Denoising method and device for radar echo signal, electronic equipment and storage medium

Abstract

The embodiment of the invention relates to the technical field of computers, and provides a radar echo signal denoising method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a radar echo signal containing noise; a denoising parameter is initialized, differential preprocessing is carried out on the radar echo signal by using the initialized denoising parameter, and a corresponding differential signal is obtained; calculating a gradient vector according to the differential signal, and calculating a scaled projection factor according to the gradient vector; updating the dual signal through iterative computation based on the gradient vector and the scaled projection factor; calculating a noise component in the radar echo signal by using the dual signal; and eliminating noise components from the radar echo signal to obtain an echo signal after noise suppression. According to the embodiment of the invention, high-quality fitting can be carried out on the noise component in the radar echo signal more quickly and accurately, meanwhile, the calculation amount is reduced, and the noise suppression accuracy and the noise suppression efficiency of the radar echo signal are effectively improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of radar signal denoising, and particularly to a method and device for denoising radar echo signals, an electronic device, and a storage medium. Background Art

[0002] Currently, radars have been widely applied in key fields such as military, aerospace, meteorology, ocean, and transportation. A radar discovers targets and determines the spatial positions of the targets by transmitting electromagnetic waves and receiving target electromagnetic waves. The level of noise interference signals directly affects the detection or recognition accuracy of the radar for targets. Lower-power noise means higher target detection or recognition accuracy. Radar echo noise usually comes from receiver noise, ground clutter, sea clutter, and external interference, etc. In recent years, with the development of electronic technology, the electromagnetic environment has become increasingly complex, and the noise interference patterns are more and more, and also more and more complex. Therefore, in order to improve the radar's ability to detect or recognize targets in a high-noise environment, it is necessary to suppress radar noise interference signals.

[0003] In the prior art, there are many methods for denoising radar echo signals, including denoising methods based on time-domain filtering (mean filtering, Gaussian filtering method, median filtering, etc.), denoising methods based on transform domain (Fourier transform, wavelet transform), denoising methods based on total variation, etc.

[0004] The advantages of the denoising method based on time-domain filtering are simple calculation and fast operation speed. The disadvantages are that it is necessary to filter the signals at each time point, and while suppressing the noise, it removes some edge and detail information of the signals, reducing the signal quality. The advantages of the denoising method based on transform domain are that it makes full use of the differences between target signals and noise signals in the frequency domain and wavelet domain for denoising. The disadvantages are that there are assumption condition limitations and detail losses, and it involves complex operations, with a relatively high calculation cost.

[0005] Different from the denoising methods based on time-domain filtering and transform domain, the denoising method based on total variation first establishes a total variation signal denoising model, and then optimizes and approximates the ideal echo signal through gradient iteration on the basis of the total variation signal denoising model. The total variation signal denoising model was proposed by Rudin, Osher, Fatemi (ROF) et al., which uses total variation as the regularization term for signal smoothing, specifically expressed as:

[0006]

[0007] where f ∈ R N represents the noisy echo signal with a time series length of N. u ∈ R N represents the echo signal after noise suppression. λ represents the trade-off parameter and λ > 0. TV(·) represents the discrete total variation, defined as

[0008] Regarding the optimization problem of the total variation signal denoising model, Rudin, Osher, Fatemi (ROF) et al. proposed to solve the parabolic partial differential equation through the time marching algorithm to obtain the solution of the model. Since the single-step computational amount of the time marching algorithm is large and it takes a long time to obtain a satisfactory restored image, this algorithm is only effective when solving low-precision solutions. Chambolle analyzed the Lagrange multipliers of the ROF dual model and proposed a semi-implicit gradient descent algorithm. This algorithm is simple, easy to implement, and has a fast convergence rate, and has become a popular algorithm for solving medium-precision solutions. Zhu and Chan analyzed the relationship between the primal and dual variables and proposed a hybrid gradient algorithm that alternately solves the primal and dual variables.

[0009] The advantage of the denoising method based on total variation is that it utilizes the smooth characteristics of the signal and can well protect the edges while removing noise. The disadvantage is that the computational amount is large and the efficiency is low during the model optimization process. Summary of the Invention

[0010] The present disclosure aims to at least solve one of the problems existing in the prior art, and provides a method and device for denoising radar echo signals, an electronic device, and a storage medium.

