlation coefficient for exemplary illustration.

[0087]

[0088] where ρ i is the correlation coefficient between the i-th modal component and the radar echo signal, and ρ h is the threshold, for example, 0.2742.

[0089] By performing correlation analysis on the decomposed IMF components and setting the correlation coefficient threshold to screen the target modal components, the spurious components can be eliminated, the effective modal components can be extracted, and the interference of the irrelevant modal components on the radar echo signal can be reduced. Then, each target modal component can be synthesized into a radar echo denoised signal to achieve the denoising of the radar echo signal.

[0090] In some embodiments, after obtaining the radar echo denoised signal, the echo spectrum signal quantity Xw[k] can be obtained by improving the estimation method of the frequency difference, and then other information such as distance and speed can be obtained.

[0091]

[0092] where D represents the signal after discrete Fourier transform, k is the frequency component, f0 is the peak frequency, Vf is the frequency resolution, A is the signal amplitude, and a m is the window coefficient.

[0093] The following combines a specific embodiment to illustrate the radar echo denoising result of the present solution.

[0094] The ranging radar signal acquisition system mainly includes: a radar sensor, a radar data acquisition host computer, a radar water level gauge, a power adapter, a fixed tripod, a four-pin aviation plug wire, and a personal computer. Fix the radar sensor on the fixed triangular bracket, keep it perpendicular to the wall, and raise it about 1 cm above the reference plane to prevent the radar wave from hitting the reference plane and generating measurement errors. Detect a stationary target (smooth wall) 3 meters directly in front of the radar, and receive data through the serial port assistant connected to the personal computer.

[0095] Figure 3 This is the waveform diagram of the radar echo signal for the noise reduction experiment provided by the embodiment of the present application. As Figure 3 shown, there are a large number of noises in the radar echo signal. Observing the signal amplitude change in the time domain has no pattern. Using the radar echo noise reduction method of this solution to decompose the radar time-domain waveform, multiple IMF components can be obtained.

[0096] Figure 4A and Figure 4B exemplarily show the first 8 IMF1-IMF8 of the decomposition results. From Figure 4A and Figure 4B it can be seen that the IMF1, IMF2, IMF3, and IMF4 components are the components with the shortest time scale and higher frequency decomposed from the original signal by the method described in the embodiment of the present application, representing the high-frequency components in the signal.

[0097] Optimize the IMF components through the fuzzy entropy value, calculate the autocorrelation coefficient of the remaining IMF components, and determine the correlation degree between the decomposed IMF1-IMF10 and the original signal. The result is as Figure 5 shown. In Figure 5 , the autocorrelation coefficient threshold is 0.2742. IMF2, IMF7, IMF8, and IMF10 are greater than the correlation coefficient threshold and can be determined as target modal components. IMF1, IMF3, IMF4, IMF5, IMF6, and IMF9 are less than the correlation coefficient threshold and can be determined as false modal components and eliminated. This solution screens the IMF components through the fuzzy entropy value, and then selects the IMF components greater than the correlation coefficient threshold, so as to obtain the IMF components containing more original signal characteristics, and then synthesize the radar echo noise reduction signal.

[0098] Figure 6 This is the waveform diagram of the synthesized signal after adopting this solution in the noise reduction experiment provided by the embodiment of the present application. From Figure 6 it can be seen that the high-frequency noise is effectively suppressed, and the trend change of the signal amplitude is completely retained. Therefore, all the rich characteristic information of the original signal can be reflected in the radar echo noise reduction signal.

[0099] To further verify the superiority of this solution, the original signal waveform is decomposed by EMD and EWT respectively below, and then the target modal components are selected for synthesis by using the correlation coefficient method.

[0100] Figure 7 This is the synthesized signal waveform diagram after using EMD for the noise reduction experiment provided by the embodiment of the present application. Figure 8 This is the synthesized signal waveform diagram after using EWT for the noise reduction experiment provided by the embodiment of the present application. By comparing Figures 6 - 8 , it can be seen that the radar echo signal processed by this solution removes significantly more high-frequency noise than either the EMD method or the EWT method, the signal is smoother, and the amplitude of the signal is more retained.

