CN111649819A - Transformer state vibration and sound detection signal filtering method and system using iteration soft threshold - Google Patents

Abstract

The embodiment of the invention discloses a method and a system for filtering a transformer state vibration and sound detection signal by using an iterative soft threshold, wherein the method comprises the following steps: step 101, acquiring a signal sequence S acquired according to a time sequence; 102, solving N eigenvalues of an average matrix B; step 103, performing truncation processing on all the characteristic values to obtain updated values of all the characteristic values; 104, solving an initial value of a soft threshold sequence x; step 105 of obtaining an iteration step t_k(ii) a Step 106 of obtaining a judgment vector g_k(ii) a Step 107 judges said judgment vector g_kThe jth element of (1)

Whether the value is larger than the judgment threshold lambda or not is judged, and a first judgment result is obtained; step 108, calculating the k step value x of the soft threshold sequence x_k(ii) a Step 109, calculating an adjacent error e; step 110 for judgmentWhether the adjacent error e is greater than or equal to a preset threshold value₀Obtaining a second judgment result; step 111 records the noise-filtered signal sequence S_NEWAnd is specifically S_NEW＝x_k。

Description

Transformer state vibration and sound detection signal filtering method and system using iteration soft threshold

Technical Field

The invention relates to the field of electric power, in particular to a method and a system for filtering a vibration and sound detection signal of a transformer.

Background

With the high-speed development of the smart grid, the safe and stable operation of the power equipment is particularly important. At present, the detection of the operating state of the power equipment with ultrahigh voltage and above voltage grades, especially the detection of the abnormal state, is increasingly important and urgent. As an important component of an electric power system, a power transformer is one of the most important electrical devices in a substation, and its reliable operation is related to the safety of a power grid. Generally, the abnormal state of the transformer can be divided into core abnormality and winding abnormality. The core abnormality is mainly represented by core saturation, and the winding abnormality generally includes winding deformation, winding looseness and the like.

The basic principle of the transformer abnormal state detection is to extract each characteristic quantity in the operation of the transformer, analyze, identify and track the characteristic quantity so as to monitor the abnormal operation state of the transformer. The detection method can be divided into invasive detection and non-invasive detection according to the contact degree; the detection can be divided into live detection and power failure detection according to whether the shutdown detection is needed or not; the method can be classified into an electrical quantity method, a non-electrical quantity method, and the like according to the type of the detected quantity. In comparison, the non-invasive detection has strong transportability and is more convenient to install; the live detection does not affect the operation of the transformer; the non-electric quantity method is not electrically connected with the power system, so that the method is safer. The current common detection methods for the operation state of the transformer include a pulse current method and an ultrasonic detection method for detecting partial discharge, a frequency response method for detecting winding deformation, a vibration detection method for detecting mechanical and electrical faults, and the like. The detection methods mainly detect the insulation condition and the mechanical structure condition of the transformer, wherein the detection of the vibration signal (vibration sound) of the transformer is the most comprehensive, and the fault and the abnormal state of most transformers can be reflected.

In the running process of the transformer, the magnetostriction of the iron core silicon steel sheets and the vibration caused by the winding electrodynamic force can radiate vibration sound signals with different amplitudes and frequencies to the periphery. When the transformer normally operates, uniform low-frequency noise is emitted outwards; if the sound is not uniform, it is not normal. The transformer can make distinctive sounds in different running states, and the running state of the transformer can be mastered by detecting the sounds made by the transformer. It is worth noting that the detection of the sound emitted by the transformer in different operating states not only can detect a plurality of serious faults causing the change of the electrical quantity, but also can detect a plurality of abnormal states which do not endanger the insulation and do not cause the change of the electrical quantity, such as the loosening of internal and external parts of the transformer, and the like.

Because the vibration sound detection method utilizes the vibration signal sent by the transformer, the vibration sound detection method is easily influenced by environmental noise, and therefore, how to effectively identify the vibration sound and the noise is the key for success of the method.

Disclosure of Invention

In the process of detecting the switching event, the actually measured power signal used is often affected by noise, and the detection of the switching event cannot be correctly performed by using the imperfect power signal. Therefore, how to effectively reconstruct the incomplete power signal and filter the influence of noise is the key to the success of the method. The existing common method has insufficient attention to the problem, and no effective measure is taken to solve the problem.

