Signal reconstruction method and device, electronic equipment and storage medium

Abstract

The application discloses a signal reconstruction method and device, electronic equipment and storage medium. The method comprises the following steps: determining the second residual of the target signal according to the first residual of the target signal, the number of atoms and the sensing matrix; wherein the target signal is determined based on at least one atom; determining the correlation degree of each atom and the target signal according to the first residual and the second residual; wherein the correlation degree indicates the correlation between the target signal and each atom; if the correlation degree meets the first condition, determining whether the second residual meets the second condition; if the second residual meets the second condition, determining the reconstruction signal of the target signal according to the target sparse coefficient of the target signal. In this way, the correlation degree of the target signal and each atom is determined according to the first residual and the second residual; it is convenient to select the atom with strong correlation, and the target signal is recovered according to the atom with suitable strong correlation, thereby improving the accuracy and precision of the reconstruction signal corresponding to the target signal.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of information and communication technology, and in particular to a signal reconstruction method and device, electronic equipment and storage medium. BACKGROUND

[0002] When a signal reaches a receiving end due to reflection or refraction in the transmission process, the amplitude and phase will all be subject to fading distortion. The receiving end receives a signal formed by the superposition of many signals, and usually needs to obtain channel state information (CSI) through channel estimation to recover the transmitted signal. Therefore, research on channel estimation technology is very important.

[0003] Most of the existing stage signal reconstruction algorithm research based on compressed sensing is based on the improvement of the orthogonal matching algorithm. The selection of atoms is improved to achieve efficient matching. However, the orthogonal matching algorithm uses direct inner product operation to match the support set of the atom update. When the difference between columns in the support set is large, the direct inner product operation of the vector cannot accurately express the correlation degree between the vectors, and the selection of the atom may be "unreasonable", which reduces the accuracy and precision of the reconstructed signal. The atom is used to describe the signal component in the target signal, and the support set is used to describe the set determined based on the column of the sensing matrix of the target signal. SUMMARY

[0004] Therefore, the embodiments of the present application provide a signal reconstruction method, device, electronic equipment and storage medium.

[0005] The technical scheme of the embodiments of the present application is as follows:

[0006] In a first aspect, the embodiments of the present application provide a signal reconstruction method, which comprises:

[0007] In some embodiments, the method comprises:

[0008] According to the first residual error of the target signal, the number of atoms and the sensing matrix, the second residual error of the target signal is determined; wherein the target signal is determined based on at least one atom;

[0009] According to the first residual error and the second residual error, the correlation degree of each atom and the target signal is determined; wherein the correlation degree indicates the correlation between the target signal and each atom;

[0010] If the correlation degree satisfies the first condition, it is determined whether the second residual error satisfies the second condition;

[0011] If the second residual error satisfies the second condition, the reconstructed signal of the target signal is determined according to the target sparse coefficient of the target signal.

[0012] In some embodiments, the method comprises:

[0013] If the relevance does not satisfy the first condition, updating the atom number;

[0014] Based on the updated atom number, re-determining the second residual of the target signal.

[0015] In some embodiments, the method comprises:

[0016] The method comprises:

[0017] If the second residual does not satisfy the second condition, updating the iteration number;

[0018] Based on the updated iteration number, re-determining the second residual of the target signal.

[0019] In some embodiments, the determining the second residual of the target signal according to the first residual of the target signal, the atom number and the sensing matrix comprises:

[0020] Based on the column of the sensing matrix and the atom number, updating an index set of the target signal;

[0021] Based on the column of the sensing matrix, updating a support set of the target signal;

[0022] Based on the support set and an observation vector of the target signal, determining channel sparse coefficients of the target signal by using a least square method;

[0023] Based on the product of the support set and the channel sparse coefficients, determining a first parameter;

[0024] Based on the difference between the observation vector and the first parameter, determining the second residual of the target signal.

[0025] In some embodiments, the determining the relevance of each atom to the target signal according to the first residual and the second residual comprises:

[0026] Based on the norm of the difference between the first residual before iteration and the second residual after iteration, determining a second parameter;

[0027] Based on the square value of the norm of the second residual after iteration, determining a third parameter;

[0028] Based on the ratio of the second parameter and the third parameter, determining the relevance of each atom to the target signal.

[0029] In some embodiments, the relevance satisfying the first condition comprises:

[0030] If it is determined that the correlation degree is greater than a first predetermined value, it is determined that the correlation satisfies the first condition.

