Signal capturing method and device, electronic equipment, storage medium and program product

Abstract

The embodiment of the invention provides a signal capturing method and device, electronic equipment, a storage medium and a program product, and the method comprises the steps: obtaining a correlation matrix and a measurement matrix after the sparse transformation processing of an initial global navigation satellite system signal, and carrying out the multiplication processing of the measurement matrix and the correlation matrix, and obtaining an observation matrix. And determining a Lipschitz constant according to the measurement matrix, and performing reconstruction processing according to the Lipschitz constant, the observation matrix, the measurement matrix, preset parameter data and preset sparsity data to obtain a target correlation matrix after reconstruction processing. And carrying out peak detection on the target correlation matrix to obtain a detection result which is used for indicating whether the global navigation satellite system signal is successfully captured or not. According to the invention, the signal capturing success rate and accuracy of the global navigation satellite system are improved.

Description

Technical Field

[0001] This application relates to the field of signal processing technology, and in particular to a signal acquisition method, apparatus, electronic device, storage medium, and program product. Background Technology

[0002] Global Navigation Satellite System (GNSS) is widely used in high-dynamic scenarios, where receivers need to quickly acquire weak and frequency-shifted GNSS signals to achieve accurate positioning.

[0003] In related technologies, greedy algorithms or iterative shrinking threshold algorithms are usually used to reconstruct the initial GNSS signal, and the target correlation matrix after reconstruction is used to capture GNSS signals in high dynamic scenes.

[0004] However, the target correlation matrix reconstructed in the above method is prone to failure, which leads to poor GNSS signal acquisition performance. Summary of the Invention

[0005] This application provides a signal acquisition method, apparatus, electronic device, storage medium, and program product to improve the success rate of target correlation matrix creation and thus enhance the GNSS signal acquisition effect.

[0006] In a first aspect, embodiments of this application provide a signal acquisition method, comprising:

[0007] Obtain the correlation matrix and measurement matrix after sparse transformation processing of the initial Global Navigation Satellite System (GNSS) signal;

[0008] The measurement matrix is ​​multiplied by the correlation matrix to obtain the observation matrix;

[0009] Determine the Lipschitz constant based on the measurement matrix;

[0010] The target correlation matrix is ​​reconstructed based on the Lipschitz constant, observation matrix, measurement matrix, preset parameter data, and preset sparsity data.

[0011] Peak detection is performed on the target correlation matrix to obtain the detection result, which is used to indicate whether the GNSS signal has been successfully captured.

[0012] In one possible implementation, determining the Lipschitz constant based on the measurement matrix includes:

[0013] Determine the transpose of the measurement matrix;

[0014] The measurement matrix is ​​multiplied by the transpose matrix, and the resulting matrix is ​​determined as the Gram matrix.

[0015] Solve for the spectral norm of the Gram matrix to determine the Lipschitz constant.

[0016] In one possible implementation, a reconstruction process is performed based on the Lipschitz constant, the observation matrix, the measurement matrix, preset parameter data, and preset sparsity data to obtain a reconstructed target correlation matrix, including:

[0017] Based on the observation matrix, measurement matrix, and preset parameter data, determine the regularization parameters;

[0018] Based on the regularization parameter, the measurement matrix, and the observation matrix, a minimum absolute shrinkage and selection operator LASSO model is constructed.

[0019] The LASSO model is solved using the Lipschitz constant and regularization parameter to obtain the calculated correlation matrix.

[0020] The reconstructed target correlation matrix is ​​obtained based on the calculated correlation matrix.

[0021] In one possible implementation, regularization parameters are determined based on the observation matrix, the measurement matrix, and preset parameter data, including:

[0022] The initial correlation matrix is ​​obtained by back-projecting the observation matrix from the measurement matrix.

[0023] The initial correlation matrix is ​​compressed based on preset parameter data to obtain regularization parameters.

[0024] In one possible implementation, a minimum absolute shrinkage and selection operator LASSO model is constructed based on the regularization parameter, the measurement matrix, and the observation matrix, including:

[0025] The regularization parameter is determined as the judgment threshold;

[0026] Set the data in the initial correlation matrix that is less than the judgment threshold to 0;

[0027] The initial shrinkage matrix is ​​obtained by calculating the ratio of the absolute values ​​of the data in the initial correlation matrix that are greater than or equal to the judgment threshold to their respective data values;

[0028] The initial shrinkage matrix, regularization parameters, measurement matrix, and observation matrix are input into the preset LASSO algorithm to construct the minimum absolute shrinkage and selection operator LASSO model.

[0029] In one possible implementation, the LASSO model is solved based on the Lipschitz constant and regularization parameters to obtain the calculated correlation matrix, including:

[0030] Determine the reciprocal of the Lipschitz constant, which is the step size of a single gradient descent iteration;

[0031] The ratio of the regularization parameter to the Lipschitz constant is determined as the shrinkage threshold;

[0032] Based on the reciprocal and the shrinkage threshold, the LASSO model is iteratively calculated to obtain the measured correlation matrix.

[0033] In one possible implementation, the reconstructed target correlation matrix is ​​obtained based on the calculated correlation matrix, including:

[0034] Based on preset sparse data, the calculated correlation matrix is ​​filtered to obtain the reconstructed target correlation matrix; or,

[0035] The calculated correlation matrix is ​​used as the initial shrinkage matrix for the next iteration until the preset iteration threshold is reached, thus obtaining the reconstructed target correlation matrix.

[0036] In one possible implementation, the calculated correlation matrix is ​​filtered based on preset sparsity data to obtain a reconstructed target correlation matrix, including:

[0037] For any column of the calculated correlation matrix, the number of data to be filtered is determined based on the preset sparsity data, and the columns of the calculated correlation matrix are used to indicate the phase offset of the code.

[0038] From the data contained in the column, according to the filtering rule of data from largest to smallest, a portion of the data corresponding to the number of data to be filtered is selected, and the remaining data in the column other than the portion of data is set to 0, thus obtaining the target correlation matrix after reconstruction.

[0039] In one possible implementation, peak detection is performed on the target correlation matrix to obtain the detection result, including:

[0040] The target correlation matrix is ​​input into a preset peak algorithm to obtain the first peak and the second peak.

