Radar signal denoising method and system based on adaptive threshold and improved threshold function

Abstract

The application provides a radar signal denoising method based on adaptive threshold and improved threshold function, and relates to the field of signal denoising. The steps are as follows: S1: performing multi-scale wavelet transform on the original radar signal to obtain wavelet coefficients under multi-scale, and obtaining the logarithmic energy entropy of the wavelet coefficient matrix; S2: dividing the original radar signal s(n) into normal radar signals and noisy radar signals; S3: processing the normal radar signals; S4: processing the noisy radar signals; S5: reconstructing the wavelet coefficients processed through steps S1-S4; S6: obtaining the denoised radar signal d(n). When wavelet denoising is performed, the original radar signal is divided into normal radar signals and noisy radar signals, the noisy radar signals are denoised, resource waste caused by blindly denoising all signals is avoided, and the denoising is more targeted; based on the adaptive wavelet threshold and the improved semi-soft threshold function, the signal-to-noise ratio of the radar signal is improved, and the denoising effect is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of signal denoising, and particularly relates to a radar signal denoising method and system based on an adaptive threshold and an improved threshold function. BACKGROUND

[0002] A radar system itself has the ability to accurately measure the distance, direction and speed of an aerial target, can detect targets at a distance far beyond the range that the human eye can see, and can do so in almost all types of weather, and regardless of whether it is day or night. Based on the advantages over the human eye, radar systems have been widely used in both military and civilian fields. First, radar can see farther than the human eye, can accurately determine the distance from a target, and is basically not affected by smoke, fog, and clouds, and can work normally in all weather conditions; in addition, because radar emits its own energy and does not rely on sunlight or other energy, it can work 24 hours a day.

[0003] However, in the process of collecting and transmitting, radar signals will inevitably be disturbed by a large amount of noise signals such as mechanical noise and electromagnetic noise, and weak radar signals will be overwhelmed by noise. If the signals are directly analyzed without denoising, the correctness of the analysis results will be affected, so it is necessary to denoise the radar signals disturbed by noise.

[0004] Wavelet analysis can better distinguish the sudden parts and noise in the signal and can denoise the signal because it can analyze the time domain and frequency domain of the signal at the same time. Since the wavelet transform was proposed, it has been well applied in many engineering technical fields, and the key to wavelet denoising lies in the selection of the threshold function and the threshold, which is related to the quality of signal denoising to some extent. Hard threshold function and soft threshold function are the most commonly used functions in the process of wavelet denoising. The hard threshold can well preserve the local characteristics such as signal edges, but can cause sharpening phenomenon. The soft threshold processing is relatively smooth, but can cause distortion phenomenon, and the existing threshold selection methods are unbiased likelihood estimation threshold (rigrsure), fixed threshold (sqtwolog), heuristic threshold (heursure), and maximum minimum threshold (minimaxi). The selection of these thresholds is a fixed value, which cannot adapt to the local characteristics of wavelet coefficients, and the application of these thresholds to divide wavelet coefficients can cause the loss of local details of radar signals.

[0005] With the continuous deepening of wavelet analysis, algorithms with different thresholds or improved threshold functions have also been proposed one after another. For example, publication number CN103777191A discloses a lidar waveform data processing method based on wavelet shift-invariant threshold denoising. It provides a method for performing wavelet denoising on radar signals, which can realize the function of denoising radar signals. However, it still uses soft threshold functions or hard threshold functions to denoise radar signals, and cannot solve the problems of discontinuity of hard threshold functions and distortion of soft threshold functions. In addition, it blindly performs denoising on all radar signals, resulting in a long time required for denoising and unnecessary waste of resources. Summary of the Invention

[0006] The technical problem to be solved by the present invention is how to improve the signal-to-noise ratio of radar signals during wavelet denoising, improve the denoising effect, and shorten the time required for signal denoising and increase the efficiency.