[0031] In some embodiments, the second residual after iteration satisfies a second condition, including:

[0032] If it is determined that a norm of the second residual after iteration is less than or equal to a second predetermined value, it is determined that the second residual after iteration satisfies the second condition.

[0033] In some embodiments, determining the reconstructed signal of the target signal according to the target sparse coefficient of the target signal includes:

[0034] Determining the reconstructed signal of the target signal according to a product of the target sparse coefficient of the target signal and a sparse matrix.

[0035] In a second aspect, embodiments of the present application provide a signal reconstruction device, the device comprising:

[0036] A first determining module configured to determine a second residual of a target signal according to a first residual of the target signal, a number of atoms and a sensing matrix, wherein the target signal is determined based on at least one atom;

[0037] A second determining module configured to determine a correlation degree of each atom and the target signal according to the first residual and the second residual, wherein the correlation degree indicates a correlation of the target signal and each atom;

[0038] A first judging module configured to determine whether the second residual satisfies a second condition if the correlation degree satisfies a first condition.

[0039] A second judging module configured to determine a reconstructed signal of the target signal according to a target sparse coefficient of the target signal if the second residual satisfies the second condition.

[0040] In a third aspect, embodiments of the present application provide an electronic device, the electronic device comprising:

[0041] A memory storing computer readable instructions;

[0042] A processor connected with the memory, configured to realize the signal reconstruction method provided in the first aspect by running the computer readable instructions.

[0043] In a fourth aspect, embodiments of the present application provide a computer readable storage medium storing computer executable instructions, the computer executable instructions being executed by a processor to realize the signal reconstruction method provided in the first aspect.

[0044] The signal reconstruction method, device, server and storage medium provided by the embodiment of the present application, the method comprises: determining a second residual error of a target signal according to a first residual error of the target signal, a number of atoms and a sensing matrix; wherein the target signal is determined based on at least one atom; determining a correlation degree of each atom and the target signal according to the first residual error and the second residual error; wherein the correlation degree indicates the correlation between the target signal and each atom; if the correlation degree satisfies a first condition, determining whether the second residual error satisfies a second condition; if the second residual error satisfies the second condition, determining a reconstruction signal of the target signal according to a target sparse coefficient of the target signal.

[0045] In the above signal reconstruction process, the correlation degree of the target signal and each atom is determined according to the first residual error and the second residual error; the correlation degree is used to indicate the correlation between the target signal and the atom. In this way, the atom with strong correlation is selected, and the target signal is recovered according to the atom with suitable strong correlation, thereby improving the accuracy and precision of the reconstruction signal corresponding to the target signal. BRIEF DESCRIPTION OF DRAWINGS

[0046] Figure 1 A flowchart of a signal reconstruction method provided by the embodiment of the present application is shown in the figure;

[0047] Figure 2 A flowchart of a signal reconstruction method provided by the embodiment of the present application is shown in the figure;

[0048] Figure 3 A reconstruction probability diagram of a signal reconstruction method provided by the embodiment of the present application is shown in the figure;

[0049] Figure 4 An iteration number diagram of a signal reconstruction method provided by the embodiment of the present application is shown in the figure;

[0050] Figure 5 A flowchart of a signal reconstruction method provided by the embodiment of the present application is shown in the figure;

[0051] Figure 6 A structural diagram of a signal reconstruction device provided by the embodiment of the present application is shown in the figure;

[0052] Figure 7 A structural diagram of an electronic device provided by the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0053] The present application will be further described in detail below with reference to the accompanying drawings and embodiments.

[0054] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description herein is for describing particular embodiments only and is not intended to be limiting of the application.

[0055] In order to better understand the technical solutions described in any one of the embodiments of the present application, first, the related technical solutions are described in part:

[0056] In the related art, orthogonal matching algorithm is often used to realize reconstruction of sparse signals, and r t represents a residual error, t is the number of iterations, represents an empty set, and the index set Λ t represents the index set of t iterations, λ t represents the index number found in the tth iteration, a j represents the jth column of the sensing matrix A, and the support set A t represents the column set of the sensing matrix A selected according to the index set Λ t , and the symbol ∪ represents set union operation, <,> represents direct inner product operation between vectors, and θ t is a sparse coefficient, is a sparse matrix.

