Multi-antenna array-based improved GNSS Anti-jamming method using optimal weight coefficient

Abstract

A multi-antenna array-based improved GNSS anti-jamming method using an optimal weight coefficient, comprising the following steps: constructing a signal model for collecting observation signals x by a GNSS antenna array consisting of N array elements; on the basis of the signal model, acquiring a covariance matrix of signals received by the array; performing eigenvalue decomposition on the covariance matrix of the signals received by the array to obtain a matrix composed of eigenvectors; respectively constructing a jamming subspace and a signal-plus-noise subspace; on the basis of a linearly constrained minimum variance beamforming algorithm, designing a subspace constraint-based improved algorithm using an optimal weight coefficient; solving for the optimal weight coefficient on the basis of the linearly constrained minimum variance beamforming algorithm; and using the optimal weight coefficient to weigh the received signals to realize GNSS jamming suppression. According to the improved algorithm, prior information of direction angles of signals is not required, gains of real signals are protected while jamming is suppressed, and the problem of potential safety hazards caused by low-cost jammers is effectively alleviated.

Description

Improved GNSS anti-interference method based on optimal weight coefficient of multi-antenna array Technical Field

[0001] The present application relates to the field of GNSS anti-interference technology, and in particular to a GNSS anti-interference method improved by optimizing weight coefficients based on a multi-antenna array. Background Art

[0002] The Global Navigation Satellite System (GNSS) provides 24 / 7 space information services to billions of users worldwide, becoming a critical infrastructure upon which nations rely. However, in recent years, the inherent vulnerability of GNSS, the complex electromagnetic environment, and interference have posed challenges to the vast number of user terminals. Interference typically involves the use of jammers to transmit interfering signals, blurring the operating spectrum of satellite navigation signals and degrading or even completely disrupting the normal operation of user receivers. Therefore, research on GNSS anti-interference technology has significant research significance and application value.

[0003] Currently, the main anti-interference methods include satellite-based anti-interference, receiver-based anti-interference, and assisted anti-interference. Satellite-based anti-interference technology primarily involves optimizing satellites, changing their orbits, increasing the signal transmission power of satellite payloads, enhancing the service frequency of satellite signals, and even optimizing the signal system design. Assisted anti-interference technology uses other means to assist in positioning and navigation. Common auxiliary technologies include integrated navigation and radar, and are a major research focus in positioning, navigation, and timing systems. Receiver-based anti-interference technology detects and suppresses interference from antennas, intermediate frequency signal processing, and digital signal processing, and is currently a major research focus in GNSS anti-interference. Spatial filtering (also known as beamforming) can spatially distinguish interference from useful signals. Using array antennas, it can handle both wideband interference and multiple narrowband interferences, making it widely used in GNSS anti-interference. For example, GNSS anti-interference technology based on the power inversion algorithm requires no prior information, is simple to implement, and is highly effective. However, the array gain of the GNSS signal cannot be guaranteed. To address the problem of satellite signal array gain not being guaranteed, the minimum variance distortion response algorithm can be used for GNSS anti-interference. However, the disadvantage is that the direction and elevation angle of each GNSS signal must be known in advance.

[0004] Summary of the Invention

[0005] This application provides an improved GNSS anti-interference method based on the optimal weight coefficient of a multi-antenna array to alleviate the safety risks caused by low-cost jammers.

[0006] This application provides a method for improving GNSS anti-interference based on optimal weight coefficients of a multi-antenna array, comprising the following steps:

[0007] Construct a signal model for collecting observation signal x using a GNSS array antenna with N elements;

[0008] According to the signal model, obtaining a covariance matrix of the array received signal;

[0009] Solving the optimal weight vector for the covariance matrix based on a preset algorithm of linear constrained minimum variance beamforming to obtain an optimal weight coefficient;

[0010] The received signal is weighted using the optimal weight coefficient to achieve GNSS interference suppression.