[0101] To further quantify the noise reduction effects of the EMD method, the EWT method, and this solution, the signal-to-noise ratios of the radar echo noise reduction signals obtained by denoising the radar echo signal with the three solutions can be compared. Table 1 shows the signal-to-noise ratios of the radar echo noise reduction signals obtained by using the EWD method, the EWT method, and this solution provided by the embodiment of the present application.

[0102]

[0103] According to Table 1, the signal-to-noise ratio after EMD noise reduction is 11.65 dB, the signal-to-noise ratio after EWT noise reduction is 16.67 dB, and the signal-to-noise ratio after this solution is the largest, which is 21.88 dB. It is increased by 11.23 dB compared with EMD and 5.21 dB compared with EWT. This further shows that this solution has the best noise reduction effect on the radar echo signal.

[0104] The embodiment of the present application provides a radar echo noise reduction method. The method includes subtracting the radar echo signal from the fitting signal corresponding to the radar echo signal to obtain a radar difference signal; determining the Fourier spectrum and S spectrum of the radar difference signal, and performing band-pass filtering on the S spectrum to obtain a filtered signal; wherein, the center frequency of the band-pass filtering is the average value of the spectral peak frequencies of the Fourier spectrum and the S spectrum; decomposing the filtered signal to obtain the modal components of the filtered signal; determining a plurality of target modal components from the modal components according to the fuzzy entropy and autocorrelation coefficient of each modal component, and synthesizing each target modal component into a radar echo noise reduction signal. This method can well retain the characteristic information, eliminate the false components, and enhance the accuracy and reliability of the radar echo signal processing, so that the radar echo signal can be effectively denoised.

[0105] Those skilled in the art can understand that the above embodiments are exemplary and are not used to limit the present application. Where possible, the execution order of one or several of the above steps can be adjusted, or selective combination can be performed to obtain one or more other embodiments. Those skilled in the art can arbitrarily select and combine from the above steps, and all those that do not depart from the essence of the present application solution fall within the protection scope of the present application.

[0106] Based on the same inventive concept, as an implementation of the above method, an embodiment of the present application provides a radar echo noise reduction device. This device embodiment corresponds to the foregoing method embodiment. For the convenience of reading, the details of the foregoing method embodiment will not be described one by one in this device embodiment. However, it should be clear that the device in this embodiment can correspondingly implement all the contents of the foregoing method embodiment.

[0107] Figure 9 It is a schematic structural diagram of the radar echo noise reduction device provided by the embodiment of the present application. As Figure 9 shown, the device provided in this embodiment includes:

[0108] A difference module 110, configured to subtract the radar echo signal from the fitting signal corresponding to the radar echo signal to obtain a radar difference signal;

[0109] A filtering module 120, configured to determine the Fourier spectrum and S spectrum of the radar difference signal, perform band-pass filtering on the S spectrum to obtain a filtered signal; wherein, the center frequency of the band-pass filtering is the average value of the spectrum peak frequencies of the Fourier spectrum and the S spectrum;

[0110] A decomposition module 130, configured to decompose the filtered signal to obtain each modal component of the filtered signal;

[0111] A synthesis module 140, configured to determine multiple target modal components from each modal component according to the fuzzy entropy and autocorrelation coefficient of each modal component, and synthesize each target modal component into a radar echo noise reduction signal.

[0112] As an optional implementation manner of the embodiment of the present application, the fitting signal is obtained by performing fitting calculation on the radar echo signal using the least squares method.

[0113] As an optional implementation manner of the embodiment of the present application, the decomposition module 130 is specifically configured to:

[0114] Determine the envelope of the filtered signal, obtain the maximum values of the envelope and arrange them in descending order;

[0115] Determine the frequency points corresponding to the maximum values within the target range, use the midpoints of adjacent frequency points as boundary points, and use the boundary points to divide the envelope into intervals;

[0116] For each interval, the wavelet function and the scaling function are respectively used to perform inverse Fourier transform on the Fourier spectrum corresponding to the interval to obtain the modal components of the filtered signal.