[0057] The steps of the orthogonal matching algorithm process are as follows:

[0058] Step 1, obtaining the observation vector y of the target signal, the sensing matrix A and the signal sparsity K; and initializing the residual error r0=y, and

[0059] Step 2, calculating the inner product of the current residual error and each column a j of the sensing matrix, and selecting the index number of the column corresponding to the maximum value in the inner product

[0060] Step 3, updating the index set Λ t of the target signal = Λ t-1 ∪{λ t}, and updating the support set A

[0061] Step 4, calculating the least square solution of the sparse coefficient θ t : y=A t θ t .

[0062] Step 5, updating the residual error of the target signal

[0063] Step 6, judging whether the iteration number t is less than the sparsity K; if t < K, going to Step 2; if t ≥ K, going to Step 7.

[0064] Step 7, determining the reconstructed signal according to the current updated residual and the residual formula r t = y - A t θ t determining the sparsity coefficient θ t the sparsity coefficient θ of the last iteration t i.e. the target sparsity coefficient determining the reconstructed signal according to the target sparsity coefficient

[0065] The orthogonal matching algorithm uses direct inner product operation to match the related atoms to update the support set. When the difference between columns is large, the direct inner product operation of the vector cannot accurately express the correlation degree between the vectors, and the atom selection is "unreasonable", which reduces the reconstruction accuracy.

[0066] The embodiment of the present application provides a signal reconstruction method, Figure 1 The implementation flow diagram of the signal reconstruction method provided by the embodiment of the present application is shown in FIG. 1, which mainly includes the following steps: Figure 1

[0067] Step S110: determining the second residual of the target signal according to the first residual of the target signal, the number of atoms and the sensing matrix; wherein the target signal is determined based on at least one atom;

[0068] Step S120: determining the correlation degree of each atom and the target signal according to the first residual and the second residual; wherein the correlation degree indicates the correlation between the target signal and each atom;

[0069] Step S130: if the correlation degree satisfies the first condition, determining whether the second residual satisfies the second condition;

[0070] Step S140: if the second residual satisfies the second condition, determining the reconstructed signal of the target signal according to the target sparsity coefficient of the target signal.

[0071] The signal reconstruction method involved in the embodiment of the present application can be executed by a terminal, which can be any mobile terminal or fixed terminal, for example, but is not limited to, a mobile communication device, a computer, a server, etc.

[0072] Here, the channel can be any wired channel or wireless channel; the wired channel includes but is not limited to at least one of the following: symmetrical cable, coaxial cable and optical fiber; the wireless channel includes but is not limited to at least one of the following: short wave channel, microwave relay channel and satellite relay channel.​​

[0073] Here, the target signal can be any signal transmitted in the channel. Exemplarily, the target signal can be a 5G communication signal, a network signal, a video signal, a medical imaging signal, etc.

[0074] Here, the target signal is determined based on at least one atom, and the target signal is a linear combination of the atoms. The atoms are used to describe the signal components of the target signal. The number of atoms indicates the number of atoms selected from the target signal.

[0075] In one embodiment, when the number of iterations is 1, the first residual is an observation vector of the target signal. Wherein, the observation vector of the target signal is determined, comprising:

[0076] The channel matrix of the target signal is vectorized to obtain a vector; and the vector is compressed based on the observation matrix to determine the observation vector of the target signal.

[0077] In one embodiment, when the number of iterations is t and t>1, the first residual is the second residual of the target signal determined in the t-1th iteration, i.e. the second residual determined in the last iteration before the tth iteration; and the second residual is the second residual determined after the tth iteration.

[0078] Exemplarily, when the number of iterations t=4, the first residual is the second residual of the target signal determined in the 3rd iteration, and the second residual is the second residual determined after the 4th iteration; and when the number of iterations t=5, the first residual is the second residual of the target signal determined in the 4th iteration, and the second residual is the second residual determined after the 5th iteration.

[0079] In some embodiments, the correlation degree indicates the correlation between the target signal and each atom in the target signal; and the correlation degree is positively correlated with the correlation. Exemplarily, the higher the correlation degree, the greater the degree of association between the target signal and the atom, and the higher the correlation between the target signal and the atom.

[0080] In some embodiments, the correlation degree satisfies a first condition in step S130, comprising:

[0081] If it is determined that the correlation degree is greater than a first predetermined value, it is determined that the correlation satisfies the first condition.

[0082] Exemplarily, the first predetermined value can be determined according to historical data, or can be determined according to user experience.