[0041] Determine the peak ratio of the first peak to the second peak;

[0042] If the peak ratio is greater than or equal to the preset peak ratio threshold, the detection result indicates that the GNSS signal has been successfully captured.

[0043] If the peak ratio is less than the preset peak ratio threshold, the detection result indicates that the GNSS signal was not successfully captured.

[0044] Secondly, embodiments of this application provide a signal acquisition device, comprising:

[0045] The acquisition module is used to acquire the correlation matrix and the measurement matrix after sparse transformation processing of the initial Global Navigation Satellite System (GNSS) signal;

[0046] The processing module is used to multiply the measurement matrix and the correlation matrix to obtain the observation matrix;

[0047] The determination module is used to determine the Lipschitz constant based on the measurement matrix;

[0048] The processing module is also used to perform reconstruction processing based on the Lipschitz constant, observation matrix, measurement matrix, preset parameter data and preset sparsity data to obtain the reconstructed target correlation matrix.

[0049] The detection module is used to perform peak detection on the target correlation matrix and obtain the detection result, which is used to indicate whether the GNSS signal has been successfully captured.

[0050] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0051] The memory stores the instructions that the computer executes;

[0052] The processor executes computer execution instructions stored in memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0053] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0054] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0055] The signal acquisition method, apparatus, electronic device, storage medium, and program product provided in this application acquire a correlation matrix and a measurement matrix after sparse transformation processing of an initial Global Navigation Satellite System (GNSS) signal. The measurement matrix and correlation matrix are multiplied to obtain an observation matrix. Based on the measurement matrix, a Lipschitz constant is determined. Then, a reconstruction process is performed using the Lipschitz constant, the observation matrix, the measurement matrix, preset parameter data, and preset sparsity data to obtain a reconstructed target correlation matrix. Peak detection is performed on the target correlation matrix to obtain a detection result, which indicates whether the GNSS signal has been successfully acquired. The method of this application processes the initial GNSS signal into a correlation matrix, combines it with the measurement matrix to obtain an observation matrix, determines the Lipschitz constant based on the measurement matrix, and then uses this constant, the observation matrix, the measurement matrix, preset parameters, and sparsity data to reconstruct the target correlation matrix. This approach makes the reconstruction process more stable by relying on the Lipschitz constant, reducing the likelihood of reconstruction failures due to local selection errors or poor sparsity adaptation in related technologies. The resulting target correlation matrix can more accurately preserve signal peaks, thus improving the success rate and accuracy of GNSS signal acquisition. Attached Figure Description

[0056] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0057] Figure 1 This is a schematic diagram illustrating an exemplary application scenario shown in this application;

[0058] Figure 2 A schematic flowchart illustrating a signal acquisition method provided in an embodiment of this application;

[0059] Figure 3 A schematic diagram of a signal acquisition method provided in an embodiment of this application;

[0060] Figure 4 A schematic diagram illustrating the determination of the Lipschitz constant is provided for an embodiment of this application.

[0061] Figure 5 A schematic diagram illustrating how to obtain a target correlation matrix, as provided in an embodiment of this application;

[0062] Figure 6 A flowchart illustrating a method for obtaining a reconstructed target correlation matrix according to an embodiment of this application;

[0063] Figure 7 A flowchart illustrating a method for obtaining detection results provided in an embodiment of this application;

[0064] Figure 8 A schematic diagram comparing the capture performance of different CS algorithms provided in an embodiment of this application;

[0065] Figure 9A A capture-related peak spectrum of the method of this application is provided in an embodiment of this application;

[0066] Figure 9B A capture correlation peak spectrum of an orthogonal matching pursuit algorithm provided in this application embodiment;

[0067] Figure 9C Capture correlation peak spectrum of a fast iterative shrinkage threshold algorithm provided in this application embodiment;

[0068] Figure 10 A schematic diagram of the structure of a signal acquisition device provided in an embodiment of this application;

[0069] Figure 11 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0070] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0071] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0072] First, let me explain the terms used in this application:

[0073] Global Navigation Satellite System (GNSS): This is a general term for all types of global satellite navigation systems. In a GNSS signal system, the electromagnetic wave signal that a satellite sends to a ground receiver, carrying positioning and navigation information, is the core basis for the receiver to achieve positioning, velocity measurement, and time synchronization.

[0074] Doppler frequency offset: refers to the phenomenon that the frequency of the signal received by the receiver deviates from the frequency of the signal actually transmitted by the transmitter due to the relative motion between the signal transmitter and receiver.

[0075] Signal-to-noise ratio (SNR) is the ratio of signal power to noise power. A higher SNR indicates a higher proportion of effective information in the signal and less noise interference, allowing the receiver to more clearly identify and interpret the signal.

[0076] Pseudocode matrix: The pseudocode matrix is ​​a matrix generated by the pseudo-random code (pseudocode) of GNSS satellites. Each column (or row) of the matrix corresponds to a signal segment of the pseudocode under different code phases.

[0077] Frequency offset matrix: It is a matrix generated by the signal components related to Doppler frequency offset. Each column (or row) of the matrix corresponds to a signal segment under different frequency offset values.

[0078] Code phase search matrix: This is a feature matrix used in GNSS signal acquisition to traverse all possible code phase offsets.

[0079] Correlation matrix: In signal processing, especially in GNSS signal reconstruction scenarios, it is a matrix that describes the correlation between different feature components of a signal.

[0080] The observation matrix is ​​a core matrix in signal processing, especially in GNSS signal reconstruction scenarios, that connects the original signal with the actual measurement data. It is used to record the "observation results of the original signal after measurement" and is one of the input foundations of the signal reconstruction algorithm.

[0081] The Least Absolute Shrinkage and Selection Operator (LASSO) model is a regularization method commonly used in regression analysis. By adding an L1 regularization term to the loss function, it shrinks the model parameters and compresses unimportant feature coefficients to 0, thereby achieving the effect of feature selection. It is particularly useful when dealing with high-dimensional data.

[0082] In related technologies, greedy algorithms are typically used, such as Orthogonal Matching Pursuit (OMP) reconstruction algorithm, Regularized Orthogonal Matching Pursuit (ROMP) reconstruction algorithm, or Compressive Sampling Matching Pursuit (CoSaMP) reconstruction algorithm, to reconstruct the initial GNSS signal. Then, the target correlation matrix after reconstruction is used to capture GNSS signals in high dynamic scenes.

