GNSS signal compression capture method and device, electronic device, and storage medium

Abstract

The present invention discloses a GNSS signal compression capture method and device, electronic equipment, and storage medium, relating to the field of communication positioning technology. The present invention comprises: using the F-norm of the difference between a Gram matrix and an approximately equiangular tight frame matrix as an objective function; iteratively optimizing a perception matrix through conjugate gradients to reduce column non-correlation, thereby increasing the probability of signal reconstruction; then solving a measurement matrix based on a spread spectrum code matrix using the optimized perception matrix; then obtaining measurement values ​​using the measurement matrix, a sparse basis, and a received signal; and reconstructing code phase and Doppler values ​​based on the measurement values ​​and the perception matrix, thereby achieving compressed capture of GNSS signals. The present invention can not only improve the probability of GNSS signal capture, but also reduce the resources and time consumed by capture.

Description

Technical Field

[0001] The present application relates to the field of communication positioning technology, and in particular to a GNSS signal compression capture method and device, electronic equipment, and storage medium. Background Art

[0002] Global Navigation Satellite Systems (GNSS) are now widely used in civil, commercial, and military applications, allowing users to calculate their position, velocity, and time (PVT) based on signals transmitted by satellites. Existing GNSS systems include GPS, GLONASS, Galileo, and BeiDou. GNSS receivers must perform many complex operations, among which signal acquisition is extremely important. It provides a rough estimate of the received signal's code phase delay and Doppler frequency, which plays a crucial role in synchronizing the local signal with the received signal.

[0003] Traditional acquisition algorithms include serial and parallel acquisition algorithms. The former takes a long time to acquire, while the latter consumes more resources. Moreover, both methods are based on the Nyquist sampling criterion and fail to fundamentally address the high sampling rate and complexity issues inherent in signal acquisition. The recently emerging theory of compressed sensing (CS) suggests that the sampling rate of sparse signals can be lower than the Nyquist rate, reducing data storage and computational complexity. This has been applied to GNSS signal acquisition, achieving promising results.

[0004] In the process of implementing the present invention, the inventors discovered that the prior art has at least the following problems:

[0005] Existing compression capture algorithms all use a random Gaussian matrix to construct a corresponding perception matrix to compress and capture GNSS signals. This matrix can reconstruct the original signal with a certain probability, but the signal reconstruction effect is poor due to the relatively poor non-correlation of its columns. Applying it to GNSS signal compression capture will result in a low capture probability of the algorithm. Summary of the Invention

[0006] The purpose of the embodiments of the present application is to provide a GNSS signal compression capture method and device, electronic equipment, and storage medium to solve the technical problem of low capture probability existing in the related art.

[0007] According to a first aspect of an embodiment of the present application, a GNSS signal compression acquisition method is provided, comprising:

[0008] S1: Receive GNSS signals and obtain the sampling point matrix R and code matrix C through sampling;

[0009] S2: Initialize the measurement matrix Φ and the inverse matrix C of the code matrix C -1 As the sparse basis Ψ, the perception matrix Θ = ΦΨ is calculated;

[0010] S3: Construct the Gram matrix G of the perception matrix = Θ T Θ;

[0011] S4: performing column normalization processing on the Gram matrix G;

[0012] S5: After column normalization, the F-norm of the difference between the Gram matrix and the approximately equiangular tight frame matrix is ​​used as the objective function;

[0013] S6: Optimize the objective function by using the improved conjugate gradient method to obtain the optimal perception matrix Θ opt , to reduce the correlation between the measurement matrix Φ and the sparse basis Ψ;

[0014] S7: According to the perception matrix Θ opt , calculate and obtain the measurement matrix Φ;

[0015] S8: Calculate the measurement value Y according to the sampling point matrix R and the measurement matrix Φ;

[0016] S9: Based on the measured value Y, combine the least squares operator X=Θ T (ΘΘ T ) -1 Y, calculate the correlation matrix X;

[0017] S10: Making a decision based on the maximum value of the correlation matrix X to determine the corresponding code phase and Doppler frequency offset.