[0083] In some embodiments, the second residual satisfies a second condition in step S140, comprising:

[0084] It is determined that the norm of the second residual is less than or equal to a second predetermined value, and it is determined that the second residual satisfies the second condition.

[0085] Exemplarily, the second predetermined value can be determined according to historical data, or can be determined according to user experience.

[0086] Exemplarily, the second predetermined value is ε2, and the tth second residual is r t The norm of the second residual is less than or equal to the second predetermined value, that is, ||r t ||2≤ε2; where t is a positive integer.

[0087] Here, the target sparse coefficient indicates a sparse representation coefficient of a reconstructed signal of the target signal on a sparse matrix.

[0088] In some embodiments, the determining the reconstructed signal of the target signal according to the target sparse coefficient of the target signal comprises:

[0089] Determining the reconstructed signal of the target signal according to the product of the target sparse coefficient of the target signal and the sparse matrix.

[0090] Exemplarily, the target sparse coefficient of the target signal is The sparse matrix of the target signal is The reconstructed signal of the target signal is An implementation manner for determining the reconstructed signal of the target signal is

[0091] In this way, the correlation degree of the target signal and each atom is determined according to the first residual and the second residual; the correlation degree is used to indicate the correlation between the target signal and the atom. The atom with strong correlation is selected, and the target signal is recovered according to the atom with appropriate strong correlation, thereby improving the accuracy and precision of the reconstructed signal corresponding to the target signal.

[0092] In some embodiments, the method comprises:

[0093] If the correlation degree does not satisfy the first condition, updating the number of atoms;

[0094] Based on the updated number of atoms, re-determining the second residual of the target signal.

[0095] In one embodiment, if the correlation degree does not satisfy the first condition, updating the number of atoms comprises:

[0096] If it is determined that the correlation degree is less than or equal to the first predetermined value, it is determined that the correlation degree does not satisfy the first condition;

[0097] Updating the number of atoms.

[0098] In one embodiment, the number of atoms is S, and updating the number of atoms can be increasing the number of atoms. Exemplarily, updating the number of atoms can be S=S+1.

[0099] In one embodiment, the second residual of the target signal is re-determined based on the updated number of atoms, comprising:

[0100] The second residual of the target signal is re-determined based on the first residual of the target signal, the updated number of atoms, and the sensing matrix.

[0101] In this way, if the correlation of an atom is lower than a first predetermined value, the number of atoms is updated, and more atoms with high correlation are accurately selected. The second residual of the target signal is re-determined based on the updated number of atoms, which improves the accuracy of the reconstructed signal and enhances the system performance of the terminal.

[0102] In some embodiments, the method comprises:

[0103] If the second residual does not satisfy the second condition, the number of iterations is updated.

[0104] The second residual of the target signal is re-determined based on the updated number of iterations.

[0105] In one embodiment, if the second residual does not satisfy the second condition, the number of iterations is updated, comprising:

[0106] If the norm of the second residual is greater than the second predetermined value, it is determined that the second residual does not satisfy the second condition.

[0107] The number of iterations is updated.

[0108] In one embodiment, the second predetermined value is ε2, and the second residual of the tth iteration is r t The norm of the second residual is greater than the second predetermined value is ||r t ||2>ε2; where t is a positive integer. Updating the number of iterations can be increasing the number of iterations. Exemplarily, updating the number of iterations can be t=t+1.

[0109] In one embodiment, the second residual of the target signal is re-determined based on the updated number of iterations, comprising:

[0110] The second residual of the target signal is re-determined based on the updated number of iterations, the first residual before iteration, the number of atoms, and the sensing matrix.

[0111] In this way, in the process of iteratively calculating the second residual of the target signal, the iteration condition, i.e., the second condition, is increased. According to the second condition, it is determined whether the second residual can terminate iteration, which reduces the number of iterations in the process of determining the reconstructed signal of the target signal and provides a guarantee for the accuracy of the reconstructed signal.

[0112] In some embodiments, such as Figure 2 As shown, step S110 includes:

[0113] Step S210: Update the index set of the target signal based on the columns and number of atoms of the sensing matrix;

[0114] Step S220: Update the support set of the target signal based on the columns of the sensing matrix;

[0115] Step S230: Using the least squares method, based on the support set and the observation vector of the target signal, determine the channel sparsity coefficient of the target signal;

[0116] Step S240: Determine the first parameter based on the product of the support set and the channel sparsity coefficients;

[0117] Step S250: Determine the second residual of the target signal based on the difference between the observation vector and the first parameter.