[0083] The OMP reconstruction algorithm progressively selects key atoms from an atom library containing different code phase and frequency offset features to approximate the true GNSS signal. An atom is a basic signal segment in the atom library representing a specific code phase-frequency offset combination, similar to the "basic building blocks" of the signal. The algorithm first initializes the support set, storing the selected atoms with empty indices and the residuals as the initial observation signal (GNSS measurement data containing noise). Then, in each iteration, since the residuals reflect the difference between the current approximation value and the true signal, the inner product of the residuals and all atoms in the atom library is calculated. A larger inner product indicates a stronger correlation between the atom (corresponding to a certain code phase-frequency offset) and the residual, making it more likely to be a component of the true signal. Therefore, the atom with the largest absolute value of the inner product is added to the support set. Next, to ensure a more accurate match between the atom combinations in the support set and the observed signal, the least squares method is used to solve for the linear combination coefficients of these atoms, obtaining the optimal approximation value. Finally, the residuals are updated to the orthogonal projection difference between the observed value and the approximation value, removing signal components already interpreted by the support set, ensuring that the new residuals only contain uncaptured signal information. Repeat the above process until the residual is small enough (or the preset number of iterations is reached). At this point, the atomic combination corresponding to the support set can reconstruct the target correlation matrix, which can then be used for code phase-frequency offset acquisition of GNSS signals.

[0084] The ROMP reconstruction algorithm first initializes the support set to empty, with the residual being the initial GNSS observation signal. During iteration, it first calculates the inner product of the residual and all atoms, selecting the atom with the largest inner product as the "seed atom" because it has the strongest correlation with the residual and is most likely to correspond to the characteristics of the real signal. Next, multiple atoms with energy close to the seed atom are selected to form a regularized candidate set. This is to avoid misselection due to noise interference when selecting a single atom, and to improve the robustness of the selection through multiple atom candidates. Then, the atoms in the candidate set are orthogonally projected to eliminate the correlation between atoms, ensuring that the selected atoms can independently reflect the signal characteristics and meet the sparsity requirements of the GNSS signal. Next, the atom that contributes the most to signal approximation is selected from the projected candidate set and added to the support set. The combination coefficients of the atoms in the support set are solved using the least squares method to obtain the approximation value of the real signal. Finally, the residual is updated to the difference between the observed value and the approximation value. This iteration is repeated until the residual is sufficiently small or the upper limit of iterations is reached. Finally, the target correlation matrix is ​​reconstructed through the atomic combinations corresponding to the support set, which is used for code phase and frequency offset acquisition of GNSS signals in high-dynamic scenarios.

[0085] The CoSaMP reconstruction algorithm first initializes the support set to empty, with the residual being the initial GNSS observation signal. It pre-determines the number K key atoms to retain. During iteration, it first calculates the inner product of the residual and all atoms, selecting the top 2K atoms with the largest inner product. Since this selection exceeds K, it covers more potentially effective atoms, avoiding the omission of true signal features. These atoms are then combined with the current support set to form a temporary support set. This integrates historically selected atoms with currently highly correlated atoms, ensuring information integrity. Next, the least squares method is used to solve for the combination coefficients of atoms in the temporary support set, quantifying the contribution of each atom to the signal. Then, "pruning" is applied to retain the top K atoms with the largest coefficients as a new support set, strictly satisfying signal sparsity requirements and removing redundant atoms. Finally, the residual is updated to the difference between the observed value and the reconstructed approximation value from the new support set. This iteration is repeated until the residual converges or the upper limit of iterations is reached. Ultimately, the target correlation matrix is ​​reconstructed using the atomic combinations corresponding to the support set, used for code phase-frequency offset acquisition of GNSS signals in high-dynamic scenarios.

[0086] However, the aforementioned OMP, ROMP, and CoSaMP algorithms all exhibit poor noise tolerance. Under low signal-to-noise ratio (SNR) conditions, noise interferes with the inner product calculation of atoms and residuals, causing the algorithms to mistakenly select noise-related atoms as real signal atoms. Incorrectly selected atoms directly reduce the reconstruction accuracy of the correlation matrix, ultimately degrading reconstruction performance. When signal sparsity is low, the algorithm's atom selection strategy relies on progressively filtering highly correlated atoms. Low sparsity means more atoms are needed to fully represent the signal, but the algorithm may not be able to select a sufficient number of effective atoms, leading to incomplete signal representation and underfitting. Furthermore, all three algorithms follow a locally optimal (currently optimal) selection logic. This strategy cannot guarantee global optimality. If the algorithm incorrectly selects atoms in early iterations due to noise interference or sparsity adaptation issues, subsequent iterations only update the residuals and support sets based on historically selected atoms, failing to backtrack and correct early selection errors. This ultimately causes the algorithm to converge to an incorrect support set, which cannot accurately reflect the code phase-frequency offset characteristics of the real signal, directly causing correlation matrix reconstruction failure and resulting in poor GNSS signal acquisition.

[0087] In related technologies, the Iterative Shrinkage-Thresholding Algorithm (ISTA) or the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) can also be used to reconstruct the initial GNSS signal, and then the target correlation matrix after reconstruction can be used to capture GNSS signals in high dynamic scenes.

[0088] The ISTA algorithm employs gradient descent, adjusting the signal in the opposite direction of the gradient based on the error between the current reconstruction result and the observed data, thereby reducing the fitting error and approximating the observation constraints. It also performs a soft thresholding operation, setting a threshold to zero for elements in the signal whose absolute value is less than the threshold, while retaining key elements to meet sparsity requirements. These two processes iterate alternately, balancing data fitting accuracy with signal sparsity, to gradually approach the optimal solution to the regularization problem, ultimately enabling the stable reconstruction of a sparse signal from a limited number of observations.

[0089] The FISTA algorithm adds an acceleration gradient method to the ISTA algorithm to speed up the convergence of ISTA, thereby further reducing the reconstruction error and improving the reconstruction success rate.

[0090] However, while the ISTA and FISTA algorithms are highly tolerant of noise and can reconstruct signals more stably in noisy environments, the ISTA algorithm has a slower convergence speed. The FISTA algorithm, on the other hand, is very sensitive to the choice of regularization parameters, which can easily lead to poor accuracy of the reconstructed target correlation matrix, resulting in poor GNSS signal acquisition results.