[0011] In one aspect of the present disclosure, a method for denoising radar echo signals is provided. The denoising method includes:

[0012] Obtain a radar echo signal containing noise;

[0013] Initialize denoising parameters, and perform differential preprocessing on the radar echo signal by using the initialized denoising parameters to obtain a corresponding differential signal;

[0014] Calculate a gradient vector according to the differential signal, and calculate a scaled projection factor according to the gradient vector;

[0015] Update the dual signal through iterative calculation based on the gradient vector and the scaled projection factor;

[0016] Calculate the noise component in the radar echo signal by using the dual signal;

[0017] Eliminate the noise component from the radar echo signal to obtain a noise-suppressed echo signal.

[0018] Optionally, the performing differential preprocessing on the radar echo signal by using the initialized denoising parameters to obtain a corresponding differential signal includes:

[0019] Calculate the i-th element in the differential signal according to the following formula

[0020]

[0021] Among them, λ represents a trade-off parameter; f i represents the element corresponding to the i-th time point in the radar echo signal; f i+1 represents the element corresponding to the (i + 1)-th time point in the radar echo signal; N represents the length of the time series of the radar echo signal.

[0022] Optionally, calculating the gradient vector according to the difference signal includes:

[0023] Calculating the gradient vector according to the following formula:

[0024]

[0025] Among them, g k represents the gradient vector at the k-th iteration; represents the difference signal; gp k represents the filtered signal obtained by Sobel filtering of the dual signal at the k-th iteration and its i-th element is expressed as:

[0026]

[0027] Among them, represents the element corresponding to the first time point in the dual signal at the k-th iteration; represents the element corresponding to the second time point in the dual signal at the k-th iteration; represents the element corresponding to the i-th time point in the dual signal at the k-th iteration; represents the element corresponding to the (i - 1)-th time point in the dual signal at the k-th iteration; represents the element corresponding to the (i + 1)-th time point in the dual signal at the k-th iteration.

[0028] Optionally, calculating the scaled projection factor according to the gradient vector includes:

[0029] Calculating the i-th element θ of the scaled projection factor at the k-th iteration according to the following formula i k :

[0030]

[0031] Among them, α represents a scaling factor; is the first intermediate variable at the k-th iteration and τ represents a step size and represents the gradient vector g at the k-th iteration kthe i-th element in is the second intermediate variable at the k-th iteration and

[0032] Optionally, updating the dual signal through iterative calculation based on the gradient vector and the scaled projection factor includes:

[0033] Updating the dual signal according to the following formula:

[0034]

[0035] where represents the element corresponding to the i-th time point in the dual signal at the (k + 1)-th iteration.

[0036] Optionally, calculating the noise component in the radar echo signal by using the dual signal includes:

[0037] Calculating the i-th element q in the noise component according to the following formula i :

[0038]

[0039] where represents the element corresponding to the (N - 1)-th time point in the dual signal at the k-th iteration.

[0040] Another aspect of the present disclosure provides a denoising device for radar echo signals, the denoising device includes:

[0041] An acquisition module, configured to acquire a radar echo signal containing noise;

[0042] A difference module, configured to initialize denoising parameters, and perform difference preprocessing on the radar echo signal by using the initialized denoising parameters to obtain a corresponding difference signal;

[0043] A calculation module, configured to calculate a gradient vector according to the difference signal, and calculate a scaled projection factor according to the gradient vector;

[0044] An iteration module, configured to update the dual signal through iterative calculation based on the gradient vector and the scaled projection factor;

[0045] A noise module, configured to calculate the noise component in the radar echo signal by using the dual signal;

[0046] An elimination module, configured to eliminate the noise component from the radar echo signal to obtain an echo signal after noise suppression.

[0047] Another aspect of the present disclosure provides an electronic device, including:

[0048] At least one processor; and,

[0049] A memory communicatively connected to the at least one processor; wherein,

[0050] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the denoising method of the radar echo signal described above.

[0051] Another aspect of the present disclosure provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the denoising method of the radar echo signal described above is implemented.