[0118] Here, the index set is used to describe the set of columns of the sensing matrix based on the target signal and the number of atoms of the target signal.

[0119] Here, the support set is used to describe the set of columns determined based on the target signal sensing matrix.

[0120] In one embodiment, step S210 includes:

[0121] The first matrix is ​​determined based on the correlation coefficient between the first residual and the columns of the sensing matrix;

[0122] Select the i largest components from the first matrix; wherein, i is equal to the number of atoms;

[0123] The first set is determined based on the column index of the column corresponding to the i largest components in the sensing matrix;

[0124] Based on the first set and the first index set before the iteration, update the second index set of the target signal.

[0125] For example, the number of iterations is t; the sensing matrix is ​​A; and the columns of the sensing matrix are a. j a j The j-th column of the sensing matrix A is indicated; (x,y) indicates the correlation coefficient between vectors x and y; if the number of atoms is S, then i = S; the first set is J.

[0126] One way to determine the first matrix u is u = (r t-1 ,a j), (1≤j≤N) dy; selecting i largest components in the first matrix comprises: selecting S largest components in the first matrix; determining a first set J based on column serial numbers of columns corresponding to the S largest components in the sensing matrix t = Λ t-1 ∪{J}.

[0127] In one embodiment, step S220 comprises:

[0128] updating the second support set of the target signal based on columns of the sensing matrix and the first support set before iteration.

[0129] Exemplarily, the number of iterations is t; the support set of the (t-1)-th iteration is A t-1 , which is the first support set before iteration; the support set of the t-th iteration is A t ; the column of the sensing matrix is a j , a j indicates the j-th column of the sensing matrix A; and the first set is J.

[0130] A way of determining the second support set based on the first support set can be

[0131] Exemplarily, the observation vector of the target signal is y, and the channel sparse coefficient of the target signal is θ t ; the superscript T represents a transpose matrix, and the superscript 1 represents a matrix inversion; the first parameter is m1; and the residual of the t-th iteration is r t , which is the second residual of the target signal.

[0132] A way of determining the channel sparse coefficient of the target signal can be θ t = (A t T A t ) -1 A t T y; a way of determining the first parameter can be m1 = A t θ t = A t (A t T A t ) -1 A t T y; a way of determining the residual of the t-th iteration of the target signal can be r t = y-m1 = y-A t θ t = y-A t (A t T At ) -1 A t T y.

[0133] In one embodiment, an n x 1 dimensional vector x with sparsity k is randomly generated, and a Gaussian independent random distribution observation matrix with dimension m x n (m = 128, n = 256) is adopted The x is observed and compressed, Figure 3 and Figure 4 The simulation results of reconstructing the vector x by using OMP (Orthogonal Matching Pursuit), GOMP (Generalized OMP) and improved algorithm (S = 3) are shown.

[0134] As shown in Figure 3 , when the sparsity k is 40, the reconstruction performance of the OMP algorithm decreases, but the GOMP algorithm and the improved algorithm can still stably reconstruct the signal. As shown in Figure 4 , when the observation matrix dimension is fixed, under the same sparsity, the reconstruction performance of the improved algorithm is higher than that of the GOMP algorithm, and the reconstruction performance of the GOMP algorithm is higher than that of the OMP algorithm.

[0135] In this way, based on the second index set and the second support set of the updated target signal, the second residual of the target signal is updated, the accuracy of the reconstructed signal used to recover the target signal is guaranteed, and the reconstruction performance is improved.

[0136] In some embodiments, step S120 comprises:

[0137] determining a second parameter based on the norm of the difference between the first residual before iteration and the second residual after iteration;

[0138] determining a third parameter based on the square value of the norm of the second residual after iteration;

[0139] determining the relevance of each atom to the target signal based on the ratio of the second parameter to the third parameter.

[0140] For example, the second parameter is m2; the third parameter is m3; the relevance is Re; the residual of the t-1th iteration is r t-1 , that is, the first residual before iteration; the residual of the tth iteration is r t , that is, the second residual after iteration.

[0141] One way to determine the second parameter can be m2 = ||r t -r t-1 ||2; and one way to determine the third parameter can be One way to determine the correlation between the target signal and each atom is to

[0142] For example, the first predetermined value is ε1; the relevance is The relevance described in step S130 that satisfies the first condition can be...