[0007] In order to solve the above problems, the present invention provides the following technical solution: a radar signal denoising method based on an adaptive threshold and an improved threshold function, comprising the following steps:

[0008] S1: Perform multi-scale wavelet transform on the original radar signal s(n) to obtain the multi-scale wavelet coefficients w j,k , and obtain the wavelet coefficient w j,k The logarithmic energy entropy of the matrix;

[0009] S2: Split the original radar signal s(n) into normal radar signal x nol and the noisy radar signal x lek ;

[0010] S3: Process normal radar signal x nol ;

[0011] S4: Processing noisy radar signal x lek The specific process is:

[0012] S401: Use wavelet 'sym3' to analyze the noisy radar signal x lek Decompose and obtain the corresponding high-frequency wavelet coefficient H j,k Matrix and low-frequency wavelet coefficient matrix;

[0013] S402: High frequency wavelet coefficient H j,k The matrix calculates the adaptive threshold λ;

[0014] S403: Using the improved semi-soft threshold function, combined with the adaptive threshold λ in step S402, to process the high-frequency wavelet coefficients H j,k The improved semi-soft threshold function is:

[0015]

[0016] wherein 0<z<1;

[0017] S5: reconstructing the wavelet coefficients processed in steps S1-S4;

[0018] S6: obtaining the de-noised radar signal d(n).

[0019] Preferably, in step S1, the wavelet coefficients w j,k are obtained by calculating the logarithmic energy entropy of the matrix of the wavelet coefficients w The total energy is

[0020] Preferably, in step S402, the adaptive threshold λ is calculated according to the formula: N is the number of high-frequency wavelet coefficients.

[0021] Preferably, in step S1, the wavelet transform of the original radar signal is performed by convolving the wavelet function with the original radar signal at different scales, wherein the wavelet function is R,a≠0, wherein a is a scale factor and b is a translation factor, The function of ψ a,b (t) is to keep the energy of the wavelet function ψ

[0022] Preferably, in step S2, the original radar signal s(n) is divided into normal radar signals x nol and noisy radar signals x lek by taking n sampling points on the original radar signal s(n) and determining the demarcation value h that distinguishes the original radar signal as a normal radar signal or a noisy radar signal. wherein x i is the amplitude of the sampling point;

[0023] After obtaining the demarcation value h, a section of the original radar signal s(n) is received, and the radar signal smaller than the demarcation value is the normal radar signal x nol , and the radar signal larger than the demarcation value is the noisy radar signal x lek .

[0024] Preferably, in step S3, the normal radar signal x nol is processed by taking m sampling points as a circular queue with a fixed length of m sampling points, and according to the first-in first-out principle, i.e. each time a new data is sampled and put into the tail of the queue, the original data at the head of the queue is discarded, and the data called out each time is the arithmetic mean of the m data in the current queue:

[0025] Preferably, in the step S401, the wavelet'sym3' is used to decompose the noisy radar signal x lek The decomposition layers for decomposition are 5 layers.

[0026] Preferably, in the step S5, the wavelet'sym3' is still used to reconstruct the wavelet coefficients processed through the steps S1-S4.

[0027] Preferably, after the step S6, the signal-to-noise ratio SNR and the mean square error MSE are calculated after the radar signal d(n) is obtained.

[0028] The calculation formula of the mean square error MSE is:

[0029] The calculation formula of the signal-to-noise ratio SNR is: Wherein s(n) represents the original radar signal, and d(n) represents the denoised radar signal.

[0030] Corresponding to the above method, the application also provides a radar signal denoising system based on an adaptive threshold and an improved threshold function, comprising the following modules:

[0031] A wavelet transform module is used to perform multi-scale wavelet transform on the original radar signal s(n) to obtain wavelet coefficients w j,k , and the logarithmic energy entropy of the wavelet coefficient matrix is obtained.

[0032] A module for segmenting the original radar signal is used to segment the original radar signal s(n) into normal radar signals x nol and noisy radar signals x lek .

[0033] A module for processing normal signals is used to process the normal radar signals x nol .

[0034] A module for processing noisy signals is used to process the noisy radar signals x lek .

[0035] A wavelet coefficient reconstruction module is used to reconstruct the wavelet coefficients.

[0036] A module for obtaining a denoised radar signal is used to obtain a denoised radar signal d(n).