The invention aims to provide a method and a system for filtering a transformer state vibration and sound detection signal by using an iterative soft threshold. The method has better robustness and simpler calculation.

In order to achieve the purpose, the invention provides the following scheme:

a transformer state vibro-acoustic detection signal filtering method using iterative soft threshold, comprising:

step 101, acquiring a signal sequence S acquired according to a time sequence;

step 102 finds N eigenvalues γ of the averaging matrix B₁,γ₂,···,γ_N. Wherein N is the length of the signal sequence S; the solving formula of the average matrix B is B ═ S-m₀]^T[S-m₀]. Wherein m is₀Is the mean of the signal sequence S;

step 103 is to apply all the characteristic values gamma₁,γ₂,···,γ_NAnd performing truncation to obtain the updated values of all the characteristic values. The cutting method comprises the following steps: if the ith said eigenvalue gamma_iLess than sigma₀ln (SNR +1)/N, the ith characteristic value gamma_iReset to 0. Wherein σ₀Is the mean square error of the signal sequence S; the SNR is the signal-to-noise ratio of the signal sequence S; i is the serial number of the characteristic value, and the value range is i ═ 1,2, ·, N;

step 104 of finding softInitial value x of threshold sequence x₀The formula is found to be x₀＝S-m₀；

Step 105 of determining an iteration step t_kThe formula is obtained as

Wherein Δ T is a sampling interval of the signal sequence S;

step 106 of obtaining a judgment vector g_kThe formula is found to be g_k＝x_k-1+t_kA^T(S-Ax_k-1) (ii) a Wherein A is a system matrix, and the calculation formula of the system matrix A is A ═ diag [ gamma ] (gamma)_i]_N×N；

Step 107 judges said judgment vector g_kThe jth element of (1)

And whether the value is larger than the judgment threshold lambda or not is judged to obtain a first judgment result. If the first judgment result shows the jth element

If the value is larger than the judgment threshold lambda, the jth element

Reset to

If the first judgment result shows the jth element

Equal to the judgment threshold lambda, then the jth element

Reset to 0; if the first judgment result shows the jth element

Is less than the judgment threshold lambda, thenThe j element

Reset to

Wherein the formula for calculating the judgment threshold λ is λ ═ σ%₀ln[SNR+1](ii) a j is the element serial number, and the value range of j is 1,2, ·, N;

step 108, calculating the k step value x of the soft threshold sequence x_kThe formula is found to be x_k＝g_k；

Step 109 calculates the adjacent error e, and the formula is e ═ x_k-x_k-1|；

Step 110 determines whether the adjacent error e is greater than or equal to a preset threshold₀And obtaining a second judgment result. If the second judgment result shows that the adjacent error e is greater than or equal to the preset threshold value₀Returning to the step 105, the step 106, the step 107, the step 108, the step 109 and the step 110, and adding 1 to the value of the iterative control parameter k; until the second judgment result shows that the approximation error e is smaller than the preset threshold value₀The preset threshold ∈₀Is composed of₀＝0.001；

Step 111 records the noise-filtered signal sequence S_NEWAnd is specifically S_NEW＝x_k。

A transformer state vibro-acoustic detection signal filtering system utilizing iterative soft thresholds, comprising:

the module 201 acquires a signal sequence S acquired in time sequence;

the module 202 finds the N eigenvalues γ of the averaging matrix B₁,γ₂,···,γ_N. Wherein N is the length of the signal sequence S; the solving formula of the average matrix B is B ═ S-m₀]^T[S-m₀]. Wherein m is₀Is the mean of the signal sequence S;

the module 203 processes all the characteristic values gamma₁,γ₂,···,γ_NAnd performing truncation to obtain the updated values of all the characteristic values. The cutting method comprises the following steps: if the ith said eigenvalue gamma_iLess than sigma₀ln (SNR +1)/N, the ith characteristic value gamma_iReset to 0. Wherein σ₀Is the mean square error of the signal sequence S; the SNR is the signal-to-noise ratio of the signal sequence S; i is the serial number of the characteristic value, and the value range is i ═ 1,2, ·, N;

module 204 finds the initial value x of the soft threshold sequence x₀The formula is found to be x₀＝S-m₀；

Module 205 finds the iteration step t_kThe formula is obtained as

Wherein Δ T is a sampling interval of the signal sequence S;

module 206 finds the decision vector g_kThe formula is found to be g_k＝x_k-1+t_kA^T(S-Ax_k-1) (ii) a Wherein A is a system matrix, and the calculation formula of the system matrix A is A ═ diag [ gamma ] (gamma)_i]_N×N；