[0052] Another aspect of the present disclosure provides a computer program product including a computer program, and when the computer program is executed by a processor, the denoising method of the radar echo signal described above is implemented.

[0053] Compared with the prior art, the present disclosure can more quickly and accurately perform high-quality fitting on the noise components in the radar echo signal based on the scaling projection factor and the gradient vector, while reducing the computational amount, effectively improving the noise suppression accuracy and noise suppression efficiency of the radar echo signal. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] One or more embodiments are exemplarily illustrated by the pictures in the corresponding drawings. These exemplary illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are represented as similar elements, unless otherwise stated, and the drawings in the figures do not constitute a proportional limitation.

[0055] Figure 1 It is a flowchart of a denoising method for a radar echo signal provided by an embodiment of the present disclosure;

[0056] Figure 2 It is a schematic diagram provided by another embodiment of the present disclosure;

[0057] Figure 3 It is a schematic structural diagram of a denoising device for a radar echo signal provided by another embodiment of the present disclosure;

[0058] Figure 4 It is a schematic structural diagram of an electronic device provided by another embodiment of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0059] To make the objectives, technical solutions, and advantages of the embodiments of the present disclosure clearer, the embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. However, those of ordinary skill in the art can understand that in the embodiments of the present disclosure, many technical details are provided to help readers better understand the present disclosure. However, even without these technical details and various changes and modifications based on the following embodiments, the technical solutions claimed in the present disclosure can still be implemented. The following division of each embodiment is for convenience of description and should not constitute any limitation on the specific implementation manner of the present disclosure. The various embodiments can be combined and cross-referenced with each other on the premise of not being contradictory.

[0060] One embodiment of the present disclosure relates to a method for denoising radar echo signals, and its process is as Figure 1 shown, including steps S110 to S160.

[0061] Step S110: Obtain a radar echo signal containing noise.

[0062] Specifically, the radar echo signal containing noise with a time series length of N can be expressed as f ∈ R N , where R N represents an N-dimensional real vector space. That is to say, the radar echo signal f containing noise is an N-dimensional real vector.

[0063] Step S120: Initialize the denoising parameters, and perform differential preprocessing on the radar echo signal using the initialized denoising parameters to obtain the corresponding differential signal.

[0064] Specifically, the denoising parameters may include, but are not limited to, a trade-off parameter λ, a step size τ, a dual signal p k ∈ R N , a scaling factor α, and an iteration number k. When initializing the denoising parameters, initial values can be set for each denoising parameter respectively. For example, the initial value of the trade-off parameter λ can be set to 10. The initial value of the step size τ can be set to The initial value of the dual signal p k can be set to p k = 0. The initial value of the scaling factor α can be set to 0.5. The starting value of the iteration number k can be set to 1, and the ending value can be set to 100. Of course, the above denoising parameters can also be set to other values, as long as λ > 1, α ∈ (0, 1), and the ending value of the iteration number k is a positive integer.

[0065] Exemplarily, in step S120, performing differential preprocessing on the radar echo signal using the initialized denoising parameters to obtain the corresponding differential signal includes: calculating the i-th element in the differential signal according to the following formula

[0066] Among them, λ represents the trade-off parameter. f i represents the element corresponding to the i-th time point in the radar echo signal. f i+1 represents the element corresponding to the (i + 1)-th time point in the radar echo signal. N represents the length of the time series of the radar echo signal.

[0067] Step S130: Calculate the gradient vector according to the differential signal, and calculate the scaled projection factor according to the gradient vector.

[0068] Specifically, in step S130, the gradient vector can be first calculated according to the dual signal Sobel filtering and the differential signal obtained in step S120, and then, in combination with the scaling factor and the step size, the scaled projection factor is calculated according to the calculated gradient vector.

[0069] Exemplarily, in step S130, calculating the gradient vector according to the differential signal includes: calculating the gradient vector according to the following formula:

[0070] where, g k represents the gradient vector at the k-th iteration. represents the differential signal. gp k represents the filtered signal obtained by dual signal Sobel filtering at the k-th iteration, and its i-th element is expressed as:

[0071]

[0072] where, represents the element corresponding to the first time point in the dual signal at the k-th iteration. represents the element corresponding to the second time point in the dual signal at the k-th iteration. represents the element corresponding to the i-th time point in the dual signal at the k-th iteration. represents the element corresponding to the (i - 1)-th time point in the dual signal at the k-th iteration. represents the element corresponding to the (i + 1)-th time point in the dual signal at the k-th iteration.