[0143] Thus, considering that each atom has a different correlation with the target signal, the correlation between the atom and the target signal is determined based on the first residual before iteration and the second residual after iteration. The correlation is used to indicate the correlation between the target signal and the atom. If the correlation does not meet the first condition, more atoms with high correlation can be selected to re-iterate the calculation, thereby improving the accuracy and precision of the reconstructed signal and enhancing the performance of signal reconstruction.

[0144] The following provides a specific example in conjunction with any of the above embodiments:

[0145] like Figure 5 As shown in the figure, this application embodiment provides a signal reconstruction method, executed by a terminal, the method including the following steps:

[0146] Step S501: Determine the index set of the target signal;

[0147] In some embodiments, the observation vector of the target signal is y; the number of initial iterations is t = 1; the first residual r0 = y is initialized; and the number of atoms is initialized to S. Denote the empty set and initialize the sensing matrix. Initialize index set The signal sparsity is K; the number of columns in the sensing matrix is ​​a. j ;a j The j-th column of the sensing matrix A is indicated; (x,y) indicates the correlation coefficient between vectors x and y.

[0148] The iteration number is t; based on the correlation coefficient between the first residual and the columns of the sensing matrix, the first matrix u = (r t-1 ,a j ), (1≤j≤N)dy; Select S maximum components in the first matrix, and determine the first set J based on the column indices of the columns corresponding to the S maximum components in the sensing matrix; Based on the first set and the first index set Λ before iteration t-1 Update the second index set Λ of the target signal. t =Λ t-1 ∪{J}.

[0149] Step S502: Update the support set of the target signal;

[0150] In some embodiments, the column of the sensing matrix is based on the first support set A t-1 , and the support set of the target signal is updated

[0151] Step S503: determining channel sparse coefficients of the target signal;

[0152] In some embodiments, the channel sparse coefficients θ of the target signal are determined based on the support set and the observation vector of the target signal by using a least square method t The method can be y = A t θ t ,

[0153] Step S504: determining a second residual of the target signal;

[0154] In some embodiments, the first parameter m1 = A t θ t = A t (A t T A t ) -1 A t T y is determined based on the product of the support set and the channel sparse coefficients; and the second residual r of the target signal is determined based on the difference between the observation vector and the first parameter t = y - m1 = y - A t θ t = y - A t (A t T A t ) -1 A t T y.

[0155] Step S505: determining the relevance of each atom to the target signal;

[0156] In some embodiments, the second parameter m2 = ||r t -r t-1 ||2 is determined based on the norm of the difference between the first residual before iteration and the second residual after iteration; and the third parameter is determined based on the square value of the norm of the second residual after iteration The relevance of each atom to the target signal is determined based on the ratio of the second parameter to the third parameter

[0157] Step S506: determining whether the relevance satisfies a first condition;

[0158] In some embodiments, the correlation degree satisfies the first condition, including: the correlation degree is greater than a first predetermined value ε1; exemplarily, the correlation degree greater than the first predetermined value can be If the correlation degree satisfies the first condition, go to S507; if the correlation degree does not satisfy the first condition, the atomic number S=S+1, go to S501.

[0159] Step S507: determining whether the second residual satisfies a second condition;

[0160] In some embodiments, the second residual satisfies the second condition, including: a norm of the second residual is less than or equal to a second predetermined value ε2; exemplarily, the norm of the second residual less than or equal to the second predetermined value can be t ||2≤ε2; if the second residual satisfies the second condition, go to S508; if the second residual does not satisfy the second condition, the iteration number t=t+1, go to S501.

[0161] Step S508: determining a reconstructed signal of the target signal;

[0162] In some embodiments, if the correlation degree satisfies the first condition and the second residual satisfies the second condition, determining the channel sparse coefficient θ t of the last iteration as the target sparse coefficient The sparse matrix is Determining the reconstructed signal of the target signal, including: determining the reconstructed signal of the target signal according to a product of the target sparse coefficient of the target signal and the sparse matrix; exemplarily, the reconstructed signal

[0163] In this way, according to the first residual and the second residual, the correlation degree of the target signal and each atom is determined; the correlation degree is used to indicate the correlation between the target signal and the atom. If the correlation degree of the atom is lower than the first predetermined value, the atomic number is updated, and more atoms with high correlation degree are accurately selected; the second residual of the target signal is determined again based on the updated atomic number, so as to select atoms with strong correlation; the target signal is recovered according to the appropriate atoms with strong correlation, and the accuracy and precision of the reconstructed signal corresponding to the target signal are improved.