[0037] The application has the following advantages:

[0038] (1) The original radar signal is segmented to obtain normal radar signals and noisy radar signals, and then only the noisy radar signals are denoised, which avoids the waste of resources caused by blindly denoising all radar signals, makes the denoising process more targeted, and saves the time required for denoising.

[0039] (2) The radar signal denoising method based on the adaptive wavelet threshold of logarithmic energy entropy and the improved semi-soft threshold function is used, the denoised radar signal data are obtained, the signal-to-noise ratio of the radar signal is improved, and the denoising effect is improved.

[0040] (3) The evaluation indexes of the signal-to-noise ratio (SNR) and the mean square error (MSE) are used to distinguish the denoising effects of the improved threshold and the traditional threshold function, the greater the signal-to-noise ratio is, the smaller the mean square error is, the denoised signal is closer to the original signal, and the better the denoising effect is. BRIEF DESCRIPTION OF DRAWINGS

[0041] Figure 1 It is a flow chart of the embodiment 1 of the present application;

[0042] Figure 2 It is a comparison chart of the MSE and the SNR of the denoising by using different thresholds and threshold functions in the embodiment 1 of the present application;

[0043] Figure 3 It is an image of the original radar signal in the embodiment 1 of the present application;

[0044] Figure 4 It is an image of the radar signal after adding noise in the embodiment 1 of the present application;

[0045] Figure 5 It is an image of the radar signal after denoising by using the hard threshold function in the embodiment 1 of the present application;

[0046] Figure 6 It is an image of the radar signal after denoising by using the soft threshold function in the embodiment 1 of the present application;

[0047] Figure 7 It is an image of the radar signal after denoising by using the semi-soft threshold function in the embodiment 1 of the present application;

[0048] Figure 8 It is an image of the radar signal after denoising by using the improved semi-soft threshold function of the present application in the embodiment 1 of the present application;

[0049] Figure 9 It is a comparison chart of the time required for denoising by using different thresholds and threshold functions in the embodiment 1 of the present application. DETAILED DESCRIPTION

[0050] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort belong to the scope of protection of the present application.

[0051] Embodiment 1

[0052] As shown in the flowchart of the whole embodiment of the present application, the present application provides a radar signal denoising method based on adaptive threshold and improved threshold function, comprising the following steps: Figure 1

[0053] S1: performing multi-scale wavelet transform on the original radar signal s(n) to obtain the wavelet coefficients w j,k in the multi-scale, wherein w j,k represents the kth wavelet coefficient of the jth layer decomposed by wavelet transform, and the logarithmic energy entropy of the matrix of the wavelet coefficients w

[0054] S101: first performing one-dimensional wavelet continuous transform on the original radar signal, that is, convolving the wavelet function with the original radar signal under multiple scales, wherein the wavelet function is: wherein a is a scale factor, b is a translation factor, the function of w a,b (t) with different a values remains unchanged;

[0055] Suppose that a signal f with a length of n is contaminated by noise e, and the obtained noisy data is X n =f+e n . In order to obtain the best approximation signal g of the signal f from the noisy data X n , g is the best approximation of f in some sense. That is, the main task of denoising is to separate the actual signal from the noise signal as much as possible, to retain the true signal and remove the noise signal, so as to achieve the purpose of denoising. Wavelet transform plays an important role in signal denoising due to its unique multi-resolution, decorrelation and selected basis flexibility. After wavelet transform, the signal presents different rules under different resolutions;

[0056] S102: obtaining the wavelet coefficients w j,k in the multi-scale after multi-scale wavelet transform, and obtaining the logarithmic energy entropy of the matrix of the wavelet coefficients w j,k Entropy is a measure of the amount of information contained in a signal, and the entropy value is used to reflect the uniformity of the probability distribution;

[0057] Suppose that E={E​k} is the energy of the matrix of wavelet transform coefficient w j,k The information entropy is defined according to the logarithmic energy entropy: The total energy is:

[0058] S2: The original radar signal s(n) is divided into normal radar signal x nol and noisy radar signal x lek , the specific process is:

[0059] First, the original radar signal s(n) is segmented, and n sampling points are taken on the original radar signal s(n). The demarcation value for distinguishing whether the radar signal is a normal radar signal or a noisy radar signal is: where x i is the amplitude of the sampling point;

[0060] After obtaining the demarcation value h, the signals in a segment of the original radar signal are received. The radar signals less than the demarcation value are stored in the normal radar signal area and recorded as x nol . The radar signals greater than the demarcation value are stored in the noisy radar signal area and recorded as x lek .