[0011] In a possible implementation, solving the optimal weight vector for the covariance matrix based on a preset algorithm of linear constrained minimum variance beamforming to obtain the optimal weight coefficient includes:

[0012] Performing eigenvalue decomposition on the covariance matrix of the array received signal to obtain a matrix composed of eigenvectors;

[0013] Construct interference subspace and signal plus noise subspace respectively;

[0014] Based on the linear constrained minimum variance beamforming algorithm, an improved algorithm for optimal weight coefficients based on subspace constraints is designed.

[0015] The optimal weight coefficients are solved according to the linear constrained minimum variance beamforming algorithm.

[0016] In a possible implementation, in the process of obtaining a matrix composed of eigenvectors: the covariance matrix Rxx of the array received signal is subjected to eigenvalue decomposition to obtain a matrix composed of N eigenvectors V = [φ1, φ2, ...φ N ], where φ is the eigenvector of the covariance matrix Rxx.

[0017] In a possible implementation, during the construction of the interference subspace and the signal plus noise subspace: assuming that Rxx has M large eigenvalues, then the subspace V corresponding to the interference signal I It can be expressed as V I =[φ1,φ2,…φ M ], signal plus noise subspace V S+N It can be expressed as V S+N =[φ M+1 ,φ M+2 ,…φ N ].

[0018] In one possible implementation, based on the linearly constrained minimum variance beamforming algorithm, the constraint criterion in the improved algorithm for designing the optimal weight coefficient based on the subspace constraint can be expressed as:

[0019] Where w represents the optimal weight coefficient, f I = [0,0,…0] is a 1×M-dimensional constraint response vector to ensure interference suppression of the interference subspace constraint, f S+N Each element of can be 0, 1 or -1, which is a 1×(NM) dimensional constraint response vector to ensure signal and noise reception; where f S+N The number of 1s and -1s in the subspace should be kept equal to further cancel out the residual interference in the signal plus noise subspace.

[0020] In a possible implementation, formula (1) is converted and the constraint condition is formula (2): w H [V I ,V S+N ]=[f I ,f S+N ] (2)

[0021] Let V = [V I ,V S+N ], formula (2) can be written as shown in the following formula (3): H V=[f I ,f S+N ] (3).

[0022] In a possible implementation, the optimal weight coefficient obtained by solving is shown in the following formula (4):

[0023] where f=[f I ,f S+N ].

[0024] In a possible implementation, the received signal is weighted using the optimal weight vector as shown in formula (5):

[0025] y=w H x(5), where y is the output signal after weighted post-processing.

[0026] As can be seen from the above technical solutions, the present application provides an improved GNSS anti-interference method based on the optimal weight coefficient of a multi-antenna array, comprising the following steps: constructing a signal model of an observation signal x collected by a GNSS array antenna with N array elements; obtaining the covariance matrix of the array received signal based on the signal model; performing eigenvalue decomposition on the covariance matrix of the array received signal to obtain a matrix composed of eigenvectors; constructing an interference subspace and a signal plus noise subspace respectively; designing an improved algorithm based on the optimal weight coefficient of the subspace constraint on the basis of the linear constrained minimum variance beamforming algorithm; solving the optimal weight coefficient according to the linear constrained minimum variance beamforming algorithm; and using the optimal weight coefficient to weight the received signal to achieve GNSS interference suppression. The improved algorithm provided in the present application does not require prior information on the signal direction angle, and can protect the gain of the real signal while suppressing interference, effectively alleviating the safety hazards caused by low-cost jammers. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solution of the present application, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0028] FIG1 is a flow chart of improving GNSS anti-interference based on optimal weight coefficients of a multi-antenna array provided by an embodiment of the present application. DETAILED DESCRIPTION

[0029] The following embodiments are described in detail, with examples illustrated in the accompanying drawings. When the following description refers to the drawings, identical numbers in different figures represent identical or similar elements unless otherwise indicated. The embodiments described in the following embodiments are not intended to represent all possible implementations consistent with the present application. They are merely examples of systems and methods consistent with certain aspects of the present application, as detailed in the claims.