[0164] As Figure 6 shown, based on the same inventive concept as the signal reconstruction method provided in the foregoing embodiments, the embodiments of the present application also provide a signal reconstruction device, the device comprising:

[0165] A first determining module 610 is configured to determine a second residual of a target signal according to a first residual of the target signal, an atomic number and a sensing matrix; wherein the target signal is determined based on at least one atom;

[0166] The second determining module 620 is used to determine the correlation between each atom and the target signal based on the first residual and the second residual; wherein the correlation indicates the correlation between the target signal and each atom;

[0167] The first judgment module 630 is used to determine whether the second residual satisfies the second condition if the correlation satisfies the first condition.

[0168] The second judgment module 640 is used to determine the reconstructed signal of the target signal based on the target sparsity coefficient of the target signal if the second residual satisfies the second condition.

[0169] In some embodiments, the apparatus further includes:

[0170] The first update module is used to update the number of atoms if the relevance does not meet the first condition;

[0171] The first determining module 610 is further configured to redetermine the second residual of the target signal based on the updated number of atoms.

[0172] In some embodiments, the apparatus further includes:

[0173] The second update module is used to update the iteration count if the second residual does not satisfy the second condition.

[0174] The first determining module 610 is further configured to redetermine the second residual of the target signal based on the updated number of iterations.

[0175] In some embodiments, the first determining module 610 is configured to: update the index set of the target signal based on the columns and atoms of the sensing matrix; update the support set of the target signal based on the columns of the sensing matrix; determine the channel sparsity coefficient of the target signal based on the support set and the observation vector of the target signal using the least squares method; determine a first parameter based on the product of the support set and the channel sparsity coefficient; and determine the second residual of the target signal based on the difference between the observation vector and the first parameter.

[0176] In some embodiments, the second determining module 620 is further configured to determine a second parameter based on the norm of the difference between the first residual before iteration and the second residual after iteration; determine a third parameter based on the square of the norm of the second residual after iteration; and determine the correlation between each atom and the target signal based on the ratio of the second parameter to the third parameter.

[0177] In some embodiments, the first determining module 630 is further configured to determine that the correlation satisfies the first condition if it is determined that the correlation is greater than a first predetermined value.

[0178] In some embodiments, the second determination module 640 is further configured to determine that the norm of the second residual after iteration is less than or equal to a second predetermined value, and to determine that the second residual after iteration satisfies a second condition.

[0179] In some embodiments, the second determination module 640 is further configured to determine the reconstructed signal of the target signal based on the product of the target sparse coefficients and the sparse matrix of the target signal.

[0180] In practical applications, the first determining module 610, the second determining module 620, the first judging module 630, and the second judging module 640 of the signal reconstruction device can be implemented by the processor in the signal reconstruction device. Of course, the processor needs to run the computer program in the memory to implement its functions.

[0181] like Figure 7 As shown in the embodiment of this application, an electronic device is provided, the electronic device comprising:

[0182] Memory 701 is used to store computer-readable instructions;

[0183] The processor 702, connected to the memory, is configured to implement the methods provided in any of the foregoing embodiments by executing computer-readable instructions.

[0184] The memory 701 can be of various types, such as random access memory, read-only memory, flash memory, etc. The memory can be used for information storage, for example, storing computer-executable instructions. The computer-executable instructions can be various program instructions, such as object program instructions and / or source program instructions.

[0185] The processor 702 can be various types of processors, such as a central processing unit, microprocessor, digital signal processor, programmable array, application-specific integrated circuit, or image processor. The processor can be connected to the memory via a bus, which can be an integrated circuit bus, etc.

[0186] like Figure 7 As shown, the electronic device may also include a network interface 703, which can be used to interact with peer devices via a network.

[0187] This application also provides a computer storage medium storing computer-executable instructions, which, when executed, can implement the methods provided in any of the foregoing embodiments.

[0188] The computer-readable storage medium provided in the embodiments of this application can be any storage medium capable of storing program code, such as ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface memory, optical disc, or CD-ROM.

[0189] In the embodiments provided in this application, it should be understood that the disclosed methods and apparatus can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple modules or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be indirect coupling or communication connection through some interfaces, devices, or units, and can be electrical, mechanical, or other forms.