[0061] S3: Processing the normal radar signal x nol , the specific process is: for the obtained normal radar signal x nol , the sliding mean method is used for filtering processing: according to the first-in first-out principle, that is, taking m consecutive sampling points as a circular queue, the length of the queue is fixed as the length of m sampling points. Each time a new data is sampled and put into the tail of the queue, and the original head data is discarded. The data called out each time is the arithmetic mean of the m data in the current queue. The expression is: In this embodiment, m = 10.

[0062] S4: Processing the noisy radar signal x lek , the specific process is:

[0063] S401: The noisy radar signal x lek is decomposed by 5 layers using the wavelet'sym3' to obtain the corresponding high-frequency wavelet coefficient matrix H j,k and the low-frequency wavelet coefficient matrix;

[0064] S402: The adaptive threshold λ of the high-frequency wavelet coefficient matrix H j,k is calculated, N is the number of high-frequency wavelet coefficients;

[0065] S403: The expression of the existing hard threshold function is: The expression of the soft threshold function is: In order to take into account the advantages of hard threshold and soft threshold function, semi-soft threshold function is born, the expression of semi-soft threshold function is: Wherein, z is a number between 0-1, the function estimates the wavelet coefficient w j,k Between soft and hard threshold function, it can improve the ringing, pseudo-Gibbs effect and other visual distortion and edge blur phenomenon, but because the parameter z in the actual operation often takes a constant value, it does not have self-adaptation, so there are still inherent bias and discontinuity phenomenon;

[0066] In order to solve the above problems, the improved semi-soft threshold function is used in the embodiment, and the high frequency wavelet coefficient H j,k The improved semi-soft threshold function is:

[0067]

[0068] Wherein, 0<z<1.

[0069] S5: using wavelet'sym3' to reconstruct the wavelet coefficient processed by steps S1-S4;

[0070] S6: after the wavelet coefficient is reconstructed, the denoised radar signal d(n) is obtained, and the signal-to-noise ratio (SNR) and mean square error (MSE) are calculated to compare the denoising effect of the improved semi-soft threshold function and the traditional threshold function,

[0071] The formula for calculating the mean square error MSE is: Wherein s(n) represents the original radar signal, and d(n) represents the denoised radar signal;

[0072] The formula for calculating the signal-to-noise ratio SNR is:

[0073] In this embodiment, the denoising process of radar signal is simulated by matlab, and the real radar signal data is used as the original data, and the noise with amplitude of 0.2 is randomly generated, wherein the signal-to-noise ratio SNR=27.3309dB and the mean square error MSE=0.0133 are compared between the generated noise signal and the original signal. On this basis, the radar signal is divided into two groups for experiment:

[0074] The first group of experiments: the threshold function is fixed, and different threshold values are selected for comparison with the threshold value proposed in the embodiment, and the selection of threshold value includes four kinds: unbiased likelihood estimation threshold (rigrsure), fixed threshold (sqtwolog), heuristic threshold (heursure) and maximum minimum threshold (minimaxi);

[0075] The second set of experiments: The threshold is fixed and different threshold functions are selected for comparison with the threshold function proposed in this embodiment. The threshold functions include three types: hard threshold function, soft threshold function, and semi-soft threshold function.

[0076] The wavelet basis selected in the above two groups of experiments is 'sym3', and the number of decomposition layers is 5.

[0077] like Figure 2 As shown in FIG5 , a comparison diagram of MSE and SNR obtained by denoising the radar signal using different thresholds and different threshold functions in this embodiment is obtained. By comparing the data, it can be seen that the radar signal denoising using the adaptive wavelet threshold and the improved semi-soft threshold function in this embodiment has a higher signal-to-noise ratio and a smaller mean square error, indicating that the signal denoised by using the adaptive wavelet threshold and the improved semi-soft threshold function in this embodiment is closer to the original signal and has a better denoising effect.