[0091] Therefore, to address the aforementioned problems, this application proposes a signal acquisition method. This method involves performing a sparse transform on the initial GNSS signal to obtain a correlation matrix, then combining this with the measurement matrix to construct an observation matrix. Based on the measurement matrix, a Lipschitz constant is determined, and this Lipschitz constant, along with the observation matrix, measurement matrix, preset parameter data, and sparsity data, are incorporated into the reconstruction process. This reconstruction method leverages the Lipschitz constant to ensure the stability and convergence of the iterations. By precisely controlling the sparsity characteristics of the reconstruction matrix through preset sparsity data, it reduces the risk of reconstruction failure due to insufficient sparsity adaptation. The resulting target correlation matrix more accurately preserves the peak characteristics of the signal, thus providing a reliable foundation for subsequent peak detection and improving the success rate and accuracy of GNSS signal acquisition.

[0092] To facilitate a better understanding of the method in this application, the following examples illustrate its application scenarios, such as... Figure 1 As shown, Figure 1 This is a schematic diagram of an exemplary application scenario shown in this application. The signal acquisition system corresponding to this application scenario may include a satellite 10 and a server 11.

[0093] Satellite 10 acquires the initial Global Navigation Satellite System (GNSS) signal and transmits it to server 11. Server 11 performs sparse transform processing on the initial GNSS signal to obtain the correlation matrix and measurement matrix. Based on the measurement matrix, it determines the observation matrix and Lipschitz constant. Then, based on the Lipschitz constant, observation matrix, measurement matrix, preset parameter data, and preset sparsity data, it performs reconstruction processing to obtain the reconstructed target correlation matrix. Peak detection is then performed on this matrix to obtain the detection result indicating whether the GNSS signal acquisition was successful.

[0094] It is understood that this application does not limit the type and number of satellites 10 and servers 11. The above scenario is only for illustrative purposes, and the specific application can be limited according to actual circumstances.

[0095] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0096] Please see Figure 2 , Figure 2 This is a flowchart illustrating a signal acquisition method provided in an embodiment of this application. The execution subject of this method can be a signal acquisition device. The signal acquisition device can be implemented through a computer program, or through a medium storing the relevant computer program, such as a USB flash drive and / or optical disc, or through a physical device integrating or installing the relevant computer program, such as a chip or electronic device. The electronic device can be a server, server cluster, smart terminal, etc. The method can include the following steps:

[0097] S201. Obtain the correlation matrix and measurement matrix after sparse transformation processing of the initial Global Navigation Satellite System (GNSS) signal.

[0098] The following will combine Figure 3 The method of this embodiment will be described below. Figure 3 This is a schematic diagram of a signal acquisition method provided in an embodiment of this application.

[0099] Acquiring initial Global Navigation Satellite System (GNSS) signals The initial GNSS signal can be a digital intermediate frequency signal, etc. The initial GNSS signal is vector-transformed using the following formulas (1) to (2) to obtain the signal vector. .

[0100] (1)

[0101] (2)

[0102] in, This represents the nth time series index of the initial GNSS signal; Indicates the frequency of the initial GNSS signal; This indicates the Doppler frequency offset of the initial GNSS signal; The initial phase of the initial GNSS signal is represented by L; the length of the received pseudocode is represented by L.

[0103] In the pseudocode matrix generation section, the local pseudocode module generates a pseudocode vector of length L corresponding to the symbol length through a pseudocode generator, and then constructs the pseudocode matrix by shifting this pseudocode vector. .

[0104]

[0105] in, Represents code elements.

[0106] In the frequency offset matrix generation section, firstly, the preset frequency offset search range is... conduct Divide into equal parts, and then construct Doppler frequency offset vectors for different search frequencies using the following formula (3). And constructed using the following formula (4) The frequency offset matrix is ​​synthesized from the Doppler frequency offset vectors. .

[0107] (3)

[0108] (4)

[0109] in, Indicates the pseudocode sampling period; This represents the m-th search frequency index.

[0110] Furthermore, based on the pseudocode matrix Sum of frequency offset matrix The initial GNSS signal is subjected to sparse transform processing to obtain the correlation matrix after sparse transform processing.

[0111] Specifically, the following formula (5) explains the application of the pseudocode matrix. and signal vector Determine the code phase search matrix .

[0112] (5)

[0113] The following formula (6) illustrates the application of the frequency offset matrix. Sum of codes phase search matrix Determine the correlation matrix after sparse transformation. .

[0114] (6)

[0115] The core characteristics of GNSS signals are pseudocode phase and Doppler frequency offset. However, the original downsampled signal, i.e., the initial GNSS signal, contains noise, and directly processing this signal would result in a large computational burden. By constructing a pseudocode matrix and a frequency offset matrix, the downsampled signal is transformed into a correlation matrix. Each element in this correlation matrix represents the correlation between a specific code phase and a specific frequency offset, and only the positions corresponding to the true parameters show significant peaks. Mapping the initial GNSS signal to the code phase-frequency offset parameter space can highlight the sparsity of the signal, thus laying the foundation for subsequent compression and reconstruction.

[0116] The measurement matrix (also known as a random Gaussian matrix) is generated based on a pre-defined random generation strategy. .

[0117] S202. Multiply the measurement matrix and the correlation matrix to obtain the observation matrix.

[0118] Using measurement matrix The correlation matrix after sparse transformation Perform compression measurements.

[0119] Specifically, based on the measurement matrix, the following formula (7) is used. For the correlation matrix after sparse transformation Compressed measurements were performed to obtain the observation matrix. .

[0120] (7)

[0121] In highly dynamic scenarios, the dimensionality of the correlation matrix can be very high, and direct transmission or storage would consume a lot of resources. By compressing the correlation matrix with a random Gaussian matrix and based on a small number of random linear measurements, the high-dimensional correlation matrix can be transformed into low-dimensional observation data. At the same time, the key information required for reconstruction can be retained, thereby significantly reducing the amount of data and reducing the computational and storage pressure to meet the real-time requirements of highly dynamic scenarios.

[0122] S203. Determine the Lipschitz constant based on the measurement matrix.

[0123] Please see Figure 4 , Figure 4 This diagram illustrates the determination of the Lipschitz constant, as provided in an embodiment of this application. Figure 4 As shown, according to the measurement matrix Determine the transpose of the measurement matrix. .