[0018] According to a second aspect of an embodiment of the present application, a GNSS signal compression and capture device is provided, comprising:

[0019] The receiving sampling module is used to receive GNSS signals and obtain the sampling point matrix R and code matrix C through sampling;

[0020] The first calculation module is used to initialize the measurement matrix Φ and the inverse matrix C of the code matrix C -1 As the sparse basis Ψ, the perception matrix Θ = ΦΨ is calculated;

[0021] A construction module for constructing the Gram matrix G=Θ of the perception matrix T Θ;

[0022] A normalization module, configured to perform column normalization processing on the Gram matrix G;

[0023] An objective function construction module is used to take the F norm of the difference between the Gram matrix and the approximately equiangular tight frame matrix as the objective function after column normalization;

[0024] The optimization solution module is used to optimize the objective function by using the improved conjugate gradient method to obtain the optimal perception matrix Θ opt , to reduce the correlation between the measurement matrix Φ and the sparse basis Ψ;

[0025] The second calculation module is used to calculate the perception matrix Θ according to the opt , calculate and obtain the measurement matrix Φ;

[0026] A third calculation module is used to calculate the measurement value Y according to the sampling point matrix R and the measurement matrix Φ;

[0027] The fourth calculation module is used to combine the least squares operator X=Θ according to the measurement value Y T (ΘΘ T ) -1 Y, calculate the correlation matrix X;

[0028] The determination module is used to make a decision based on the maximum value of the correlation matrix X to determine the corresponding code phase and Doppler frequency offset.

[0029] According to a third aspect of the embodiments of the present application, there is provided an electronic device, including:

[0030] one or more processors;

[0031] a memory for storing one or more programs;

[0032] When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in the first aspect.

[0033] According to a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, on which computer instructions are stored. When the instructions are executed by a processor, the steps of the method described in the first aspect are implemented.

[0034] The technical solutions provided by the embodiments of the present application may have the following beneficial effects:

[0035] It can be seen from the above embodiments that the present application uses the F-norm of the difference between the Gram matrix and the equiangular tight frame (ETF) matrix as the objective function, and optimizes the objective function through the improved conjugate gradient method to reduce the correlation between the measurement matrix and the sparse basis; theoretical analysis and simulation results show that under the same signal-to-noise ratio conditions, the present application can significantly improve the GNSS signal capture probability compared with the existing compression capture algorithm.

[0036] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0038] Figure 1 The figure is a flowchart of a GNSS signal compression acquisition method according to an exemplary embodiment.

[0039] Figure 2 is a comparison histogram of off-diagonal elements of a Gram matrix corresponding to a sensing matrix according to an exemplary embodiment.

[0040] Figure 3 is a graph showing how the detection probability of each algorithm varies with the PRN code length according to an exemplary embodiment.

[0041] Figure 4 is a graph showing how the detection probability of each algorithm varies with the compression ratio N / M according to an exemplary embodiment.

[0042] Figure 5 The figure is a block diagram of a GNSS signal compression and capture device according to an exemplary embodiment. DETAILED DESCRIPTION

[0043] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0044] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. As used in this application and the appended claims, the singular forms "a," "an," "the," and "the" are intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0045] Figure 1 FIG. 1 is a flow chart showing a method for compressing and capturing GNSS signals according to an exemplary embodiment. Figure 1 As shown, the following steps may be included:

[0046] S1: Receive GNSS signals and obtain the sampling point matrix R and code matrix C through sampling. Specifically, it includes the following steps:

[0047] S11: Perform A / D sampling and down-conversion on the received GNSS signal r(t) to obtain r(n), and perform preprocessing on the signal. Take N down-converted signals to form a signal vector r0 = [r(0), r(1), ..., r(N-1)] T , and perform cyclic shift to form the sampling point matrix R:

[0048]

[0049] S12: Take the N-point local PRN code vector c=[c0,c1,…,c N-1 ]∈R N×1 , and perform cyclic shift to obtain the code matrix C, and the inverse matrix C of the code matrix C -1 As a sparse basis Ψ:

[0050]

[0051] Since the PRN code autocorrelation function is a series of correlation triangles with a period of N PRN code chips, and there is a low correlation of 1 / N within the code chip period, the correlation value of the signal is sparse. The code matrix can be used to sparsify the GNSS signal to obtain a sparse vector.