[0078] like Figure 3 As shown, it is the original radar signal in this embodiment;

[0079] like Figure 4 As shown, it is the radar signal after adding noise in this embodiment;

[0080] like Figure 5 As shown in FIG, the radar signal after denoising by the hard threshold function is applied in this embodiment. It can be observed that the signal after denoising by the hard threshold function has many peaks.

[0081] like Figure 6 As shown in FIG. 1 , the radar signal after denoising by the soft threshold function is applied in this embodiment. It can be observed that the signal after denoising by the soft threshold function has fewer peaks than that by the hard threshold function, but there are still fuzzy points, and the degree of closeness to the original signal is not ideal.

[0082] like Figure 7 As shown in FIG, the radar signal after denoising using the semi-soft threshold function in this embodiment. It can be observed that the semi-soft threshold function combines the advantages of the soft and hard threshold functions, but still has inherent deviations and discontinuities, and the degree of closeness to the original signal is still not high;

[0083] like Figure 8 As shown, the radar signal after denoising by the improved semi-soft threshold function of this embodiment is shown. It can be observed that the signal denoised by the improved semi-soft threshold function of this embodiment is closest to the original signal, that is, it has a good denoising effect, can better preserve the detailed information of the original radar signal, and effectively solves the inherent deviation and discontinuity in denoising by using the semi-soft threshold function.

[0084] like Figure 9The shown is a comparison chart of the time required for radar signal denoising by applying different thresholds, different denoising methods and the new adaptive threshold and improved threshold function in the embodiment, and it can be observed that:

[0085] When a certain threshold function is applied to the radar signal denoising, compared with other fixed thresholds, the time required for denoising is shorter when the adaptive new threshold of the embodiment is applied.

[0086] When a certain threshold is selected as the threshold of the threshold function, compared with other threshold functions, the time required for denoising is shorter when the improved threshold function of the embodiment is applied.

[0087] When the adaptive threshold and the improved threshold function of the embodiment are applied at the same time, the time required for denoising is the shortest.

[0088] That is, when the adaptive threshold and the improved threshold function of the embodiment are applied separately in the process of wavelet denoising, the time required for denoising can be reduced. When the adaptive threshold and the improved threshold function of the embodiment are combined for wavelet denoising, the time required for denoising can be further reduced.

[0089] In the embodiment, the original radar signal is segmented to obtain normal radar signals and noisy radar signals, and then only the noisy radar signals are denoised, so that the resource waste caused by blindly denoising all radar signals is avoided, the denoising process is more targeted, the time required for the denoising process is shorter, and the denoising efficiency is improved. The radar signal denoising method using the adaptive wavelet threshold of the logarithmic energy entropy and the improved threshold function obtains the denoised radar signal data, improves the signal-to-noise ratio of the radar signal, improves the denoising effect, and can better preserve the detail information possessed by the original signal.

[0090] Embodiment 2

[0091] Corresponding to the embodiment 1 of the application, the embodiment 2 of the application also provides a radar signal denoising system based on an adaptive threshold and an improved threshold function, comprising the following modules:

[0092] A wavelet transform module is used to perform multi-scale wavelet transform on the original radar signal s(n) to obtain wavelet coefficients w j,k , which represents the kth wavelet coefficient of the jth layer of wavelet transform decomposition, and the logarithmic energy entropy of the wavelet coefficient matrix is obtained. Specifically, it comprises the following units:

[0093] A wavelet transform unit is used to perform one-dimensional continuous wavelet transform on the original radar signal, that is, to convolve the wavelet function with the original radar signal at multiple scales, and the wavelet function is: Where a is a scale factor, b is a translation factor, The role is to make the wavelet ψ a,b (t) with different a values

[0094] The wavelet transform has the characteristics of multi-resolution, decorrelation and flexible basis selection, and plays an important role in signal denoising. After wavelet transform, the signal shows different rules at different resolutions.

[0095] The log energy entropy unit is used to obtain the wavelet coefficients w j,k at multiple scales after multi-scale wavelet transform, and the log energy entropy of the matrix of wavelet coefficients w j,k is obtained. The information entropy is a measure of the amount of information contained in the signal, and the entropy value reflects the uniformity of the probability distribution.

