Improved gnss adaptive processing error correction method

Abstract

The application discloses an improved GNSS adaptive processing error correction method, relates to the field of array signal processing, and is based on sampling covariance matrix eigen decomposition, constructs a noise projection matrix, projects a correlation vector to a noise subspace, reduces interference components in the correlation vector, changes a time domain weighting structure, reduces the influence of a superposition multi-peak of a GNSS signal correlation function, and provides a high-precision interference suppression method; the application, on one hand, the weight value generated by the space-time center tap constraint can make the phase discrimination curve after superposition with the center correlation peak as a reference more stable; on the other hand, the projection matrix obtained through eigen decomposition of the covariance matrix solves the problem that the interference part in the covariance inverse matrix and the correlation vector is not orthogonal due to the center tap constraint matrix, reduces the residual interference generated, further reduces the interference components, and improves the convergence speed of the adaptive weight value.

Description

Technical Field

[0001] The present invention relates to the field of array signal processing, and in particular to an improved GNSS adaptive processing error correction method. Background Art

[0002] The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.

[0003] Satellite navigation signals are extremely weak when they reach the ground terminal, and are usually drowned out by noise, making them very susceptible to intentional and unintentional interference. With the gradual application of technologies such as nulling antennas, adaptive beamforming systems (such as Figure 2 (as shown in Figure 3), by weighting the received data of each array element to achieve space-time filtering, thereby suppressing interference. Traditional navigation beam nulling methods do not require knowledge of the signal waveform and have unconstrained power minimization, resulting in time-domain waveform distortion of the signal, making it impossible to accurately obtain propagation delay and guarantee high-precision navigation positioning.

[0004] Most of the subsequently proposed anti-interference methods are based on the constraints imposed on signal processing by array processing. They fail to utilize the information despread by the receiver, the ephemeris message, and the multi-peak superposition caused by space-time processing. Consequently, they are unable to obtain high-precision time delay information. The anti-interference indicator signal-to-interference-and-noise ratio (SIR) and the navigation signal carrier-to-noise ratio (CNR) are not fully compatible, resulting in these anti-interference methods tending to focus on anti-interference performance without considering navigation and positioning accuracy. The data received by the array always contains the correct navigation information. By despreading and tracking the navigation data and adding center tap constraints to the estimated post-correlation correlation matrix, the multi-peak superposition problem is resolved and high gain is achieved at the corresponding navigation signal code phase, thereby suppressing interference while improving positioning accuracy. Summary of the Invention

[0005] The present invention addresses the problems existing in the above-mentioned background technology and provides an improved GNSS adaptive processing error correction method. By combining space-time array adaptive processing with the despreading information of the navigation signal in the receiver, the post-processing correlation matrix and the correlation vector containing the delay information are estimated. Using a center constraint method, the correlation peak at the center of each space-time delay node is selected as the constraint for the desired signal, ensuring that each delay node of the desired signal is calibrated and summed around this center. Simultaneously, eigendecomposition of the sampling covariance matrix is ​​used to separate the interference subspace from the noise subspace. Using the subspace reconstruction projection matrix, the interference component of the correlation vector is projected into the noise subspace, reducing the impact of the interference correlation component, increasing the convergence speed of the adaptive weights, improving C / N0 performance, and achieving good code phase delay estimation performance. The weights generated by the space-time center tap constraint can make the phase detection curve after superposition with the center correlation peak as the reference more stable. Furthermore, the projection matrix obtained by eigendecomposing the covariance matrix solves the non-orthogonality between the inverse covariance matrix and the interference component of the correlation vector caused by the center tap constraint matrix, reducing the residual interference generated, further reducing the interference component, and increasing the convergence speed of the adaptive weights. Thus, the above-mentioned problems are solved.