[0052] S2: Initialize the measurement matrix Φ and the inverse matrix C of the code matrix C -1 As the sparse basis Ψ, the perception matrix Θ = ΦΨ is calculated;

[0053] Specifically, initialize the number of iterations t max , m×n dimensional measurement matrix Φ and n×n dimensional sparse basis Ψ, calculate the perception matrix Θ = ΦΨ;

[0054] The perception matrix Φ can be randomly generated, as long as the dimension is satisfied, and the sparse basis Ψ is the inverse matrix C of the code matrix C -1 Decide.

[0055] The non-correlation of the perception matrix columns affects signal reconstruction performance, so this invention primarily optimizes the perception matrix. Secondly, since the sparse basis is determined by the code matrix, the arbitrary selection of the measurement matrix does not affect the optimized results. Furthermore, this design allows the iterative optimization process of the perception matrix to be completed offline, without affecting the computational complexity of the entire algorithm.

[0056] S3: Construct the Gram matrix G of the perception matrix = Θ T Θ;

[0057] Specifically, the Gram matrix G of the perception matrix is ​​constructed as follows: T Θ, making it as close as possible to the target matrix G t , where the expansion of G is as follows:

[0058]

[0059] For any K-sparse signal, when the sparsity K satisfies The signal can be accurately recovered. Where μ(Θ) is the mutual correlation coefficient, and its formula is as follows:

[0060]

[0061] The column of Θ, that is, the mutual correlation coefficient μ(Θ), is a measure of the minimum similarity of the perception matrix columns.

[0062] From the expansion of G, we can get the column correlation coefficient μ(Θ), which can also be defined as the maximum absolute value of the non-diagonal elements in the matrix G. Therefore, the cross-correlation coefficient μ(Θ) is converted into a Gram matrix for subsequent processing.

[0063] S4: performing column normalization processing on the Gram matrix G;

[0064] Specifically, the Gram matrix G is column normalized is a diagonal matrix.

[0065] Normalization can simplify subsequent operations and reduce computational complexity.

[0066] S5: After column normalization, the F-norm of the difference between the Gram matrix and the approximately equiangular tight frame matrix is ​​used as the objective function;

[0067] Specifically, the smaller the off-diagonal elements of the matrix G are, the smaller the mutual correlation coefficient is, which is more conducive to signal reconstruction. Therefore, the F norm of the difference between the Gram matrix and the approximately equiangular tight frame matrix is ​​used as the objective function:

[0068]

[0069] where ρ is the objective function, ||·|| F is the F-norm, G t To optimize the target matrix:

[0070]

[0071] δ e The Gram matrix group, which conforms to an equiangular tight frame matrix, is used to constrain the set of objective functions. Since the smaller the off-diagonal elements of the matrix G, the smaller its correlation coefficient μ(Θ), but off-diagonal elements cannot be zero, the F-norm of the difference between it and a equiangular tight frame matrix is ​​used as the objective function for iterative optimization. This allows it to infinitely approximate the equiangular tight frame matrix, thereby minimizing the correlation coefficient μ(Θ), thereby improving signal recovery performance.

[0072] S6: Optimize the objective function by using the improved conjugate gradient method to obtain the optimal perception matrix Θ opt , to reduce the correlation between the measurement matrix Φ and the sparse basis Ψ; the following steps may be included:

[0073] S61: Since the sparse basis Ψ is composed of the inverse matrix C of the code matrix C -1 Determine, thus transforming the objective function to obtain the following formula:

[0074]

[0075]

[0076] S62: Order And the gradient function is obtained by derivation of the deformed objective function:

[0077]

[0078] Where Γ is a diagonal matrix with the same diagonal elements as the matrix D:

[0079]

[0080] S63: Use the conjugate gradient operator to iteratively update the above perception matrix Θ:

[0081] Θ k+1 =Θ k +αd k

[0082] S64: Calculate the step size α using the Armijo-Goldstein criterion;

[0083] S65: According to Calculation factor β k ;

[0084] S66: Exploitation Iteratively update the gradient direction d;

[0085] S67: Using the contraction operator For the target matrix G t Zoom in or out;

[0086] S68: Determine the number of iterations t and count t+1. If t≥t max Then output Θ opt .