[0124] The measurement matrix is ​​obtained by using the following formula (8). With transpose matrix Perform multiplication, and determine the resulting matrix as a Gram matrix. .

[0125] (8)

[0126] The spectral norm of the Gram matrix is ​​solved using the following formula (9), and the Lipschitz constant is determined.

[0127] (9)

[0128] The Lipschitz constant is an indicator that measures the "rate of change" of a function and describes the "smoothness" of the function within its domain. It is determined by calculating the spectral norm of the Gram matrix in order to improve the convergence, stability, and efficiency of subsequent reconstruction algorithms.

[0129] S204. Based on the Lipschitz constant, observation matrix, measurement matrix, preset parameter data, and preset sparsity data, the target correlation matrix is ​​reconstructed to obtain the reconstructed target correlation matrix.

[0130] Please see Figure 5 , Figure 5 This is a schematic diagram illustrating how to obtain a target correlation matrix according to an embodiment of this application, where the observation matrix... The parameters can be adjusted using the preset parameter data. The preset sparsity data is the sparsity K, and the measurement matrix is... The Lipschitz constant is used as the input to the correlation matrix reconstruction part. Then, the LASSO model is solved by continuous asymptotic threshold shrinkage and single gradient descent to obtain the reconstructed target correlation matrix.

[0131] S205. Perform peak detection on the target correlation matrix to obtain the detection result, which is used to indicate whether the GNSS signal has been successfully captured.

[0132] The target correlation matrix is ​​input into the peak detection module to obtain the first peak value (main peak value) and the second peak value (secondary peak value). The detection result is determined based on the main peak value and the secondary peak value. The detection result can indicate whether the GNSS signal was successfully captured or not.

[0133] If the GNSS signal is successfully acquired, the code phase delay and Doppler frequency offset are obtained based on the target correlation matrix. If the GNSS signal is not successfully acquired, the GNSS signal is reacquired according to the method of this embodiment.

[0134] In the above embodiments of this application, the initial GNSS signal is processed into a correlation matrix, and an observation matrix is ​​obtained by combining it with the measurement matrix. A Lipschitz constant is also determined based on the measurement matrix. This constant, the observation matrix, the measurement matrix, preset parameters, and sparsity data are then used to reconstruct the target correlation matrix. This method relies on the Lipschitz constant to make the reconstruction process more stable, reducing the likelihood of reconstruction failures due to local selection errors or poor sparsity adaptation in correlation techniques. The resulting target correlation matrix more accurately preserves signal peaks, improving both the success rate and accuracy of GNSS signal acquisition.

[0135] Furthermore, based on the above embodiments, the following embodiments illustrate the process of reconstructing the target correlation matrix by using the Lipschitz constant, observation matrix, measurement matrix, preset parameter data, and preset sparsity data.

[0136] Please see Figure 6 , Figure 6 A flowchart illustrating a method for obtaining a reconstructed target correlation matrix, provided in this application embodiment, includes the following steps:

[0137] S601. Determine the regularization parameters based on the observation matrix, measurement matrix, and preset parameter data.

[0138] According to the following formula (10), based on the measurement matrix For the observation matrix Perform back projection to obtain the initial correlation matrix. .

[0139] (10)

[0140] The initial correlation matrix is ​​compressed based on preset parameter data to obtain regularization parameters.

[0141] Specifically, the parameters can be adjusted according to the preset parameter data using the following formula (11). For the initial correlation matrix The peak value of the main peak is compressed to determine the regularization parameter. .

[0142] (11)

[0143] Among them, adjustable parameters The value range is (0, 1); Represents the initial correlation matrix The element in the i-th row and j-th column, and satisfying .

[0144] In related technologies, the FISTA algorithm is highly sensitive to the choice of regularization parameters. Typically, regularization parameters need to be selected through experience, cross-validation, or heuristic rules based on noise levels, thus increasing the complexity of using the FISTA algorithm. When the regularization parameter is too large, the signal amplitude is excessively compressed or zeroed out, leading to the loss of information in the estimated correlation matrix. When the regularization parameter is too small, the sparsity penalty is insufficient, and the reconstructed signal contains too much noise or artifacts, reducing the correlation matrix reconstruction performance.

[0145] In this application, an initial correlation matrix is ​​obtained by back-projecting the observation matrix using the measurement matrix. Then, an adjustable parameter with a value range of (0, 1) is used to compress the peak value of the initial correlation matrix to determine the regularization parameter. This process is based on actual data such as the observation and measurement matrices, eliminating the need for experience or complex verification, thus significantly reducing the complexity of parameter selection. Simultaneously, the precise compression of the peak value through the adjustable parameter reduces information loss caused by an excessively large regularization parameter and noise interference caused by an excessively small regularization parameter, allowing the regularization parameter to better match the actual signal characteristics, thereby effectively improving the stability and accuracy of the correlation matrix reconstruction.

[0146] S602. Based on the regularization parameter, measurement matrix, and observation matrix, construct the LASSO model of minimum absolute contraction and selection operator.

[0147] regularization parameters The initial correlation matrix is ​​determined as the threshold for judgment. Data values ​​below the judgment threshold are set to 0, and the initial correlation matrix is ​​adjusted. The ratio of the data points greater than or equal to the judgment threshold to their respective absolute values ​​is calculated in the initial correlation matrix. Mid-peak Elements greater than or equal to the threshold value Continuous asymptotic threshold shrinkage is performed to obtain the initial shrinkage matrix. .

[0148] Specifically, the initial contraction matrix is ​​obtained through the following formulas (12) and (13). .

[0149] (12)

[0150] (13)

[0151] in, Represents the initial shrinkage matrix The element in the i-th row and j-th column.

[0152] The initial shrinkage matrix Regularization parameters Measurement matrix and observation matrix Input the data into the preset LASSO algorithm to construct the minimum absolute shrinkage and selection operator LASSO model.

[0153] Specifically, the LASSO model of minimum absolute contraction and selection operator is constructed using the following formula (14). :

[0154] (14)

[0155] S603. Solve the LASSO model based on the Lipschitz constant and regularization parameters to obtain the calculated correlation matrix.