[0030] The Global Navigation Satellite System (GNSS) provides 24 / 7 space information services to billions of users worldwide, becoming a critical infrastructure upon which nations rely. However, in recent years, the inherent vulnerability of GNSS, the complex electromagnetic environment, and interference have posed challenges to the vast number of user terminals. Interference typically involves the use of jammers to transmit interfering signals, blurring the operating spectrum of satellite navigation signals and degrading or even completely disrupting the normal operation of user receivers. Therefore, research on GNSS anti-interference technology has significant research significance and application value.

[0031] Currently, the main anti-interference methods include satellite-based anti-interference, receiver-based anti-interference, and assisted anti-interference. Satellite-based anti-interference technology primarily involves optimizing satellites, changing their orbits, increasing the signal transmission power of satellite payloads, enhancing the service frequency of satellite signals, and even optimizing the signal system design. Assisted anti-interference technology uses other means to assist in positioning and navigation. Common auxiliary technologies include integrated navigation and radar, and is a major research focus in positioning, navigation, and timing systems. Receiver-based anti-interference technology detects and suppresses interference from antennas, intermediate frequency signal processing, and digital signal processing, and is currently a major research focus in GNSS anti-interference. Spatial filtering (also known as beamforming) can spatially distinguish interference from useful signals. Using array antennas, it can handle both wideband interference and multiple narrowband interferences, making it widely used in GNSS anti-interference. For example, GNSS anti-interference technology based on the power inversion algorithm requires no prior knowledge, is simple to implement, and is highly effective. However, it cannot guarantee the array gain of the GNSS signal. To address the problem of satellite signal array gain not being guaranteed, the minimum variance distortion response algorithm can be used for GNSS anti-interference. However, the disadvantage is that the direction and elevation angle of each GNSS signal must be known in advance.

[0032] Therefore, in order to address the security risks brought by low-cost jammers, the present invention proposes an improved algorithm for optimal weight coefficients based on a multi-antenna array. The improved algorithm does not require prior information on the signal direction angle and can protect the gain of the real signal while suppressing interference.

[0033] This application provides a method for improving GNSS anti-interference based on optimal weight coefficients of a multi-antenna array, comprising the following steps:

[0034] Construct a signal model for collecting observation signal x using a GNSS array antenna with N elements;

[0035] According to the signal model, obtaining a covariance matrix of the array received signal;

[0036] Solving the optimal weight vector for the covariance matrix based on a preset algorithm of linear constrained minimum variance beamforming to obtain an optimal weight coefficient;

[0037] The received signal is weighted using the optimal weight coefficient to achieve GNSS interference suppression.

[0038] In a possible implementation, solving the optimal weight vector for the covariance matrix based on a preset algorithm of linear constrained minimum variance beamforming to obtain the optimal weight coefficient includes:

[0039] Performing eigenvalue decomposition on the covariance matrix of the array received signal to obtain a matrix composed of eigenvectors;

[0040] Construct interference subspace and signal plus noise subspace respectively;

[0041] Based on the linear constrained minimum variance beamforming algorithm, an improved algorithm for optimal weight coefficients based on subspace constraints is designed.

[0042] The optimal weight coefficients are solved according to the linear constrained minimum variance beamforming algorithm.

[0043] In a possible implementation, in the process of obtaining a matrix composed of eigenvectors: the covariance matrix Rxx of the array received signal is subjected to eigenvalue decomposition to obtain a matrix composed of N eigenvectors V = [φ1, φ2, ...φ N ], where φ is the eigenvector of the covariance matrix Rxx.

[0044] In a possible implementation, during the construction of the interference subspace and the signal plus noise subspace: assuming that Rxx has M large eigenvalues, then the subspace V corresponding to the interference signal I It can be expressed as V I =[φ1,φ2,…φ M ], signal plus noise subspace V S+N It can be expressed as V S+N =[φ M+1 ,φ M+2 ,…φ N ].