The module 207 judges the judgment vector g_kThe jth element of (1)

And whether the value is larger than the judgment threshold lambda or not is judged to obtain a first judgment result. If the first judgment result shows the jth element

If the value is larger than the judgment threshold lambda, the jth element

Reset to

If the first judgment result shows the jth element

Equal to the judgment threshold lambda, then the jth element

Reset to 0; if the first judgment result shows the jth element

If the value is less than the judgment threshold lambda, the jth element

Reset to

Wherein the formula for calculating the judgment threshold λ is λ ═ σ%₀ln[SNR+1](ii) a j is the element serial number, and the value range of j is 1,2, ·, N;

module 208 finds the kth step x of the sequence of soft thresholds x_kThe formula is found to be x_k＝g_k；

The block 209 finds the adjacent error e, which is given by the equation e ═ x_k-x_k-1|；

The module 210 determines whether the adjacent error e is greater than or equal to a predetermined threshold₀And obtaining a second judgment result. If the second judgment result shows that the adjacent error e is greater than or equal to the preset threshold value₀Then returning to said module 205, said module 206, said module 207, said module 208, said module 209 and said module 210, the value of said iterative control parameter k is increased by 1; until the second judgment result shows that the approximation error e is smaller than the preset threshold value₀The preset threshold ∈₀Is composed of₀＝0.001；

Module 211 records the noise-filtered signal sequence S_NEWAnd is specifically S_NEW＝x_k。

According to the specific embodiment provided by the invention, the invention discloses the following technical effects:

in the process of detecting the switching event, the actually measured power signal used is often affected by noise, and the detection of the switching event cannot be correctly performed by using the imperfect power signal. Therefore, how to effectively reconstruct the incomplete power signal and filter the influence of noise is the key to the success of the method. The existing common method has insufficient attention to the problem, and no effective measure is taken to solve the problem.

The invention aims to provide a method and a system for filtering a transformer state vibration and sound detection signal by using an iterative soft threshold. The method has better robustness and simpler calculation.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.

FIG. 1 is a schematic flow diagram of the process of the present invention;

FIG. 2 is a schematic flow chart of the system of the present invention;

FIG. 3 is a flow chart illustrating an embodiment of the present invention.

Detailed Description

The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.

FIG. 1 is a schematic flow chart of a method for filtering a transformer state vibration and sound detection signal by using an iterative soft threshold

Fig. 1 is a schematic flow chart of a transformer state vibro-acoustic detection signal filtering method using iterative soft threshold according to the present invention. As shown in fig. 1, the method for filtering a transformer state vibro-acoustic detection signal by using an iterative soft threshold specifically includes the following steps:

step 101, acquiring a signal sequence S acquired according to a time sequence;

step 102 finds N eigenvalues γ of the averaging matrix B₁,γ₂,···,γ_N. Wherein N is the length of the signal sequence S; the solving formula of the average matrix B is B ═ S-m₀]^T[S-m₀]. Wherein m is₀Is the mean of the signal sequence S;

step 103 is to apply all the characteristic values gamma₁,γ₂,···,γ_NAnd performing truncation to obtain the updated values of all the characteristic values. The cutting method comprises the following steps: if the ith said eigenvalue gamma_iLess than sigma₀ln (SNR +1)/N, the ith characteristic value gamma_iReset to 0. Wherein σ₀Is the mean square error of the signal sequence S; the SNR is the signal-to-noise ratio of the signal sequence S; i is the serial number of the characteristic value, and the value range is i ═ 1,2, ·, N;

step 104 finds the initial value x of the soft threshold sequence x₀The formula is found to be x₀＝S-m₀；

Step 105 of determining an iteration step t_kThe formula is obtained as

Wherein Δ T is a sampling interval of the signal sequence S;

step 106 of obtaining a judgment vector g_kThe formula is found to be g_k＝x_k-1+t_kA^T(S-Ax_k-1) (ii) a Wherein A is a system matrix, and the calculation formula of the system matrix A is A ═ diag [ gamma ] (gamma)_i]_N×N；

Step 107 judges said judgment vector g_kThe jth element of (1)

And whether the value is larger than the judgment threshold lambda or not is judged to obtain a first judgment result. If the first judgment result shows the jth element