[0156] Determine the reciprocal of the Lipschitz constant ,in, For the step size of a single gradient descent iteration calculated iteratively, let Let the step size be the gradient descent step size for the initial shrinkage matrix. Perform gradient descent.

[0157] The ratio of the regularization parameter to the Lipschitz constant is determined as the shrinkage threshold. Based on the reciprocal and the shrinkage threshold, the LASSO model is iteratively calculated to obtain the measured correlation matrix. .

[0158] Specifically, the calculated correlation matrix is ​​obtained through the following formulas (15) to (17). :

[0159] (15)

[0160] (16)

[0161] (17)

[0162] in, This represents the gradient of the objective function; Indicates the gradient descent step size; Represents the initial shrinkage matrix The new matrix after gradient descent; Representation matrix The element in the i-th row and j-th column; Represents the estimated correlation matrix The element in the i-th row and j-th column.

[0163] S604. Obtain the reconstructed target correlation matrix based on the calculated correlation matrix.

[0164] Based on the preset sparsity data, i.e., sparsity K, the calculated correlation matrix is... After filtering, the reconstructed target relevance matrix is ​​obtained. .

[0165] For any column of the calculated correlation matrix, the number of data to be filtered is determined according to the preset sparsity data. The column of the calculated correlation matrix is ​​used to indicate the phase offset of the indicator code. From the data contained in the column, according to the filtering rule of data from large to small, a portion of the data corresponding to the number of data to be filtered is filtered out, and the remaining data in the column except for the portion of the data is set to 0, so as to obtain the target correlation matrix after reconstruction.

[0166] Specifically, the reconstructed target correlation matrix is ​​obtained through the following formula (18). :

[0167] (18)

[0168] in, Represents the reconstructed correlation matrix The element in the i-th row and j-th column.

[0169] Compared with the ISTA algorithm, the above reconstruction process only requires one gradient descent, two threshold shrinkages, and one K-sparse step, without any iterative loops, thus improving the real-time performance of GNSS signal acquisition.

[0170] Alternatively, the calculated correlation matrix can be used as the initial shrinkage matrix for the next iteration, and the steps in S603 above can be repeated until the iteration reaches the preset iteration threshold to obtain the reconstructed target correlation matrix.

[0171] Due to the initial shrinkage matrix Since a threshold shrinkage has already been performed, the correlation matrix can be reconstructed with a smaller number of iterations to obtain the reconstructed target correlation matrix.

[0172] In the embodiments described above, regularization parameters are determined based on the observation matrix, measurement matrix, and preset parameter data. This reduces the subjectivity and complexity of relying on experience to select regularization parameters in related technologies, ensuring that the parameters are adapted to the actual signal characteristics. A LASSO model is constructed based on these regularization parameters, the measurement matrix, and the observation matrix. Leveraging the sparsity constraint characteristics of the model, noise interference can be effectively filtered out, while retaining key signal features. By collaboratively solving the LASSO model using the Lipschitz constant and the regularization parameters, both the stability and convergence speed of the model solution are guaranteed, and the accuracy of the calculated correlation matrix is ​​improved, ultimately yielding the target correlation matrix. The entire process tightly integrates parameter determination, model construction, and accurate solution, not only reducing the difficulty of correlation matrix reconstruction but also minimizing information loss and suppressing noise effects, thereby improving the reconstruction quality of the target correlation matrix and providing reliable data support for subsequent GNSS signal acquisition in high-dynamic scenarios.

[0173] Furthermore, based on any of the above embodiments, the following examples illustrate the process of peak detection on the target correlation matrix to obtain the detection results. Please refer to... Figure 7 , Figure 7 This application provides a flowchart illustrating a method for obtaining detection results, which may include the following steps:

[0174] S701. Input the target correlation matrix into the preset peak algorithm to obtain the first peak and the second peak.

[0175] The reconstructed correlation matrix The input peak detection module performs detection and judgment to obtain the first peak value, i.e. the main peak value, and the second peak value, i.e. the secondary peak value.

[0176] S702. Determine the peak ratio of the first peak value to the second peak value.

[0177] The ratio of the peak values ​​of the primary peak and the secondary peak is determined based on their respective peak values.

[0178] S703. If the peak ratio is greater than or equal to the preset peak ratio threshold, the detection result indicates that the GNSS signal has been successfully captured.

[0179] If the peak ratio is greater than or equal to the preset peak ratio threshold, it indicates that the GNSS signal has been successfully captured, and the corresponding code phase delay and Doppler frequency offset are output for positioning.

[0180] S704. If the peak ratio is less than the preset peak ratio threshold, the detection result indicates that the GNSS signal was not successfully captured.

[0181] If the peak ratio is less than the preset peak ratio threshold, it means that the GNSS signal has not been successfully captured and needs to be recaptured according to the method of this application.

[0182] In the above embodiments of this application, by inputting the target correlation matrix into a preset peak algorithm, the first peak and the second peak are obtained simultaneously. Then, the peak ratio of the two is calculated as the judgment criterion, effectively reducing the limitation of a single peak being affected by noise fluctuations and signal strength changes. By comparing with a preset peak ratio threshold, the boundary between successful and failed signal acquisition is clearly defined. When the ratio meets the threshold, it indicates that the real signal peak is significantly higher than the interference peak, and successful acquisition can be determined. Otherwise, it is determined that acquisition has not been successful, thereby improving the accuracy of GNSS signal acquisition and detection in high dynamic scenarios.

[0183] The effects of this application will be illustrated below with specific examples.

[0184] Taking a Doppler frequency offset of 180kHz as an example, in the simulation of the capture performance of the FISTA reconstructed compressed sensing (CS) algorithm, the number of iterations were selected as 5, 10, 20 and 30 respectively, and the regularization parameter λ was set to 500 and 1500 according to empirical values.

[0185] Please see Figure 8 , Figure 8 This diagram illustrates a comparison of the acquisition performance of different CS algorithms provided in this application embodiment. The horizontal axis represents the signal-to-noise ratio (SNR), and the vertical axis represents the detection probability of the GNSS signal. The detection probability indicates the probability that the GNSS signal is successfully detected. Figure 1 illustrates the method of this application; Figure 2 illustrates the CS algorithm reconstructed by OMP; Figure 3 illustrates the CS algorithm reconstructed by FISTA with 5 iterations (I=5); Figure 4 illustrates the CS algorithm reconstructed by FISTA with 10 iterations (I=10, λ=1500); Figure 5 illustrates the CS algorithm reconstructed by FISTA with 20 iterations (I=20); Figure 6 illustrates the CS algorithm reconstructed by FISTA with 30 iterations (I=30); and Figure 7 illustrates the CS algorithm reconstructed by FISTA with 10 iterations (I=10, λ=1500).