[0045] In a possible implementation, based on the linearly constrained minimum variance beamforming algorithm, the constraint criterion in the improved algorithm for designing the optimal weight coefficient based on the subspace constraint can be expressed as:

[0046] Where w represents the optimal weight coefficient, f I = [0,0,…0] is a 1×M-dimensional constraint response vector to ensure interference suppression of the interference subspace constraint, f S+N Each element of can be 0, 1 or -1, which is a 1×(NM) dimensional constraint response vector to ensure signal and noise reception; where f S+N The number of 1s and -1s in the subspace should be kept equal to further cancel out the residual interference in the signal plus noise subspace.

[0047] In a possible implementation, formula (1) is converted and the constraint condition is formula (2): w H [V I ,V S+N ]=[f I ,f S+N ] (2)

[0048] Let V = [V I ,VS+N ], formula (2) can be written as shown in the following formula (3): H V=[f I ,f S+N ] (3).

[0049] In a possible implementation, the optimal weight coefficient obtained by solving is shown in the following formula (4):

[0050] where f=[f I ,f S+N ].

[0051] In a possible implementation, the received signal is weighted using the optimal weight vector as shown in formula (5):

[0052] y=w H x(5), where y is the output signal after weighted post-processing.

[0053] Example

[0054] This embodiment is described with reference to FIG1 . In this embodiment, a method for improving GNSS anti-interference based on optimal weight coefficients of a multi-antenna array includes the following steps:

[0055] Step 1: Considering the noise as Gaussian white noise, construct a GNSS array antenna with N elements to collect the observation signal x.

[0056] Step 2: Calculate the autocorrelation matrix R of the array signal xx .

[0057] Step 3: Design beamforming constraints using the eigenvectors. Step 3 includes the following steps:

[0058] Step 3.1: The covariance matrix R of the array received signal xx Perform eigenvalue decomposition to obtain a matrix V consisting of N eigenvectors = [φ1,φ2,…φ N ], where φ is the covariance matrix R xx The eigenvector of

[0059] Step 3.2: Assume R xx There are M larger eigenvalues, then the subspace V corresponding to the interference signal I It can be expressed as V I =[φ1,φ2,…φ M ], signal plus noise subspace V S+N It can be expressed as V S+N =[φ M+1 ,φ M+2 ,…φ N ]

[0060] Step 3.3: Based on the linearly constrained minimum variance beamforming algorithm, an improved algorithm for optimal weight coefficients based on subspace constraints is designed. The constraint criterion proposed in this invention can be expressed as:

[0061] Where w represents the optimal weight coefficient, f I = [0,0,…0] is a 1×M-dimensional constraint response vector to ensure interference suppression of the interference subspace constraint. S+N Each element of can be 0, 1 or -1, but not all 0. This is a 1×(NM) dimensional constraint response vector, and a noise subspace constraint is added to the signal to ensure signal and noise reception. S+N The number of 1s and -1s in the subspace should be kept as equal as possible to further cancel out the residual interference in the signal plus noise subspace.

[0062] The constraint condition of formula (1) can be written as: H [V I ,V S+N ]=[f I ,f S+N ] (2)

[0063] Let V = [V I ,V S+N ], formula (2) can be written as w H V=[f I ,f S+N ] (3)

[0064] Step 3.4: According to the solution principle of the linear constrained minimum variance beamforming algorithm, the optimal weight coefficient w of the present invention can be expressed as:

[0065] where f=[f I ,f S+N ].

[0066] Step 4: Use the optimal weight coefficient to weight the received signal x to suppress the interference signal: y = w H x, where y is the output signal after weighted post-processing.