[0006] The technical solutions of the present invention are as follows:

[0007] An improved GNSS adaptive processing error correction method, comprising:

[0008] Step S1: Initialize parameter settings;

[0009] Step S2: sampling to obtain array received data;

[0010] Step S3: Calculate the sampling covariance matrix based on the array received data;

[0011] Step S4: performing despreading correlation processing on the data containing navigation information in the array received data with a specific delay to obtain a signal correlation vector;

[0012] Step S5: Perform eigendecomposition on the sampled covariance matrix, arrange the eigenvalues ​​and their corresponding eigenvectors in descending order of eigenvalue according to the set eigenvalue threshold, take the maximum eigenvalue as the power estimate of all interference signals, and select the small eigenvalue as the power value of the noise;

[0013] Step S6: The eigenvector corresponding to the small eigenvalue is the noise subspace; then the noise projection matrix is ​​obtained according to the noise subspace, and the space-time reference tap extraction matrix is ​​constructed at the same time;

[0014] Step S7: Based on the noise projection matrix and the reference tap extraction matrix, the signal correlation vector in step S4 is first projected into the noise subspace and then the center reference tap is selected to obtain an estimated correlation vector extracted by projection;

[0015] Step S8: deriving the optimal weight vector of the adaptive beamforming method according to the estimated correlation vector and the post-correlation covariance matrix in step S4.

[0016] Furthermore, the step S1 includes:

[0017] The spatial range of the direction of arrival of the desired signal , the number of expected signals is 1, the number of interference signals , signal reference GPS L1 CA code, number of array elements , number of delays , number of array elements , number of delays , the spacing between array antennas , number of array data sampling points , power spectrum search interval .

[0018] Furthermore, the step S2 includes:

[0019] No. Digital output for each front-end channel Indicates that the filter The instantaneous signal snapshot on each tap is represented by:

[0020]

[0021] every time The snapshots of the filters are combined into a received signal snapshot vector:

[0022] .

[0023] Furthermore, the sampling covariance matrix in step S3 is as follows:

[0024]

[0025] in, Represents the conjugate transpose of a matrix.

[0026] Furthermore, the step S4 includes:

[0027] Step S41: Define as a vector The despread finite integral expectation is:

[0028]

[0029] The role of is to move the time delay reference of the correlation function. When the delay caused by the antenna is taken into account, the cross-correlation function here is equivalent to the cross-correlation between the signal processed by the antenna response and the same signal received by the isotropic receiver at the reference point. The deviation caused by the algorithm processing is generated relative to this reference point. The correlation vector estimate of the incident signal can be expressed as:

[0030]

[0031] in, , For the snapshot number, is the sampling period, 、 、 Represent the estimated values ​​of the correlation vectors of the desired signal, interference signal, and noise, respectively. is called the correlation vector; The vector is composed of each antenna element corresponding to 、 Related vector composition;

[0032]

[0033] Among them, The first vector The relevant quantities are:

[0034]

[0035] is an exact replica of the incident GNSS signal, the timing reference signal corresponding to the GNSS signal received by an isotropic antenna at the phase reference point.

[0036] Furthermore, the step S5 includes:

[0037] Step S51: Correlation matrix of samples Perform eigendecomposition to separate the interference subspace from the noise subspace, and use a simple threshold decision to set represents the feature value threshold defined by;

[0038]

[0039] Step S52: The projection matrix is ​​defined by the noise subspace, which is:

[0040] .

[0041] Furthermore, the step S6 includes:

[0042] Construct a center tap reference extraction matrix;

[0043]

[0044] wherein, is the center tap modification vector, which can be decomposed into the given corresponding M array element vectors:

[0045]

[0046] The sub-vectors are given by :

[0047]

[0048] In the case of , the delay bin is odd, corresponding to the center tap being set to 1, and all other taps offset from the center being set to zero, and the delay bin is even, the middle pair of taps are set to 1, and all other taps offset from the center are set to zero.

[0049] Further, the step S7 comprises:

[0050] Step S71: projecting the estimated correlation vector into the noise subspace; let denote the projected estimated correlation vector, which is given by:

[0051]

[0052] Step S72: constraining the vector :

[0053] .

[0054] Further, the optimal weight vector is obtained by the following formula:

[0055] .

[0056] Compared with the prior art, the present application has the beneficial effects that:

[0057] 1. An improved GNSS adaptive processing error correction method, based on the scheme of post-correlation adaptive anti-jamming, by changing the space-time weighting structure, and by a projection matrix to reduce the interference components in the correlation vector, to improve the correlation vector despread integration convergence speed, and to obtain stable code phase bias performance.

[0058] 2. An improved GNSS adaptive processing error correction method, based on an anti-interference scheme with despread correlation vector constraints, can effectively improve the time domain conformality of a single satellite signal, ensure good phase detection functions and correlation peaks in different signal scenarios, and improve the accuracy of satellite pseudorange acquisition while taking anti-interference into account, thereby enhancing positioning performance.