If the value is larger than the judgment threshold lambda, the jth element

Reset to

If the first judgment result shows the jth element

Equal to the judgment threshold lambda, then the jth element

Reset to 0; if the first judgment result shows the jth element

If the value is less than the judgment threshold lambda, the jth element

Reset to

Wherein the formula for calculating the judgment threshold λ is λ ═ σ%₀ln[SNR+1](ii) a j is the element serial number, and the value range of j is 1,2, ·, N;

step 108, calculating the k step value x of the soft threshold sequence x_kThe formula is found to be x_k＝g_k；

Step 109, calculating the adjacent error e, and calculating the formulaIs e ═ x_k-x_k-1|；

Step 110 determines whether the adjacent error e is greater than or equal to a preset threshold₀And obtaining a second judgment result. If the second judgment result shows that the adjacent error e is greater than or equal to the preset threshold value₀Returning to the step 105, the step 106, the step 107, the step 108, the step 109 and the step 110, and adding 1 to the value of the iterative control parameter k; until the second judgment result shows that the approximation error e is smaller than the preset threshold value₀The preset threshold ∈₀Is composed of₀＝0.001；

Step 111 records the noise-filtered signal sequence S_NEWAnd is specifically S_NEW＝x_k。

FIG. 2 structural intent of a transformer state vibro-acoustic detection signal filtering system using iterative soft threshold

Fig. 2 is a schematic structural diagram of a transformer state vibro-acoustic detection signal filtering system using iterative soft threshold according to the present invention. As shown in fig. 2, the transformer state vibro-acoustic detection signal filtering system using the iterative soft threshold comprises the following structures:

the module 201 acquires a signal sequence S acquired in time sequence;

the module 202 finds the N eigenvalues γ of the averaging matrix B₁,γ₂,···,γ_N. Wherein N is the length of the signal sequence S; the solving formula of the average matrix B is B ═ S-m₀]^T[S-m₀]. Wherein m is₀Is the mean of the signal sequence S;

the module 203 processes all the characteristic values gamma₁,γ₂,···,γ_NAnd performing truncation to obtain the updated values of all the characteristic values. The cutting method comprises the following steps: if the ith said eigenvalue gamma_iLess than sigma₀ln (SNR +1)/N, the ith characteristic value gamma_iReset to 0. Wherein σ₀Is the mean square error of the signal sequence S; the SNR is the signal-to-noise ratio of the signal sequence S; i is the serial number of the characteristic value, and the value range is i-1, 2, ··,N；

Module 204 finds the initial value x of the soft threshold sequence x₀The formula is found to be x₀＝S-m₀；

Module 205 finds the iteration step t_kThe formula is obtained as

Wherein Δ T is a sampling interval of the signal sequence S;

module 206 finds the decision vector g_kThe formula is found to be g_k＝x_k-1+t_kA^T(S-Ax_k-1) (ii) a Wherein A is a system matrix, and the calculation formula of the system matrix A is A ═ diag [ gamma ] (gamma)_i]_N×N；

The module 207 judges the judgment vector g_kThe jth element of (1)

And whether the value is larger than the judgment threshold lambda or not is judged to obtain a first judgment result. If the first judgment result shows the jth element

If the value is larger than the judgment threshold lambda, the jth element

Reset to

If the first judgment result shows the jth element

Equal to the judgment threshold lambda, then the jth element

Reset to 0; if the first judgment result shows the jth element

If the value is less than the judgment threshold lambda, the jth element

Reset to

Wherein the formula for calculating the judgment threshold λ is λ ═ σ%₀ln[SNR+1](ii) a j is the element serial number, and the value range of j is 1,2, ·, N;

module 208 finds the kth step x of the sequence of soft thresholds x_kThe formula is found to be x_k＝g_k；

The block 209 finds the adjacent error e, which is given by the equation e ═ x_k-x_k-1|；

The module 210 determines whether the adjacent error e is greater than or equal to a predetermined threshold₀And obtaining a second judgment result. If the second judgment result shows that the adjacent error e is greater than or equal to the preset threshold value₀Then returning to said module 205, said module 206, said module 207, said module 208, said module 209 and said module 210, the value of said iterative control parameter k is increased by 1; until the second judgment result shows that the approximation error e is smaller than the preset threshold value₀The preset threshold ∈₀Is composed of₀＝0.001；