[0087] The use of the conjugate gradient method can reduce the number of iterations of the algorithm, allowing it to find the optimal gradient direction more quickly. Secondly, each iterative update uses a shrinkage operator to scale the target matrix, which can further reduce the error.

[0088] S7: According to the perception matrix Θ opt , calculate and obtain the measurement matrix Φ;

[0089] Specifically, the formula Θ=ΦΨ is used to solve the measurement matrix Φ, which paves the way for calculating the measurement value Y, because the measurement value Y M×N =Φ M×N ·R N×N , where R is the known sampling point matrix, so the measurement matrix Φ needs to be calculated;

[0090] S8: Calculate the measurement value Y according to the sampling point matrix R and the measurement matrix Φ;

[0091] Specifically, the measured value Y is obtained according to the signal compression formula:

[0092]

[0093] Each element in Y is obtained by directly multiplying the measurement matrix and the sampling point matrix, that is,

[0094]

[0095] The input signal can be viewed as a compressed correlation of the measurement matrix Φ. Therefore, using compressed sensing can reduce the number of correlators to M / N times the original number, thereby reducing the computational complexity and amount of calculation. At the same time, the reduction in the amount of computational data also reduces resource overhead and subsequent computational pressure.

[0096] S9: Based on the measured value Y, combine the least squares operator X=Θ T (ΘΘ T ) -1 Y, calculate the correlation matrix X;

[0097] Specifically, Y and Θ opt Will be brought into the formula

[0098] min||X||1 stY=Θ·X

[0099] Using the least squares iteration X = Θ T (ΘΘ T ) -1 The Y algorithm solves the LASSO model and obtains the correlation matrix X:

[0100]

[0101] S10: Making a decision based on the maximum value of the correlation matrix X to determine the corresponding code phase and Doppler frequency offset.

[0102] Specifically, when the maximum value of the correlation matrix X is greater than the capture threshold, it represents that the signal is captured successfully, wherein the coordinates of the maximum value of X represent the correlation peak position of the signal, that is, the coordinates of the correlation peak are the frequency offset and code phase of the signal capture.

[0103] Example:

[0104] S1: Signal preprocessing

[0105] The received GNSS signal r(t) is sampled and down-converted to obtain r(n), and the signal is preprocessed. The N-point down-converted signal is taken to form a signal vector r0 = [r(0), r(1), ..., r(N-1)] T , and perform cyclic shift to form the sampling point matrix R:

[0106]

[0107] Take the N-point length local PRN code vector c=[c0,c1,…,c N-1 ]∈R N×1 , and perform cyclic shift to obtain the code matrix C, and the inverse matrix C of the code matrix C -1 As a sparse basis Ψ:

[0108]

[0109] S2: Initialization

[0110] Initialize the number of iterations t max , m×n dimensional measurement matrix Φ and n×n dimensional sparse basis Ψ, calculate the perception matrix Θ = ΦΨ;

[0111] The perception matrix Φ can be randomly generated, as long as the dimension is satisfied, and the sparse basis Ψ is the inverse matrix C of the code matrix C -1 Decide.

[0112] S3: Gram matrix construction

[0113] For any K-sparse signal, when the sparsity K satisfies The signal can be accurately recovered. Where μ(Θ) is the mutual correlation coefficient, and its formula is as follows:

[0114]

[0115] The column of Θ, that is, the mutual correlation coefficient μ(Θ), is a measure of the minimum similarity of the perception matrix columns.

[0116] Construct the Gram matrix G of the perception matrix = Θ T Θ, making it as close as possible to the target matrix G t , where the expansion of G is as follows:

[0117]

[0118] From the expansion of G, we can get the column correlation coefficient μ(Θ), which can also be defined as the maximum absolute value of the off-diagonal elements in the matrix G. Therefore, it is converted into a Gram matrix for subsequent processing.