[0059] 3. An improved GNSS adaptive processing error correction method is proposed. It only defines the central delay node reference in the constraint vector. The adaptive weights have fewer constraints in the time domain / frequency domain, which can lead to a good code delay deviation response. At the same time, it reduces the influence of the weights on the variability of specific code delays, reduces the variability of the weights, and improves the convergence of the weights. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 Flowchart of an improved GNSS adaptive processing error correction method;

[0061] Figure 2 Schematic diagram of adaptive beamforming system;

[0062] Figure 3 The signal-to-noise ratio is -30dB, the interference-to-signal ratio is 50dB, the interference power is 20dBmW, the expected signal power value is -30dBmW, and the noise power value is 0dBmW. The expected signal comes from , the interference signal comes from , . Snapshot number , the correlation peak when the correlation vector integration time is 1ms;

[0063] Figure 4 This is the phase-locked S-curve diagram under the same scenario;

[0064] Figure 5 Figure 2 is the code delay error diagram for each direction in the same scenario. DETAILED DESCRIPTION

[0065] It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.

[0066] The features and performance of the present invention are further described in detail below with reference to the embodiments.

[0067] Example 1

[0068] An improved GNSS adaptive processing error correction method, based on eigendecomposition of the sampled covariance matrix, constructs a noise projection matrix, projects the correlation vector into the noise subspace, and reduces the interference component in the correlation vector. Simultaneously, the time-domain weighting structure is modified to reduce the impact of the superposition of multiple peaks in the GNSS signal correlation function, providing a high-precision interference suppression method. On the one hand, the weights generated by the space-time center tap constraint can make the phase detection curve after superposition with the center correlation peak as a reference more stable. On the other hand, the projection matrix obtained by eigendecomposing the covariance matrix solves the problem of non-orthogonality between the inverse covariance matrix and the interference component in the correlation vector caused by the center tap constraint matrix, reducing the residual interference generated, further reducing the interference component, and improving the convergence speed of the adaptive weights.

[0069] See also Figure 1 Specifically, an improved GNSS adaptive processing error correction method includes:

[0070] Step S1: Initialize parameter settings;

[0071] Step S2: sampling to obtain array received data;

[0072] Step S3: Calculate the sampling covariance matrix based on the array received data;

[0073] Step S4: performing despreading correlation processing on the data containing navigation information in the array received data with a specific delay to obtain a signal correlation vector;

[0074] Step S5: Perform eigendecomposition on the sampled covariance matrix, arrange the eigenvalues ​​and their corresponding eigenvectors in descending order of eigenvalue according to the set eigenvalue threshold, take the maximum eigenvalue as the power estimate of all interference signals, and select the small eigenvalue as the power value of the noise;

[0075] Step S6: The eigenvector corresponding to the small eigenvalue is the noise subspace; then the noise projection matrix is ​​obtained according to the noise subspace, and the space-time reference tap extraction matrix is ​​constructed at the same time;

[0076] Step S7: Based on the noise projection matrix and the reference tap extraction matrix, the signal correlation vector in step S4 is first projected into the noise subspace and then the center reference tap is selected to obtain an estimated correlation vector extracted by projection;

[0077] Step S8: deriving the optimal weight vector of the adaptive beamforming method according to the estimated correlation vector and the post-correlation covariance matrix in step S4.

[0078] In this embodiment, specifically, step S1 includes:

[0079] The spatial range of the direction of arrival of the desired signal , the number of expected signals is 1, the number of interference signals , signal reference GPS L1 CA code, number of array elements , number of delays , number of array elements , number of delays , the spacing between array antennas , number of array data sampling points , power spectrum search interval .

[0080] In this embodiment, specifically, step S2 includes:

[0081] No. Digital output for each front-end channel Indicates that the filter The instantaneous signal snapshot on each tap is represented by:

[0082]

[0083] every time The snapshots of the filters are combined into a received signal snapshot vector:

[0084] .

[0085] In this embodiment, specifically, the sampling covariance matrix in step S3 is as follows:

[0086]

[0087] in, Represents the conjugate transpose of a matrix.