[0186] When the compression ratio is 1 / 2, from Figure 8 As can be seen, compared with the CS algorithm reconstructed by OMP, the CS algorithm of this application significantly improves the acquisition performance of GNSS signals due to its effective noise suppression capability.

[0187] When the detection probability is 90%, the SNR threshold of the CS algorithm in this application is lower than that of other algorithms.

[0188] When the regularization parameter is set to 500, the capture performance of the FISTA-reconstructed CS algorithm improves with the number of iterations. However, when the number of iterations is ≥10, the FISTA algorithm gets trapped in a local optimum, causing the capture performance to no longer improve with the number of iterations. When the regularization parameter is modified to 1500, after 10 iterations, the capture performance of the FISTA-reconstructed CS algorithm is further improved compared to 10 iterations (regularization parameter 500). This indicates that the capture performance of the FISTA-reconstructed CS algorithm is easily affected by the regularization parameter; even after 10 iterations, the SNR threshold of this algorithm is still higher than that of the CS algorithm reconstructed in this application.

[0189] Please see Figure 9A , Figure 9A This application provides a capture-related peak spectrum of a method according to an embodiment of the present application. Figure 9B This application provides a capture correlation peak spectrum for an orthogonal matching pursuit algorithm, as shown in the embodiments of this application. Figure 9C The capture correlation peak spectrum is provided for a fast iterative shrinkage threshold algorithm according to an embodiment of this application.

[0190] according to Figure 9A , Figure 9B as well as Figure 9C As shown in the figures, the OMP-reconstructed CS algorithm captures cluttered signals and fails to capture GNSS signals. Although the FISTA-reconstructed CS algorithm can still achieve correct signal capture, its peak-to-peak ratio is lower than that of the CS algorithm in this application, approximately 0.4. Therefore, under the same conditions, the correlation peaks (i.e., the main and secondary peaks) obtained by the method in this application have better performance, more accurate data, and thus more accurate detection results are obtained based on accurate main and secondary peak values.

[0191] This application also provides a signal acquisition device, please refer to [link to relevant documentation]. Figure 10 , Figure 10 A schematic diagram of a signal acquisition device provided in this application embodiment includes:

[0192] The acquisition module 1001 is used to acquire the correlation matrix and the measurement matrix after sparse transformation processing of the initial Global Navigation Satellite System (GNSS) signal.

[0193] The processing module 1002 is used to multiply the measurement matrix and the correlation matrix to obtain the observation matrix.

[0194] Module 1003 is used to determine the Lipschitz constant based on the measurement matrix.

[0195] The processing module 1002 is also used to perform reconstruction processing based on the Lipschitz constant, observation matrix, measurement matrix, preset parameter data and preset sparsity data to obtain the reconstructed target correlation matrix.

[0196] The detection module 1004 is used to perform peak detection on the target correlation matrix and obtain the detection result, which is used to indicate whether the GNSS signal has been successfully captured.

[0197] In one possible implementation, the determining module 1003 is specifically used for:

[0198] Determine the transpose of the measurement matrix.

[0199] The measurement matrix is ​​multiplied by the transpose matrix, and the resulting matrix is ​​determined as the Gram matrix.

[0200] Solve for the spectral norm of the Gram matrix to determine the Lipschitz constant.

[0201] In one possible implementation, the processing module 1002 is further specifically used for:

[0202] The regularization parameters are determined based on the observation matrix, measurement matrix, and preset parameter data.

[0203] Based on the regularization parameter, the measurement matrix, and the observation matrix, a minimum absolute shrinkage and selection operator LASSO model is constructed.

[0204] The LASSO model is solved using the Lipschitz constant and regularization parameters to obtain the calculated correlation matrix.

[0205] The reconstructed target correlation matrix is ​​obtained based on the calculated correlation matrix.

[0206] In one possible implementation, the processing module 1002 is further specifically used for:

[0207] The initial correlation matrix is ​​obtained by back-projecting the observation matrix onto the measurement matrix.

[0208] The initial correlation matrix is ​​compressed based on preset parameter data to obtain regularization parameters.

[0209] In one possible implementation, the processing module 1002 is further specifically used for:

[0210] The regularization parameter is determined as the judgment threshold.

[0211] Set the data in the initial correlation matrix that are less than the judgment threshold to 0.

[0212] The initial shrinkage matrix is ​​obtained by calculating the ratio of the absolute values ​​of the data in the initial correlation matrix that are greater than or equal to the judgment threshold to their respective data values.

[0213] The initial shrinkage matrix, regularization parameters, measurement matrix, and observation matrix are input into the preset LASSO algorithm to construct the minimum absolute shrinkage and selection operator LASSO model.

[0214] In one possible implementation, the processing module 1002 is further specifically used for:

[0215] Determine the reciprocal of the Lipschitz constant, which is the step size of a single gradient descent iteration.

[0216] The ratio of the regularization parameter to the Lipschitz constant is determined as the shrinkage threshold.

[0217] Based on the reciprocal and the shrinkage threshold, the LASSO model is iteratively calculated to obtain the measured correlation matrix.

[0218] In one possible implementation, the processing module 1002 is further specifically used for:

[0219] Based on preset sparse data, the calculated correlation matrix is ​​filtered to obtain the reconstructed target correlation matrix. Alternatively,

[0220] The calculated correlation matrix is ​​used as the initial shrinkage matrix for the next iteration until the preset iteration threshold is reached, thus obtaining the reconstructed target correlation matrix.

[0221] In one possible implementation, the processing module 1002 is further specifically used for:

[0222] For any column of the calculated correlation matrix, the number of data to be filtered is determined based on the preset sparsity data, and the column of the calculated correlation matrix is ​​used to indicate the phase offset of the code.

[0223] From the data contained in the column, according to the filtering rule of data from largest to smallest, a portion of the data corresponding to the number of data to be filtered is selected, and the remaining data in the column other than the portion of data is set to 0, thus obtaining the target correlation matrix after reconstruction.