[0073] Exemplarily, in step S130, calculating the scaled projection factor according to the gradient vector includes: calculating the i-th element θ of the scaled projection factor at the k-th iteration according to the following formula i k :

[0074]

[0075] where, α represents the scaling factor. is the first intermediate variable at the k-th iteration and τ represents the step size and Denote the \(i\)-th element of the gradient vector \(\mathbf{g}\) at the \(k\)-th iteration. k in is the second intermediate variable at the \(k\)-th iteration and

[0076] Step S140: Update the dual signal through iterative calculation based on the gradient vector and the scaled projection factor.

[0077] Specifically, the termination value of the iteration number \(k\) is used to indicate the total number of iterative calculations. For example, when the termination value of the iteration number \(k\) is set to 100, step S140 needs to perform 100 iterative calculations.

[0078] Exemplarily, step S140 includes: updating the dual signal according to the following formula:

[0079] where denotes the element corresponding to the \(i\)-th time point in the dual signal at the \((k + 1)\)-th iteration.

[0080] Step S150: Calculate the noise component in the radar echo signal using the dual signal.

[0081] Specifically, after the dual signal is updated, step S150 can calculate the noise component in the radar echo signal using the updated dual signal.

[0082] Exemplarily, step S150 includes: calculating the \(i\)-th element \(q\) of the noise component according to the following formula i :

[0083]

[0084] where denotes the element corresponding to the \((N - 1)\)-th time point in the dual signal at the \(k\)-th iteration.

[0085] Step S160: Eliminate the noise component from the radar echo signal to obtain the echo signal after noise suppression.

[0086] Specifically, denote the echo signal after noise suppression as \(u\) and the noise component as \(q\), then \(u=\mathbf{f}-q\).

[0087] The denoising method of the radar echo signal provided by the embodiments of the present disclosure, compared with the prior art, can more quickly and accurately perform high-quality fitting on the noise component in the radar echo signal based on the scaled projection factor and the gradient vector, while reducing the computational amount, effectively improving the noise suppression accuracy and noise suppression efficiency of the radar echo signal.

[0088] To enable those skilled in the art to better understand the above embodiments, a specific example is given below for illustration.

[0089] Combined with Figure 2 , a method for denoising radar echo signals, comprising the following steps 1 to 3.

[0090] Step 1: Obtain the echo signal with noise, that is, the radar echo signal f ∈ R with noise and a time series length of N N , and perform initialization of denoising parameters and differential preprocessing of the echo signal, including steps 1.1 and 1.2.

[0091] Step 1.1: Initialization of denoising parameters: Let the trade-off parameter λ = 10, the step size the dual signal p k = 0 ∈ R N , the scaling factor α = 0.5, the initial value of the iteration number k, that is, the initial iteration number, is 1, and the termination value of the iteration number k, that is, the termination iteration number K = 100.

[0092] Step 1.2: Differential preprocessing of the echo signal: Its input, output, and process are as follows respectively:

[0093] Input: The radar echo signal f ∈ R with noise N and the trade-off parameter λ;

[0094] Output: The differential signal

[0095] Process:

[0096] Step 2: Calculate the gradient vector and the scaled projection factor, and update the dual signal through iterative calculation, including steps 2.1 to 2.5.