[0096] Let E = {E k} be the energy of the wavelet transform coefficient matrix w j,k , and the information entropy is defined as: The total energy is calculated as:

[0097] The original radar signal segmentation module is used to segment the original radar signal s(n) into normal radar signal x nol and noisy radar signal x lek , which includes the following units:

[0098] The calculation of the threshold unit is used to segment the original radar signal s(n), and the threshold value of the radar signal is: where x i is the amplitude of the sampling point.

[0099] The signal classification unit is used to receive a signal in a segment of the original radar signal after obtaining the threshold value. The radar signal less than the threshold value is stored in the normal radar signal area and recorded as x nol , and the radar signal greater than the threshold value is stored in the noisy radar signal area and recorded as x lek .

[0100] The normal signal processing module is used to process the normal radar signal x nol , and the specific working process is as follows: for the obtained normal radar signal x nol , the sliding mean method is used for filtering processing: according to the first-in first-out principle, i.e. taking m consecutive sampling points as a circular queue, the length of the queue is fixed as the length of m sampling points. Each time a new data is sampled and put into the tail of the queue, and the original head data is discarded. The data called out each time is the arithmetic mean of the m data in the current queue. The expression is: m = 10 in this embodiment.

[0101] processing noisy signal module: for processing noisy radar signal x lek , specifically comprising the following units:

[0102] decomposition noisy signal unit: for adopting wavelet'sym3' to carry out 5-layer decomposition to noisy radar signal x lek , and obtain the matrix of corresponding high-frequency wavelet coefficient H j,k and low-frequency wavelet coefficient matrix;

[0103] adaptive threshold calculation unit: for calculating the adaptive threshold λ of the matrix of high-frequency wavelet coefficient H j,k , and N is the number of high-frequency wavelet coefficient;

[0104] improved semi-soft threshold function unit: for processing the matrix of high-frequency wavelet coefficient H j,k in combination with the adaptive threshold λ calculated by the adaptive threshold calculation unit, and the improved semi-soft threshold function is:

[0105]

[0106] wherein, 0<z<1.

[0107] wavelet coefficient reconstruction module: for reconstructing the wavelet coefficient processed by the wavelet transform module, the original radar signal segmentation module, the normal signal processing module and the noisy signal processing module by adopting wavelet'sym3';

[0108] obtaining denoised radar signal module: for obtaining the denoised radar signal d(n), and calculating signal-to-noise ratio (SNR) and mean square error (MSE) to compare the denoising effect of improved threshold function and traditional threshold function,

[0109] The calculation formula of the mean square error MSE is:

[0110] The calculation formula of the signal-to-noise ratio SNR is: Wherein s(n) represents the original radar signal, and d(n) represents the denoised radar signal.

[0111] In this embodiment, the wavelet coefficients w j,kAnd the logarithmic energy entropy of the wavelet coefficient matrix is obtained, and the original radar signals are classified into normal radar signals and noisy radar signals according to the segmentation of the original radar signal module, so that the processing of the normal signal module and the processing of the noisy signal module are clear in the subsequent process, the denoising process is simpler, the required time is shorter, the efficiency is higher, then the wavelet coefficient reconstruction module is used to reconstruct the wavelet coefficient processed by the above several modules, and finally the denoised radar signal module is used to obtain the denoised radar signal.

[0112] Through the operation and mutual cooperation of the above 6 modules, the radar signal preprocessing process is optimized, higher signal-to-noise ratio and lower mean square error can be obtained in wavelet denoising, the time required for the denoising process is short, the denoising effect is good, and the efficiency is high.