[0224] In one possible implementation, the detection module 1004 is specifically used for:

[0225] The target correlation matrix is ​​input into a preset peak algorithm to obtain the first peak and the second peak.

[0226] Determine the peak ratio of the first peak to the second peak.

[0227] If the peak ratio is greater than or equal to the preset peak ratio threshold, the detection result indicates that the GNSS signal has been successfully captured.

[0228] If the peak ratio is less than the preset peak ratio threshold, the detection result indicates that the GNSS signal was not successfully captured.

[0229] The signal acquisition device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0230] Figure 11 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 11 As shown, the electronic device provided in this embodiment includes at least one processor 1101 and a memory 1102. Optionally, the device further includes a communication component 1103. The processor 1101, memory 1102, and communication component 1103 are connected via a bus 1104.

[0231] In a specific implementation, at least one processor 1101 executes computer execution instructions stored in memory 1102, causing at least one processor 1101 to perform the above-described method.

[0232] The specific implementation process of processor 1101 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0233] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0234] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0235] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0236] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0237] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0238] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory, electrically erasable programmable read-only memory, erasable programmable read-only memory, programmable read-only memory, read-only memory, magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0239] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0240] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0241] The units described 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 can be selected to achieve the purpose of this embodiment according to actual needs.

[0242] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0243] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0244] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0245] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method of acquisition of a signal, characterized in that, The method comprises the following steps: obtaining a correlation matrix after sparse transformation processing of an initial global navigation satellite system (GNSS) signal and a measurement matrix; multiplying the measurement matrix and the correlation matrix to obtain an observation matrix; determining a Lipschitz constant according to the measurement matrix; performing reconstruction processing according to the Lipschitz constant, the observation matrix, the measurement matrix, preset parameter data and preset sparsity data to obtain a target correlation matrix after reconstruction processing; performing peak detection on the target correlation matrix to obtain a detection result, which is used to indicate whether the GNSS signal is successfully captured.

2. The method of claim 1, wherein, The method comprises the following steps: determining a transpose matrix of the measurement matrix; multiplying the measurement matrix and the transpose matrix to determine a Gram matrix; solving the spectral norm of the Gram matrix to determine the Lipschitz constant.

3. The method of claim 1, wherein, The method comprises the following steps: determining a regularization parameter according to the observation matrix, the measurement matrix and the preset parameter data; constructing a least absolute shrinkage and selection operator (LASSO) model according to the regularization parameter, the measurement matrix and the observation matrix; solving the LASSO model according to the Lipschitz constant and the regularization parameter to obtain a calculated correlation matrix; obtaining the target correlation matrix after reconstruction processing according to the calculated correlation matrix.

4. The method of claim 3, wherein, The method comprises the following steps: performing back projection on the observation matrix according to the measurement matrix to obtain an initial correlation matrix; performing compression processing on the initial correlation matrix according to the preset parameter data to obtain the regularization parameter.

5. The method of claim 4, wherein, The method comprises the following steps: determining the regularization parameter as a judgment threshold; setting data smaller than the judgment threshold in the initial correlation matrix to 0; performing ratio calculation on data greater than or equal to the judgment threshold in the initial correlation matrix and the respective corresponding data absolute values to obtain an initial shrinkage matrix; inputting the initial shrinkage matrix, the regularization parameter, the measurement matrix and the observation matrix into a preset LASSO algorithm to construct the LASSO model.

6. The method of claim 5, wherein, The method comprises the following steps: determining an inverse of the Lipschitz constant, which is a single gradient descent step length of iterative calculation; A ratio of the regularization parameter to the Lipschitz constant is determined as a shrinkage threshold; According to the reciprocal and the shrinkage threshold, the LASSO model is iteratively calculated to obtain a measured correlation matrix.

7. The method of claim 6, wherein, The target correlation matrix after reconstruction processing is obtained according to the measured correlation matrix, including: According to the preset sparsity data, the measured correlation matrix is filtered to obtain the target correlation matrix after reconstruction processing; or, The measured correlation matrix is determined as an initial shrinkage matrix for next iteration calculation until the iteration reaches a preset iteration threshold to obtain the target correlation matrix after reconstruction processing.

8. The method of claim 7, wherein, The target correlation matrix after reconstruction processing is obtained according to the measured correlation matrix, including: For any column of the measured correlation matrix, according to the preset sparsity data, a filtering data quantity is determined, and the column of the measured correlation matrix is used to indicate a code phase offset; According to a filtering rule that data is sorted from large to small, part of data corresponding to the filtering data quantity is filtered from the data contained in the column, and the remaining data in the column except the part of data is set to 0 to obtain the target correlation matrix after reconstruction processing.

9. The method according to any one of claims 1 to 8, characterized in that, The target correlation matrix is subjected to peak detection to obtain a detection result, including: The target correlation matrix is input into a preset peak algorithm to obtain a first peak value and a second peak value; A peak ratio of the first peak value to the second peak value is determined; If the peak ratio is greater than or equal to a preset peak ratio threshold, the detection result indicates that the GNSS signal is successfully captured; If the peak ratio is less than the preset peak ratio threshold, the detection result indicates that the GNSS signal is not successfully captured.

10. An apparatus for capturing a signal, characterized by Including: An acquisition module is configured to acquire a correlation matrix after sparse transformation processing of an initial global navigation satellite system (GNSS) signal and a measurement matrix; A processing module is configured to multiply the measurement matrix and the correlation matrix to obtain an observation matrix; A determination module is configured to determine a Lipschitz constant according to the measurement matrix; The processing module is further configured to perform reconstruction processing according to the Lipschitz constant, the observation matrix, the measurement matrix, preset parameter data and preset sparsity data to obtain a target correlation matrix after reconstruction processing; A detection module is configured to perform peak detection on the target correlation matrix to obtain a detection result, which is used to indicate whether the GNSS signal is successfully captured.

11. An electronic device, comprising: Including: A memory and a processor; The memory stores computer execution instructions; The processor executes the computer execution instructions stored in the memory, so that the processor performs the method in any one of claims 1-9.

12. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer execution instructions, and the computer execution instructions are executed by the processor to implement the method in any one of claims 1-9.

13. A computer program product, characterised in that, The computer program is executed by the processor to implement the method in any one of claims 1-9.

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