Module 211 records the noise-filtered signal sequence S_NEWAnd is specifically S_NEW＝x_k。

The following provides an embodiment for further illustrating the invention

FIG. 3 is a flow chart illustrating an embodiment of the present invention. As shown in fig. 3, the method specifically includes the following steps:

step 301, acquiring a signal sequence S acquired according to a time sequence;

step 302 finds N eigenvalues γ of the averaging matrix B₁,γ₂,···,γ_N. Wherein N is the length of the signal sequence S; the solving formula of the average matrix B is B ═ S-m₀]^T[S-m₀]. Wherein m is₀Is the mean of the signal sequence S;

step 303 is to apply all the characteristic values gamma₁,γ₂,···,γ_NAnd performing truncation to obtain the updated values of all the characteristic values. The cutting method comprises the following steps: if the ith said eigenvalue gamma_iLess than sigma₀ln (SNR +1)/N, the ith characteristic value gamma_iReset to 0. Wherein σ₀Is the mean square error of the signal sequence S; the SNR is the signal-to-noise ratio of the signal sequence S; i is the serial number of the characteristic value, and the value range is i ═ 1,2, ·, N;

step 304 finds the initial value x of the soft threshold sequence x₀The formula is found to be x₀＝S-m₀；

Step 305 finds the iteration step t_kThe formula is obtained as

Wherein Δ T is a sampling interval of the signal sequence S;

step 306 of obtaining a judgment vector g_kThe formula is found to be g_k＝x_k-1+t_kA^T(S-Ax_k-1) (ii) a Wherein A is a system matrix, and the calculation formula of the system matrix A is A ═ diag [ gamma ] (gamma)_i]_N×N；

Step 307 judges the judgment vector g_kThe jth element of (1)

And whether the value is larger than the judgment threshold lambda or not is judged to obtain a first judgment result. If the first judgment result shows the jth element

If the value is larger than the judgment threshold lambda, the jth element

Reset to

If the first judgment result shows the jth element

Equal to the judgment threshold lambda, then the jth element

Reset to 0; if the first judgment result shows the jth element

If the value is less than the judgment threshold lambda, the jth element

Reset to

Wherein the formula for calculating the judgment threshold λ is λ ═ σ%₀ln[SNR+1](ii) a j is the element serial number, and the value range of j is 1,2, ·, N;

step 308, the k-th step value x of the soft threshold sequence x is obtained_kThe formula is found to be x_k＝g_k；

Step 309 determines the adjacent error e, which is given by the equation e ═ x_k-x_k-1|；

Step 310 determines whether the adjacent error e is greater than or equal to a predetermined threshold₀And obtaining a second judgment result. If the second judgment result shows that the adjacent error e is greater than or equal to the preset threshold value₀Returning to said step 305, said step 306, said step 307, said step 308, said step 309 and said step 310, and adding 1 to the value of said iterative control parameter k; until the second judgment result shows that the approximation error e is smaller than the preset threshold value₀The preset threshold ∈₀Is composed of₀＝0.001；

Step 311 records the noise-filtered signal sequence S_NEWAnd is specifically S_NEW＝x_k。

The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is simple because the system corresponds to the method disclosed by the embodiment, and the relevant part can be referred to the method part for description.

The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.

Claims (2)

1. The method for filtering the transformer state vibration and sound detection signal by using the iterative soft threshold is characterized by comprising the following steps of:

step 101, acquiring a signal sequence S acquired according to a time sequence;

step 102 of obtaining N eigenvalues gamma of the average matrix B₁,γ₂,···,γ_N. Wherein N is the length of the signal sequence S; the solving formula of the average matrix B is B ═ S-m₀]^T[S-m₀]. Wherein m is₀Is the mean of the signal sequence S;

step 103 is to apply all the characteristic values gamma₁,γ₂,···,γ_NAnd performing truncation to obtain the updated values of all the characteristic values. The cutting method comprises the following steps: if the ith said eigenvalue gamma_iLess than sigma₀ln (SNR +1)/N, the ith characteristic value gamma_iReset to 0. Wherein σ₀Is the mean square error of the signal sequence S; the SNR is the signal-to-noise ratio of the signal sequence S;

step 104 finds the initial value x of the soft threshold sequence x₀＝S-m₀；

Step 105 of obtaining an iteration step t_kThe formula is obtained as

Wherein Δ T is a sampling interval of the signal sequence S;

step 106 of obtaining a judgment vector g_kThe formula is found to be g_k＝x_k-1+t_kA^T(S-Ax_k-1) (ii) a Wherein A is a system matrix, and the calculation formula of the system matrix A is A ═ diag [ gamma ] (gamma)_i]_N×N；