[0113] The above embodiments are only used to illustrate the technical solutions of the present application, but not limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for radar signal denoising based on adaptive threshold and improved threshold function, characterized in that, The method comprises the following steps: S1: multi-scale wavelet transform is performed on the original radar signal s(n) to obtain wavelet coefficients w under multi-scale j,k , and the logarithmic energy entropy of the matrix of wavelet coefficients w j,k is obtained. S2: split the original radar signal s(n) into normal radar signals x nol with the noisy radar signal x lek ; S3: Process normal radar signal x nol ; S4: processing the noisy radar signal x lek The specific process is as follows: S401: decompose the noisy radar signal x using wavelet'sym3' lek to obtain a matrix of high frequency wavelet coefficients H j,k and a matrix of low frequency wavelet coefficients S402: Calculate adaptive threshold λ for high frequency wavelet coefficients H j,k of matrix S403: using an improved semi-soft threshold function to process the high-frequency wavelet coefficients H combined with the adaptive threshold λ in step S402, the improved semi-soft threshold function being: j,k matrix, the improved semi-soft threshold function being: 0 < z < 1; S5: reconstructing the wavelet coefficients processed by the steps S1-S4; S6: obtaining the denoised radar signal d(n).

2. The method for radar signal de-noising based on adaptive threshold and improved threshold function according to claim 1, characterized in that, In the step S1, the wavelet coefficient w j,k The formula of the logarithmic energy entropy of the matrix of the wavelet coefficient w The total energy 3. The method for radar signal de-noising based on adaptive threshold and improved threshold function according to claim 2, characterized in that, The formula for calculating the adaptive threshold λ in step S402 is: N is the number of high-frequency wavelet coefficients.

4. The method for radar signal de-noising based on adaptive threshold and improved threshold function according to claim 2, characterized in that, The specific process of wavelet transform on the original radar signal in the step S1 is: convoluting wavelet functions with the original radar signal under different scales, wherein the wavelet function is wherein a is a scale factor, b is a translation factor, The function of ψ a,b (t) is to keep the energy of the wavelet function with different a values unchanged.

5. The method for radar signal de-noising based on adaptive threshold and improved threshold function according to claim 1, characterized in that, The step S2 divides the original radar signal s(n) into normal radar signals x nol and noisy radar signals x lek The specific process is as follows: n sampling points are taken on the original radar signal s(n), and the demarcation value for distinguishing the original radar signal into normal radar signals or noisy radar signals is: where x i is the amplitude of the sampling point. After the threshold value h is obtained, a section of the radar signal s(n) is received, and the radar signal less than the threshold value is the normal radar signal x nol , and the radar signal greater than the threshold value is the noisy radar signal x lek .

6. The method for radar signal de-noising based on adaptive threshold and improved threshold function according to claim 1, characterized in that, The step S3 processes the normal radar signal x nol The specific process is as follows: m sampling points are taken continuously as a circular queue, the length of the queue is fixed as the length of m sampling points, according to the first-in first-out principle, that is, a new data is put into the tail of the queue every time sampling, and the original data at the head of the queue is discarded, and the data called out every time is always the arithmetic mean of the m data in the current queue:

7. The method of radar signal de-noising based on adaptive threshold and improved threshold function according to claim 1, characterized in that, In the step S401, the wavelet'sym3' is used to decompose the noisy radar signal x lek The decomposition layers are 5 layers.

8. The method of radar signal de-noising based on adaptive threshold and improved threshold function according to claim 7, characterized in that, In the step S5, the wavelet'sym3' is used to reconstruct the wavelet coefficients processed by the steps S1-S4.

9. The method for radar signal de-noising based on adaptive threshold and improved threshold function according to claim 1, characterized in that, After the step S6, the signal-to-noise ratio SNR and the mean square error MSE are calculated, The formula for calculating the mean square error MSE is: The formula for calculating the signal-to-noise ratio SNR is: Where s(n) represents the original radar signal, and d(n) represents the radar signal after denoising.

10. A radar signal denoising system based on adaptive threshold and improved threshold function, characterized in that, The method comprises the following modules: Wavelet transform module: for the original radar signal s(n) is carried out multi-scale wavelet transform, get the multi-scale wavelet coefficient w j,k And get the logarithmic energy entropy of wavelet coefficient matrix; Segmenting original radar signal module: for segmenting the original radar signal s(n) into normal radar signals x nol with noisy radar signals x lek ; The processing normal signal module is configured to process a normal radar signal x nol ; processing noisy signal module: for processing noisy radar signal x lek ; a wavelet coefficient reconstruction module, configured to reconstruct the wavelet coefficients; an obtaining denoised radar signal module, configured to obtain the denoised radar signal d(n).

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