[0119] S4: Normalization

[0120] Normalize the columns of the Gram matrix G is a diagonal matrix. Normalization can simplify subsequent operations and reduce computational complexity.

[0121] S5: Objective function construction

[0122] The smaller the off-diagonal elements of the matrix G are, the smaller the mutual correlation coefficient is, which is more conducive to signal reconstruction. The F norm of the difference between the Gram matrix and the approximately equiangular tight frame matrix is ​​used as the objective function:

[0123]

[0124] where ρ is the objective function, ||·|| F is the F-norm, G t To optimize the target matrix:

[0125]

[0126] The F norm of the difference between the matrix G and an equiangular tight frame matrix is ​​used as the objective function for iterative optimization, which can make it infinitely approximate the equiangular tight frame matrix, and then make the correlation coefficient μ(Θ) as small as possible, thereby improving the recovery performance.

[0127] S6: Objective function solution

[0128] Since the sparse basis Ψ is composed of the inverse matrix C of the code matrix C -1 Determine and transform the objective function to obtain the following formula:

[0129]

[0130]

[0131] make And the gradient function is obtained by derivation of the deformed objective function:

[0132]

[0133] Where Γ is a diagonal matrix with the same diagonal elements as the matrix D:

[0134]

[0135] The conjugate gradient operator is used to iteratively update the above perception matrix Θ:

[0136] Θ k+1 =Θ k +αd k

[0137] The step size α is calculated using the Armijo-Goldstein criterion;

[0138] according to Calculation factor β k ;

[0139] use Iteratively update the gradient direction d;

[0140] Using the contraction operator For the target matrix G t Zoom in or out;

[0141] Make a judgment for the number of iterations t and count t+1. If t≥t max Then output Θ opt .

[0142] The use of the conjugate gradient method can reduce the number of iterations of the algorithm, allowing it to find the optimal gradient direction more quickly; secondly, using the shrinkage operator to scale the target matrix in each iterative update can further reduce the error.

[0143] S7: Measurement Matrix Solution

[0144] According to the perception matrix Θ opt , calculate the measurement matrix Φ; use the formula Θ=ΦΨ to solve the measurement matrix Φ, which paves the way for calculating the measurement value Y, because the measurement value Y M×N =Φ M×N ·R N×N , where R is the known sampling point matrix, so the measurement matrix Φ needs to be calculated.

[0145] S8: Solving for the measured value Y

[0146] The measured value Y is obtained according to the signal compression formula:

[0147]

[0148] Each element in Y is obtained by directly multiplying the measurement matrix and the sampling point matrix, that is,

[0149]

[0150] Specifically, Y and Θ opt Will be brought into the formula

[0151] min||X||1 stY=Θ·X

[0152] Using the least squares iteration X = Θ T (ΘΘ T ) -1 The Y algorithm solves the LASSO model and obtains the correlation matrix X:

[0153]

[0154] S10: Correlation peak judgment

[0155] When the maximum value of the correlation matrix X is greater than the capture threshold, it indicates that the signal is captured successfully. The coordinates of the maximum value of X represent the correlation peak position of the signal, that is, the coordinates of the correlation peak are the frequency offset and code phase of the captured signal.

[0156] This section compares the capture performance and algorithm complexity of the present invention (CG) capture algorithm, the Elad capture algorithm (Elad), and the Gaussian compression algorithm (GP) under high dynamic conditions. The simulation experiment parameters are shown in the following table:

[0157] parameter Parameter value <![CDATA[Signal rate (R b )]]> 50bps Code length (N) 1023 <![CDATA[Chip rate (R c )]]> 1.023cps

[0158] Figure 2 The histograms of the off-diagonal elements of the Gram matrices corresponding to the various sensing matrices are compared. It can be seen that the off-diagonal elements of the Gram matrices constructed by the proposed CG algorithm are closer to zero than those of existing matrices. This demonstrates that the column incoherence of the optimized sensing matrix is ​​superior to that of existing algorithms, which means it is more conducive to signal recovery and reconstruction.