Step 107 judges said judgment vector g_kThe jth element of (1)

And whether the value is larger than the judgment threshold lambda or not is judged to obtain a first judgment result. If the first judgment result shows the jth element

If the value is larger than the judgment threshold lambda, the jth element

Reset to

If the first judgment result shows the jth element

Equal to the judgment threshold lambda, then the jth element

Reset to 0; if the first judgment result shows the jth element

If the value is less than the judgment threshold lambda, the jth element

Reset to

Wherein the formula for calculating the judgment threshold λ is λ ═ σ%₀ln[SNR+1]；

Step 108, calculating the k step value x of the soft threshold sequence x_kThe formula is found to be x_k＝g_k；

Step 109 calculates the adjacent error e, and the formula is e ═ x_k-x_k-1|；

Step 110 determines whether the adjacent error e is greater than or equal to a preset threshold₀And obtaining a second judgment result. If the second judgment result shows that the adjacent error e is greater than or equal to the preset threshold value₀Returning to the step 105, the step 106, the step 107, the step 108, the step 109 and the step 110, and adding 1 to the value of the iterative control parameter k; until the second judgment result shows that the approximation error e is smaller than the preset threshold value₀. The preset threshold value is₀＝0.001；

Step 111 records the noise-filtered signal sequence S_NEWAnd is specifically S_NEW＝x_k。

2. The transformer state vibration and sound detection signal filtering system using the iterative soft threshold is characterized by comprising the following components:

the module 201 acquires a signal sequence S acquired in time sequence;

the module 202 finds the N eigenvalues γ of the averaging matrix B₁,γ₂,···,γ_N. Wherein N is the length of the signal sequence S; the solving formula of the average matrix B is B ═ S-m₀]^T[S-m₀]. Wherein m is₀Is the mean of the signal sequence S;

the module 203 processes all the characteristic values gamma₁,γ₂,···,γ_NAnd performing truncation to obtain the updated values of all the characteristic values. Cutting-off squareThe method comprises the following steps: if the ith said eigenvalue gamma_iLess than sigma₀ln (SNR +1)/N, the ith characteristic value gamma_iReset to 0. Wherein σ₀Is the mean square error of the signal sequence S; the SNR is the signal-to-noise ratio of the signal sequence S;

module 204 finds the initial value x of the soft threshold sequence x₀＝S-m₀；

Module 205 determines the iteration step t_kThe formula is obtained as

Wherein Δ T is a sampling interval of the signal sequence S;

module 206 finds the decision vector g_kThe formula is found to be g_k＝x_k-1+t_kA^T(S-Ax_k-1) (ii) a Wherein A is a system matrix, and the calculation formula of the system matrix A is A ═ diag [ gamma ] (gamma)_i]_N×N；

The module 207 judges the judgment vector g_kThe jth element of (1)

And whether the value is larger than the judgment threshold lambda or not is judged to obtain a first judgment result. If the first judgment result shows the jth element

If the value is larger than the judgment threshold lambda, the jth element

Reset to

If the first judgment result shows the jth element

Equal to the judgment threshold lambda, then the jth element

Reset to 0; if the first judgment result shows the jth element

If the value is less than the judgment threshold lambda, the jth element

Reset to

Wherein the formula for calculating the judgment threshold λ is λ ═ σ%₀ln[SNR+1]；

Module 208 finds the kth step x of the sequence of soft thresholds x_kThe formula is found to be x_k＝g_k；

The block 209 finds the adjacent error e, which is given by the equation e ═ x_k-x_k-1|；

The module 210 determines whether the adjacent error e is greater than or equal to a predetermined threshold₀And obtaining a second judgment result. If the second judgment result shows that the adjacent error e is greater than or equal to the preset threshold value₀Then returning to said module 205, said module 206, said module 207, said module 208, said module 209 and said module 210, the value of said iterative control parameter k is increased by 1; until the second judgment result shows that the approximation error e is smaller than the preset threshold value₀. The preset threshold value is₀＝0.001；

Module 211 records the noise-filtered signal sequence S_NEWAnd is specifically S_NEW＝x_k。

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