[0190] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0191] In addition, each functional unit in the various embodiments of this application can be integrated into one processing module, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0192] It should be noted that terms such as "first" and "second" are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0193] Furthermore, the technical solutions described in the embodiments of this application can be combined arbitrarily without conflict.

[0194] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A signal reconstruction method, comprising at least one iteration, characterized in that, The same iteration includes: Based on the first residual of the target signal, the number of atoms, and the sensing matrix, the second residual of the target signal is determined; wherein, the first residual is the residual before the current iteration, the second residual is the residual after the current iteration, and the target signal is determined based on at least one atom; the second residual of the current iteration becomes the first residual of the next iteration; Based on the first and second residuals of this iteration, the correlation between each atom and the target signal is determined; wherein, the correlation indicates the correlation between the target signal and each atom. If the correlation satisfies the first condition, determine whether the second residual of this iteration satisfies the second condition. If the second residual of this iteration satisfies the second condition, the reconstructed signal of the target signal is determined according to the target sparse coefficient of the target signal, and the iteration process ends; The step of determining the correlation between each atom and the target signal based on the first residual and the second residual of the current iteration includes: determining a second parameter based on the norm of the difference between the first residual and the second residual of the current iteration; determining a third parameter based on the square of the norm of the second residual of the current iteration; and determining the correlation between each atom and the target signal based on the ratio of the second parameter to the third parameter.

2. The method according to claim 1, characterized in that, The method includes: If the correlation does not meet the first condition, update the number of atoms; Based on the updated number of atoms, the second residual of the target signal for this iteration is redefined.

3. The method according to claim 1, characterized in that, The method includes: If the second residual in this iteration does not satisfy the second condition, update the iteration count; Based on the updated number of iterations, the second residual of the target signal for the next iteration is determined.

4. The method according to claim 1, characterized in that, Determining the second residual of the target signal based on the first residual, the number of atoms, and the sensing matrix of the target signal includes: The index set of the target signal is updated based on the columns and number of atoms of the sensing matrix; Based on the columns of the sensing matrix, update the support set of the target signal; Using the least squares method, based on the support set and the observation vector of the target signal, the channel sparsity coefficient of the target signal is determined; The first parameter is determined based on the product of the support set and the channel sparsity coefficients; Based on the difference between the observation vector and the first parameter, the second residual of the target signal in this iteration is determined.

5. The method according to claim 1, characterized in that, The relevance satisfies the first condition, including: If the correlation is determined to be greater than a first predetermined value, then the correlation is determined to satisfy the first condition.

6. The method according to claim 1, characterized in that, The second residual of this iteration satisfies the second condition, including: The norm of the second residual in this iteration is determined to be less than or equal to the second predetermined value, and the second residual in this iteration is determined to satisfy the second condition.

7. The method according to claim 1, characterized in that, Determining the reconstructed signal of the target signal based on the target sparse coefficients of the target signal includes: The reconstructed signal of the target signal is determined by multiplying the target sparse coefficients with the sparse matrix.

8. A signal reconstruction device, characterized in that, The device includes: In at least one iteration, a first determining module is used to determine a second residual of the target signal based on a first residual of the target signal, the number of atoms, and the sensing matrix; wherein the first residual is the residual before the current iteration, the second residual is the residual after the current iteration, and the target signal is determined based on at least one atom; wherein the second residual of the current iteration is the first residual of the next iteration; The second determining module is used to determine the correlation between each atom and the target signal based on the first residual and the second residual of the current iteration; wherein the correlation indicates the correlation between the target signal and each atom; The first judgment module is used to determine whether the second residual of the current iteration satisfies the second condition if the correlation satisfies the first condition. The second judgment module is used to determine the reconstructed signal of the target signal based on the target sparse coefficient of the target signal if the second residual of the current iteration satisfies the second condition, and then end the iteration process. The second determining module is further configured to determine a second parameter based on the norm of the difference between the first residual and the second residual of the current iteration; to determine a third parameter based on the square of the norm of the second residual of the current iteration; and to determine the correlation between each atom and the target signal based on the ratio of the second parameter to the third parameter.

9. An electronic device, characterized in that, include: Memory, which stores computer-readable instructions; A processor, connected to the memory, is configured to implement the signal reconstruction method provided by any one of claims 1 to 7 by executing the computer-readable instructions.

10. A computer storage medium, characterized in that, The computer storage medium stores computer-executable instructions; when the computer-executable instructions are executed by the processor, they can implement the signal reconstruction method according to any one of claims 1 to 7.

Images