[0159] Figure 3 The effect of code length on the detection probability of several capture algorithms is compared when the SNR is -15dB. We can see that as the code length increases, the detection probability of the proposed CG algorithm consistently exceeds that of other compression capture algorithms. This figure illustrates that improved column incoherence brings the noise power closer to the Welch lower bound, which improves the performance of the compression capture algorithm.

[0160] Figure 4 The effects of different compression ratios on the detection probability of several acquisition algorithms at an SNR of -15dB were compared. It can be seen that the detection probability of compressed acquisition significantly decreases with decreasing M / N. This indicates that compressed measurement can lead to signal loss, but the detection probability of the proposed CG algorithm is consistently higher than that of other algorithms. This demonstrates that the proposed algorithm improves column incoherence and reduces noise power.

[0161] Corresponding to the aforementioned embodiment of the GNSS signal compression capture method, the present application also provides an embodiment of a GNSS signal compression capture device.

[0162] Figure 5 FIG1 is a block diagram of a GNSS signal compression capture device according to an exemplary embodiment. Figure 5 The device includes a receiving sampling module 1, a first calculation module 2, a construction module 3, a normalization module 4, an objective function construction module 5, an optimization solution module 6, a second calculation module 7, a third calculation module 8, a fourth calculation module 9, and a determination module 10.

[0163] Receiving sampling module 1, used to receive GNSS signals and obtain sampling point matrix R and code matrix C through sampling;

[0164] The first calculation module 2 is used to initialize the measurement matrix Φ and the inverse matrix C of the code matrix C -1 As the sparse basis Ψ, the perception matrix Θ = ΦΨ is calculated;

[0165] Construction module 3, used to construct the Gram matrix G=Θ of the perception matrix T Θ;

[0166] A normalization module 4 is used to perform column normalization processing on the Gram matrix G;

[0167] The objective function construction module 5 is used to take the F norm of the difference between the Gram matrix and the approximately equiangular tight frame matrix as the objective function after column normalization;

[0168] Optimization solution module 6 is used to optimize the objective function by using the improved conjugate gradient method to obtain the optimal perception matrix Θ opt , to reduce the correlation between the measurement matrix Φ and the sparse basis Ψ;

[0169] The second calculation module 7 is used to calculate the perception matrix Θ according to the opt , calculate and obtain the measurement matrix Φ;

[0170] A third calculation module 8 is used to calculate the measurement value Y according to the sampling point matrix R and the measurement matrix Φ;

[0171] The fourth calculation module 9 is used to calculate the measured value Y in combination with the least squares operator X=Θ T (ΘΘ T ) -1 Y, calculate the correlation matrix X;

[0172] The determination module 10 is configured to make a decision based on the maximum value of the correlation matrix X and determine the corresponding code phase and Doppler frequency offset.

[0173] Regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0174] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to the partial description of the method embodiments. The device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present application scheme. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0175] Accordingly, the present application also provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the GNSS signal compression capture method as described above.

[0176] Accordingly, the present application also provides a computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implement the GNSS signal compression capture method as described above.

[0177] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the contents disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of the present application and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered merely as exemplary, and the true scope and spirit of the present application are indicated by the claims.

[0178] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A GNSS signal compression capture method, characterized in that: include: S1: Receive GNSS signals , obtain the sampling point matrix R and code matrix C through sampling; S2: Initialize the measurement matrix And the inverse matrix of the code matrix C As a sparse basis , calculate the perception matrix ; S3: Constructing the Gram matrix of the perception matrix ; S4: For the Gram matrix Perform column normalization; S5: After column normalization, the F-norm of the difference between the Gram matrix and the approximately equiangular tight frame matrix is ​​used as the objective function; S6: Optimize the objective function by using the improved conjugate gradient method to obtain the optimal perception matrix , to reduce the measurement matrix With sparse basis relevance; S7: According to the perception matrix , using sparse basis , calculate the measurement matrix ; S8: Based on the sampling point matrix R and the measurement matrix , calculate the measured value ; S9: According to the measured value , combined with the least squares operator , calculate the correlation matrix ; S10: According to the correlation matrix The maximum value of is judged to determine the corresponding code phase and Doppler frequency offset, and the code phase and Doppler value are reconstructed through the measurement value and the sensing matrix to achieve GNSS signal compression capture; Among them, receiving GNSS signals , the sampling point matrix R and code matrix C are obtained by sampling, including: S11: Received GNSS signals Perform A / D sampling and down conversion to obtain , and preprocess the signal to obtain N Point down-converted signal to form a signal vector , and perform cyclic shift to form the sampling point matrix R: ； S12: Take N Point-length local PRN code vector , and perform cyclic shift on it to obtain the code matrix C: 。 2. The method according to claim 1, characterized in that For the Gram matrix Perform column normalization, including: Gram matrix Perform column normalization , is a diagonal matrix.