[0088] In this embodiment, specifically, step S4 includes:

[0089] Step S41: Define as a vector The despread finite integral expectation is:

[0090]

[0091] The role of is to move the time delay reference of the correlation function. When the delay caused by the antenna is taken into account, the cross-correlation function here is equivalent to the cross-correlation between the signal processed by the antenna response and the same signal received by the isotropic receiver at the reference point. The deviation caused by the algorithm processing is generated relative to this reference point. The correlation vector estimate of the incident signal can be expressed as:

[0092]

[0093] in, , For the snapshot number, is the sampling period, 、 、 Represent the estimated values ​​of the correlation vectors of the desired signal, interference signal, and noise, respectively. is called the correlation vector; The vector is composed of each antenna element corresponding to 、 Related vector composition;

[0094]

[0095] Among them, The first vector The relevant quantities are:

[0096]

[0097] is an exact replica of the incident GNSS signal, the timing reference signal corresponding to the GNSS signal received by an isotropic antenna at the phase reference point.

[0098] In this embodiment, specifically, step S5 includes:

[0099] Step S51: Correlation matrix of samples Perform eigendecomposition to separate the interference subspace from the noise subspace, and use a simple threshold decision to set represents the feature value threshold defined by;

[0100]

[0101] Step S52: The projection matrix is ​​defined by the noise subspace, which is:

[0102] .

[0103] In this embodiment, specifically, step S6 includes:

[0104] Construct a center tap reference extraction matrix;

[0105]

[0106] in, is the center tap modification vector, which can be decomposed into the corresponding M array element vectors given:

[0107]

[0108] The subvector is given by :

[0109]

[0110] exist In the delay section, the odd number of delay nodes corresponds to the center tap being set to 1 and all other off-center taps being set to zero. The even number of delay nodes corresponds to the center pair of taps being set to 1 and all other off-center taps being set to zero.

[0111] In this embodiment, specifically, step S7 includes:

[0112] Step S71: Estimated correlation vector Projected into the noise subspace; let Denotes the estimated correlation vector of the projection, given by:

[0113]

[0114] Step S72: Constraint vector :

[0115] .

[0116] In this embodiment, specifically, the optimal weight vector is obtained by the following formula:

[0117] .

[0118] Example 2

[0119] Example 2 is an example of Example 1, please refer to Figure 1-5 .

[0120] In this example, the number of array elements is , Delay Section , the number of sampling points is , the expected number of signals is 1, and the expected signal direction is , the expected signal direction range is , the number of interference signals is , the interference signal comes from , .like Figure 1 As shown, the present invention provides a GNSS space-time anti-interference error correction method, comprising the steps of:

[0121] Step 1: Set initialization parameters;

[0122] The spatial range of the direction of arrival of the desired signal , the number of expected signals is 1, and the number of interference signals is , the signal reference carrier frequency is The number of array elements is , number of delay nodes , the spacing between array antennas , number of sampling points , the despreading integration accumulation time is 1ms;

[0123] Step 2: Sample the data received by the array and get , take 5 delay nodes, , ;

[0124] Step 3: Calculate the sampling covariance matrix ;

[0125] Step 4: Generate the spread spectrum code corresponding to the GNSS signal locally, and correlate it with the delay tap data according to the specific code phase delay , integrate and accumulate for 1ms, and take the average value to get the correlation vector;

[0126] Step 5: Perform eigendecomposition on the sampling covariance matrix ,in represents the eigenvalues ​​in descending order, Represents the eigenvector of the corresponding eigenvalue; taking the minimum eigenvalue is As an estimate of the noise power: ; Take the eigenvectors corresponding to the 7 small eigenvalues ​​to obtain the noise subspace: , calculate the noise projection matrix ;

[0127] Step 6: Construct the center tap reference extraction matrix;

[0128]

[0129] in is the center tap modification vector, which can be decomposed into the corresponding M array element vectors given;

[0130]

[0131] The subvector is composed of ;

[0132]

[0133] exist In the example, the delay nodes are odd, corresponding to the center tap being set to 1 and all other off-center taps being set to zero; the delay nodes are even, corresponding to the center pair of taps being set to 1 and all other off-center taps being set to zero;

[0134] Step 7: Calculate the constraint vector ;

[0135]

[0136] Step 8: Use the constraint vector in step 8 to obtain the adaptive weights, and finally get:

[0137] .

[0138] The above-described embodiments merely represent specific implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of protection of the present application. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the technical concept of the present application, and all such variations and improvements fall within the scope of protection of the present application.