[0097] Step 2.1: Sobel filtering of the dual signal: Its input, output, and process are as follows respectively:

[0098] Input: The dual signal p k ∈ R N ;

[0099] Output: The filtered signal gp k ∈ R N ;

[0100] Process:

[0101] Step 2.2: Calculation of the gradient vector: Its input, output, and process are as follows respectively:

[0102] Input: The filtered signal gp k ∈ R N and the differential signal

[0103] Output: Gradient vector g k ∈R N ;

[0104] Process:

[0105] Step 2.3: Scaling calculation: Its input, output, and process are as follows:

[0106] Input: Scaling factor α, gradient vector g k ∈R N , dual signal p k ∈R N ;

[0107] Output: Scaled projection factor θ k ∈R N ;

[0108] Process:

[0109] Wherein,

[0110] Step 2.4: Dual signal update: Its input, output, and process are as follows:

[0111] Input: Dual signal p k ∈R N , gradient vector g k ∈R N , scaled projection factor θ k ∈R N ;

[0112] Output: Updated dual signal p k+1 ∈R N ;

[0113] Process:

[0114] Step 2.5: Determine whether the iteration number k satisfies k = K. If k = K is satisfied, go to Step 3. If k = K is not satisfied, let and k = k + 1, then go to Step 2.1.

[0115] Step 3: Calculate the noise component using the dual signal finally updated in Step 2, eliminate it from the radar echo signal, and obtain the echo signal after noise suppression, including Step 3.1 and Step 3.2.

[0116] Step 3.1: Calculate the noise component: Its input, output, and process are as follows:

[0117] Input: Dual signal p k ∈R N , trade-off parameter λ;

[0118] Output: Noise component q ∈ R N ;

[0119] Process:

[0120] Step 3.2: Noise reduction: Its input, output, and process are respectively:

[0121] Input: Radar echo signal f ∈ R containing noise N and noise component q ∈ R N ;

[0122] Output: Echo signal u ∈ R after noise suppression N ;

[0123] Process: u = f - q.

[0124] Another embodiment of the present disclosure relates to a denoising device for radar echo signals, as Figure 3 shown, including an acquisition module 310, a difference module 320, a calculation module 330, an iteration module 340, a noise module 350, and an elimination module 360.

[0125] The acquisition module 310 is used to acquire a radar echo signal containing noise.

[0126] The difference module 320 is used to initialize the denoising parameters, and perform differential preprocessing on the radar echo signal using the initialized denoising parameters to obtain the corresponding differential signal.

[0127] The calculation module 330 is used to calculate the gradient vector according to the differential signal, and calculate the scaled projection factor according to the gradient vector.

[0128] The iteration module 340 is used to update the dual signal through iterative calculation based on the gradient vector and the scaled projection factor.

[0129] The noise module 350 is used to calculate the noise component in the radar echo signal using the dual signal.

[0130] The elimination module 360 is used to eliminate the noise component from the radar echo signal to obtain the echo signal after noise suppression.

[0131] For the specific implementation method of the denoising device for radar echo signals provided by the embodiments of the present disclosure, reference can be made to the denoising method for radar echo signals provided by the embodiments of the present disclosure, which will not be elaborated here.

[0132] The denoising device for radar echo signals provided by the embodiments of the present disclosure can, compared with the prior art, more quickly and accurately perform high-quality fitting on the noise components in the radar echo signals based on the scaled projection factor and the gradient vector, while reducing the computational amount, effectively improving the noise suppression accuracy and the noise suppression efficiency of the radar echo signals.

[0133] Another embodiment of the present disclosure relates to an electronic device, as Figure 4 shown, including:

[0134] At least one processor 401; and,

[0135] A memory 402 communicatively connected to the at least one processor 401; wherein,

[0136] The memory 402 stores instructions executable by the at least one processor 401, and the instructions are executed by the at least one processor 401 to enable the at least one processor 401 to execute the denoising method of the radar echo signals described in the above embodiments.

[0137] Wherein, the memory and the processor are connected by a bus. The bus can include any number of interconnected buses and bridges, and the bus connects various circuits of one or more processors and the memory together. The bus can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art, and thus will not be further described herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be one element or multiple elements, such as multiple receivers and transmitters, and provides a unit for communicating with various other devices on the transmission medium. The data processed by the processor is transmitted on the wireless medium through the antenna. Further, the antenna also receives data and transmits the data to the processor.

[0138] The processor is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interface, voltage regulation, power management, and other control functions. The memory can be used to store the data used by the processor when performing operations.

[0139] Another embodiment of the present disclosure relates to a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, it implements the denoising method of the radar echo signals described in the above embodiments.