3. The method according to claim 2, characterized in that After column normalization, the F norm of the difference between the Gram matrix and the approximately equiangular tight frame matrix is ​​used as the objective function, including: The F norm of the difference between the Gram matrix and the approximately equiangular tight frame matrix is ​​used as the objective function: ； in is the objective function, is the F-norm, To optimize the target matrix: ； in is the Gram matrix that meets the equiangular tight frame matrix, is the restriction threshold of the Gram matrix elements.

4. The method according to claim 3, characterized in that The objective function is optimized by the improved conjugate gradient method to obtain the optimal perception matrix , to reduce the measurement matrix With sparse basis Relevance includes: Due to the sparse basis The inverse matrix of the code matrix C Determine, thus transforming the objective function to obtain the following formula: ； make , and derive the deformed objective function to obtain the gradient function: ； in is a diagonal matrix, the diagonal elements are the same as the matrix same: ； The conjugate gradient operator is used to transform the above perception matrix Perform iterative updates: ； And use the Armijo-Goldstein criterion to calculate the step size ; according to Calculation Factor ; use Iteratively update the gradient direction ; Using the contraction operator , for the target matrix Zoom in or out; Number of iterations Verdict and Count ,like Then output .

5. The method according to claim 4, characterized in that According to the sampling point matrix R and the measurement matrix , calculate the measured value ,include: The measured value is obtained according to the signal compression formula : ； in Each element in is obtained by directly multiplying the measurement matrix and the sampling point matrix, that is, 。 6. The method according to claim 5, characterized in that According to the measured value , combined with the least squares operator , calculate the correlation matrix ,include: Will and Will be substituted into the formula: ； Least squares iteration The algorithm solves the LASSO model and obtains the correlation matrix ; 。 7. A GNSS signal compression capture device, characterized in that: include: Receive sampling module for receiving GNSS signals , obtain the sampling point matrix R and code matrix C through sampling; The first calculation module is used to initialize the measurement matrix And the inverse matrix of the code matrix C As a sparse basis , calculate the perception matrix ; A construction module for constructing the Gram matrix of the perception matrix ; Normalization module for the Gram matrix Perform column normalization; An objective function construction module is used to take the F norm of the difference between the Gram matrix and the approximately equiangular tight frame matrix as the objective function after column normalization; The optimization solution module is used to optimize the objective function by using the improved conjugate gradient method to obtain the optimal perception matrix , to reduce the measurement matrix With sparse basis relevance; The second calculation module is used to calculate the , using sparse basis , calculate the measurement matrix ; The third calculation module is used to calculate the sampling point matrix R and the measurement matrix , calculate the measured value ; The fourth calculation module is used to calculate the , combined with the least squares operator , calculate the correlation matrix ; A determination module is used to determine the The maximum value of is judged to determine the corresponding code phase and Doppler frequency offset, and the code phase and Doppler value are reconstructed through the measurement value and the sensing matrix to achieve GNSS signal compression capture; Among them, receiving GNSS signals , the sampling point matrix R and code matrix C are obtained by sampling, including: S11: Received GNSS signals Perform A / D sampling and down conversion to obtain , and preprocess the signal to obtain N Point down-converted signal to form a signal vector , and perform cyclic shift to form the sampling point matrix R: ； S12: Take N Point-length local PRN code vector , and perform cyclic shift on it to obtain the code matrix C: 。 8. An electronic device, characterized in that: include: one or more processors; a memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having computer instructions stored thereon, characterized in that: When the instruction is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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