[0117] As an optional implementation manner of the embodiment of the present application, the fuzzy entropy of the target modal component is greater than the entropy threshold, and the autocorrelation coefficient of the target modal component is greater than the correlation coefficient threshold.

[0118] As an optional implementation manner of the embodiment of the present application, the band-pass range of the band-pass filter is composed of an interval between the lowest frequency and the highest frequency. The lowest frequency is not less than 0.6F, and the highest frequency is not greater than 1.4F, where F represents the center frequency of the band-pass filter.

[0119] As an optional implementation manner of the embodiment of the present application, the envelope is obtained by using the polynomial fitting interpolation method, and the autocorrelation coefficient is the Pearson correlation coefficient.

[0120] As an optional implementation manner of the embodiment of the present application, the radar echo noise reduction device further includes an acquisition module 150, and the acquisition module 150 is used to acquire the radar echo signal before obtaining the fitting curve of the radar echo signal.

[0121] The radar echo noise reduction device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, which will not be elaborated here.

[0122] Those skilled in the art can clearly understand that for the convenience and conciseness of description, only the above-mentioned division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of the present application. The specific working processes of the units and modules in the above system can refer to the corresponding processes in the foregoing method embodiments, which will not be elaborated here.

[0123] Based on the same inventive concept, the embodiment of the present application also provides a radar echo noise reduction device. Figure 10 This is a structural schematic diagram of the radar echo noise reduction device provided in the embodiment of the present application, as Figure 10As shown in the figure, the radar echo noise reduction device provided in this embodiment includes: a memory 210 and a processor 220. The memory 210 is used to store computer programs; the processor 220 is used to execute the method described in the above method embodiment when calling the computer program.

[0124] The radar echo noise reduction device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, which will not be elaborated here.

[0125] This application embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method described in the above method embodiment is implemented.

[0126] This application embodiment also provides a computer program product. When the computer program product runs on the radar echo noise reduction device, the radar echo noise reduction device is caused to execute the method described in the above method embodiment.

[0127] In the above embodiment, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the processes or functions described in the embodiments of this application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired (such as coaxial cable, optical fiber, digital subscriber line) or wireless (such as infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that the computer can access or a data storage device such as a server or data center that includes one or more integrated available media. The available media can be magnetic media (such as floppy disks, hard disks, or magnetic tapes), optical media (such as DVDs), or semiconductor media (such as solid state disks (SSDs)), etc.

[0128] Those of ordinary skill in the art can understand all or part of the processes in the above method embodiments. These processes can be completed by relevant hardware instructed by a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above method embodiments. The foregoing storage medium can include various media that can store program codes, such as ROM or random access memory RAM, magnetic disks, or optical discs.

[0129] The naming or numbering of steps that appear in this application does not mean that the steps in the method flow must be executed in the chronological / logical order indicated by the naming or numbering. The named or numbered process steps can be changed in the order of execution according to the technical purpose to be achieved, as long as the same or similar technical effects can be achieved.

[0130] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For parts that are not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0131] In the embodiments provided in this application, it should be understood that the disclosed devices / apparatuses and methods can be implemented in other ways. For example, the device / apparatus embodiments described above are merely illustrative. For example, the division of the modules or units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be in electrical, mechanical or other forms.

[0132] It should be understood that when used in the specification and appended claims of this application, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their combinations.

[0133] In the description of this application, unless otherwise specified, " / " means that the objects associated before and after are in an "or" relationship. For example, A / B can represent A or B; the "and / or" in this application is only a description of the association relationship of the associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Among them, A and B can be singular or plural.

[0134] Moreover, in the description of this application, unless otherwise specified, "a plurality of" means two or more than two. "At least one of the following" or its similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or multiple.