[0140] That is, those skilled in the art can understand that all or part of the steps in the methods described in the above embodiments can be completed by instructing relevant hardware through a program. This program is stored in a storage medium and includes several instructions to enable a device (which can be a single-chip microcomputer, a chip, etc.) or a processor to execute all or part of the steps in the methods described in various embodiments of the present disclosure. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs.

[0141] Another embodiment of the present disclosure relates to a computer program product, including a computer program, which when executed by a processor, implements the method for denoising radar echo signals described in the above embodiments.

[0142] Those of ordinary skill in the art can understand that the above embodiments are specific embodiments for implementing the present disclosure, and in practical applications, various changes can be made to them in form and details without departing from the spirit and scope of the present disclosure.

Claims

1. A method for denoising a radar echo signal, characterized in that: The denoising method comprises: Acquire radar echo signals containing noise; Initializing denoising parameters, and performing differential preprocessing on the radar echo signal using the initialized denoising parameters to obtain a corresponding differential signal; Calculating a gradient vector according to the differential signal, and calculating a scaled projection factor according to the gradient vector; Based on the gradient vector and the scaled projection factor, updating the dual signal by iterative calculation; Calculating the noise component in the radar echo signal using the dual signal; The noise component is eliminated from the radar echo signal to obtain a noise-suppressed echo signal.

2. The denoising method according to claim 1, characterized in that: The step of performing differential preprocessing on the radar echo signal using the initialized denoising parameters to obtain a corresponding differential signal includes: According to the following formula, the i-th element in the differential signal is calculated: Among them, λ represents the trade-off parameter; f i represents the element corresponding to the i-th time point in the radar echo signal; f i+1 represents the element corresponding to the i+1th time point in the radar echo signal; N represents the time series length of the radar echo signal.

3. The denoising method according to claim 2, characterized in that: The step of calculating the gradient vector according to the differential signal comprises: The gradient vector is calculated according to the following formula: Among them, g k represents the gradient vector at the kth iteration; represents the differential signal; gp k represents the filtered signal obtained by Sobel filtering of the dual signal at the kth iteration and its i-th element is expressed as: in, Represents the element corresponding to the first time point in the dual signal at the kth iteration; Represents the element corresponding to the second time point in the dual signal at the kth iteration; represents the element corresponding to the i-th time point in the dual signal at the k-th iteration; represents the element corresponding to the i-1th time point in the dual signal at the kth iteration; Represents the element corresponding to the i+1th time point in the dual signal at the kth iteration.

4. The denoising method according to claim 3, characterized in that: The step of calculating a scaled projection factor according to the gradient vector comprises: The i-th element θ in the scaled projection factor at the k-th iteration is calculated according to the following formula: i k : Where α represents the scaling factor; is the first intermediate variable at the kth iteration and τ represents the step size and Represents the gradient vector g at the kth iteration k The i-th element in ; is the second intermediate variable at the kth iteration and 5. The denoising method according to claim 4, characterized in that: The updating of the dual signal by iterative calculation based on the gradient vector and the scaled projection factor comprises: Update the dual signal according to the following formula: in, Represents the element corresponding to the i-th time point in the dual signal at the k+1-th iteration.

6. The denoising method according to claim 5, characterized in that: The step of calculating the noise component in the radar echo signal by using the dual signal comprises: According to the following formula, the i-th element q in the noise component is calculated: i : in, Represents the element corresponding to the N-1th time point in the dual signal at the kth iteration.

7. A radar echo signal denoising device, characterized in that: The denoising device comprises: An acquisition module, used for acquiring radar echo signals containing noise; A differential module, used to initialize denoising parameters, and perform differential preprocessing on the radar echo signal using the initialized denoising parameters to obtain a corresponding differential signal; A calculation module, configured to calculate a gradient vector according to the differential signal, and calculate a scaled projection factor according to the gradient vector; An iteration module, configured to update a dual signal by iterative calculation based on the gradient vector and the scaled projection factor; A noise module, used for calculating the noise component in the radar echo signal using the dual signal; The elimination module is used to eliminate the noise component from the radar echo signal to obtain a noise-suppressed echo signal.

8. An electronic device, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the radar echo signal denoising method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the radar echo signal denoising method according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the radar echo signal denoising method according to any one of claims 1 to 6 is implemented.

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