[0139] This background section is provided to generally present the context of the invention, and the work of the presently named inventors, the work to the extent described in this background section, and aspects of the description in this section that did not constitute prior art at the time of filing are neither explicitly nor implicitly admitted to be prior art to the present invention.

Claims

1. An improved GNSS adaptive processing error correction method, characterized in that: include: Step S1: Initialize parameter settings; Step S2: sampling to obtain array received data; Step S3: Calculate the sampling covariance matrix based on the array received data; Step S4: performing despreading correlation processing on the data containing navigation information in the array received data with a specific delay to obtain a signal correlation vector; Step S5: Perform eigendecomposition on the sampled covariance matrix, arrange the eigenvalues ​​and their corresponding eigenvectors in descending order of eigenvalue according to the set eigenvalue threshold, take the maximum eigenvalue as the power estimate of all interference signals, and select the small eigenvalue as the power value of the noise; Step S6: The eigenvector corresponding to the small eigenvalue is the noise subspace; Then, the noise projection matrix is ​​obtained according to the noise subspace, and the space-time reference tap extraction matrix is ​​constructed at the same time; Step S7: Based on the noise projection matrix and the reference tap extraction matrix, the signal correlation vector in step S4 is first projected into the noise subspace and then the center reference tap is selected to obtain an estimated correlation vector extracted by projection; Step S8: deriving the optimal weight vector of the adaptive beamforming method according to the estimated correlation vector.

2. The improved GNSS adaptive processing error correction method according to claim 1, characterized in that: The step S1 comprises: The spatial range of the direction of arrival of the desired signal , the number of expected signals is 1, the number of interference signals , signal reference GPSL1 CA code, number of array elements , number of delays , number of array elements , number of delays , the spacing between array antennas , number of array data sampling points , power spectrum search interval .

3. The improved GNSS adaptive processing error correction method according to claim 2, characterized in that: The step S2 includes: No. Digital output for each front-end channel Indicates that the filter The instantaneous signal snapshot on each tap is represented by: every time The snapshots of the filters are combined into a received signal snapshot vector: 。 4. The improved GNSS adaptive processing error correction method according to claim 3, characterized in that: The sampling covariance matrix in step S3 is as follows: in, Represents the conjugate transpose of a matrix.

5. The improved GNSS adaptive processing error correction method according to claim 4, characterized in that: The step S4 comprises: Step S41: Define as a vector The despread finite integral expectation is: The role of is to move the time delay reference of the correlation function. When the delay caused by the antenna is taken into account, the cross-correlation function here is equivalent to the cross-correlation between the signal processed by the antenna response and the same signal received by the isotropic receiver at the reference point. The deviation caused by the algorithm processing is generated relative to this reference point. The correlation vector estimate of the incident signal can be expressed as: in, , For the snapshot number, is the sampling period, 、 、 Represent the estimated values ​​of the correlation vectors of the desired signal, interference signal, and noise, respectively. is called the correlation vector; The vector is composed of each antenna element corresponding to 、 Related vector composition; Among them, The first vector The relevant quantities are: is an exact replica of the incident GNSS signal, the timing reference signal corresponding to the GNSS signal received by an isotropic antenna at the phase reference point.

6. The improved GNSS adaptive processing error correction method according to claim 5, characterized in that: The step S5 comprises: Step S51: Correlation matrix of samples Perform eigendecomposition to separate the interference subspace from the noise subspace, and use a simple threshold decision to set represents the feature value threshold defined by; Step S52: The projection matrix is ​​defined by the noise subspace, which is: 。 7. The improved GNSS adaptive processing error correction method according to claim 6, characterized in that: The step S6 comprises: Construct a center tap reference extraction matrix; in, is the center tap modification vector, which can be decomposed into the corresponding M array element vectors given: The subvector is given by : exist In the delay section, the odd number of delay nodes corresponds to the center tap being set to 1 and all other off-center taps being set to zero. The even number of delay nodes corresponds to the center pair of taps being set to 1 and all other off-center taps being set to zero.

8. The improved GNSS adaptive processing error correction method according to claim 7, characterized in that: The step S7 includes: Step S71: Estimated correlation vector Projected into the noise subspace; let Denotes the estimated correlation vector of the projection, given by: Step S72: Constraint vector : 。 9. The improved GNSS adaptive processing error correction method according to claim 8, characterized in that: The formula for obtaining the optimal weight vector is as follows: 。

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