[0135] As used in the specification of this application and the appended claims, the term "if" may be construed, depending on the context, as "when" or "once" or "in response to determining" or "in response to detecting". Similarly, the phrase "if determined" or "if [the described condition or event] is detected" may be construed, depending on the context, to mean "once determined" or "in response to determining" or "once [the described condition or event] is detected" or "in response to detecting [the described condition or event]".

[0136] In addition, in the description of the specification of this application and the appended claims, the terms "first", "second", "third", etc. 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 may be interchanged where appropriate so that the embodiments described herein can be implemented in an order different from that shown or described herein.

[0137] Reference to "one embodiment" or "some embodiments" or the like described in the specification of this application means that a particular feature, structure, or characteristic described in connection with that embodiment is included in one or more embodiments of this application. Thus, statements such as "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments", etc. that appear in different places in this specification do not necessarily all refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in another way.

[0138] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit them; although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A radar echo denoising method, characterized in that: include: Subtracting the radar echo signal from the fitting signal corresponding to the radar echo signal to obtain a radar difference signal; Determine the Fourier spectrum and S spectrum of the radar difference signal, and perform bandpass filtering on the S spectrum to obtain a filtered signal; wherein the center frequency of the bandpass filtering is the average value of the spectrum peak frequencies of the Fourier spectrum and the S spectrum; Decomposing the filtered signal to obtain modal components of the filtered signal; According to the fuzzy entropy and autocorrelation coefficient of each modal component, a plurality of target modal components are determined from each modal component, and each target modal component is synthesized into a radar echo noise reduction signal.

2. The method according to claim 1, characterized in that The fitting signal is obtained by fitting the radar echo signal using the least square method.

3. The method according to claim 1, characterized in that The decomposing the filtered signal to obtain the modal components of the filtered signal includes: Determine the envelope of the filtered signal, obtain the maximum value of the envelope and arrange them in descending order; Determine the frequency point corresponding to the maximum value within the target range, take the midpoint of the adjacent frequency points as the boundary point, and divide the envelope into intervals using the boundary point; For each interval, a wavelet function and a scaling function are respectively used to inversely transform the Fourier spectrum corresponding to the interval to obtain the modal component of the filtered signal.

4. The method according to claim 1, characterized in that: The fuzzy entropy of the target modal component is greater than an entropy threshold, and the autocorrelation coefficient of the target modal component is greater than a correlation coefficient threshold.

5. The method according to claim 1, characterized in that: The bandpass range of the bandpass filter is composed of the interval between the lowest frequency and the highest frequency, the lowest frequency is not less than 0.6F, the highest frequency is not greater than 1.4F, and F represents the center frequency of the bandpass filter.

6. The method according to claim 3, characterized in that The envelope is obtained by using a polynomial fitting interpolation method, and the autocorrelation coefficient is a Pearson correlation coefficient.

7. The method according to any one of claims 1 to 6, characterized in that: Before obtaining the fitting curve of the radar echo signal, the method further includes: The radar echo signal is collected.

8. A radar echo noise reduction device, characterized in that: include: A difference module, used for subtracting a radar echo signal from a fitting signal corresponding to the radar echo signal to obtain a radar difference signal; A filtering module, used to determine the Fourier spectrum and S spectrum of the radar difference signal, and perform bandpass filtering on the S spectrum to obtain a filtered signal; wherein the center frequency of the bandpass filtering is the average value of the spectrum peak frequencies of the Fourier spectrum and the S spectrum; A decomposition module, used for decomposing the filtered signal to obtain modal components of the filtered signal; The synthesis module is used to determine a plurality of target modal components from each of the modal components according to the fuzzy entropy and the autocorrelation coefficient of each of the modal components, and synthesize each of the target modal components into a radar echo noise reduction signal.

9. A radar echo noise reduction device, characterized in that: include: A memory and a processor, wherein the memory is used to store a computer program; and the processor is used to execute the method according to any one of claims 1 to 7 when calling the computer program.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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