[0067] As can be seen from the above embodiments, the present application provides an improved GNSS anti-interference method based on the optimal weight coefficient of a multi-antenna array, comprising the following steps: constructing a signal model of an observation signal x collected by a GNSS array antenna with N array elements; obtaining the covariance matrix of the array received signal according to the signal model; performing eigenvalue decomposition on the covariance matrix of the array received signal to obtain a matrix composed of eigenvectors; constructing an interference subspace and a signal plus noise subspace respectively; designing an improved algorithm based on the optimal weight coefficient of the subspace constraint on the basis of the linear constrained minimum variance beamforming algorithm; solving the optimal weight coefficient according to the linear constrained minimum variance beamforming algorithm; and weighting the received signal using the optimal weight coefficient to achieve GNSS interference suppression. The improved algorithm provided in the present application does not require prior information on the signal direction angle, and can protect the gain of the real signal while suppressing interference, effectively alleviating the safety hazards caused by low-cost jammers.

[0068] Similar parts between the embodiments provided in this application can be referenced to each other. The specific implementation methods provided above are only a few examples under the overall concept of this application and do not constitute a limitation on the scope of protection of this application. For those skilled in the art, any other implementation methods expanded based on the scheme of this application without expending creative work shall fall within the scope of protection of this application.

Claims

1. A GNSS anti-interference method based on optimal weight coefficients of a multi-antenna array, characterized in that: The following steps are involved: Construct a signal model for collecting observation signals x by a GNSS array antenna with N array elements; According to the signal model, obtaining a covariance matrix of the array received signal; Solving the optimal weight vector for the covariance matrix based on a preset algorithm of linear constrained minimum variance beamforming to obtain an optimal weight coefficient; The received signal is weighted using the optimal weight coefficient to achieve GNSS interference suppression.

2. The method for improving GNSS anti-interference based on optimal weight coefficients of a multi-antenna array according to claim 1, characterized in that: Solving the optimal weight vector for the covariance matrix based on a preset algorithm of linear constrained minimum variance beamforming to obtain the optimal weight coefficient includes: Performing eigenvalue decomposition on the covariance matrix of the array received signal to obtain a matrix composed of eigenvectors; Construct interference subspace and signal plus noise subspace respectively; Based on the linear constrained minimum variance beamforming algorithm, an improved algorithm for optimal weight coefficients based on subspace constraints is designed; The optimal weight coefficients are solved according to the linear constrained minimum variance beamforming algorithm.

3. The method for improving GNSS anti-interference based on optimal weight coefficients of a multi-antenna array according to claim 2, characterized in that: In the process of obtaining the matrix composed of eigenvectors: the covariance matrix Rxx of the array receiving signal is subjected to eigenvalue decomposition to obtain a matrix V=[φ1,φ2,…φ N ], where φ is the eigenvector of the covariance matrix Rxx.

4. The method for improving GNSS anti-interference based on optimal weight coefficients of a multi-antenna array according to claim 2, characterized in that: In the process of constructing the interference subspace and the signal plus noise subspace: Assuming that Rxx has M large eigenvalues, the subspace V corresponding to the interference signal I It can be expressed as V I =[φ1,φ2,…φ M ], signal plus noise subspace V S+N It can be expressed as V S+N =[φ M+1 ,φ M+2 ,…φ N ].

5. The method for improving GNSS anti-interference based on optimal weight coefficients of a multi-antenna array according to claim 2, characterized in that: Based on the linear constrained minimum variance beamforming algorithm, the constraint criterion in the improved algorithm for designing the optimal weight coefficient based on subspace constraints can be expressed as: Where w represents the optimal weight coefficient, f I = [0,0,…0] is a 1×M-dimensional constraint response vector to ensure interference suppression of the interference subspace constraint, f S+N Each element of can be 0, 1 or -1, which is a 1×(NM)-dimensional constrained response vector to ensure signal and noise reception; where f S+N The number of 1s and -1s in the signal-plus-noise subspace should be kept equal to further cancel out the residual interference in the signal-plus-noise subspace.

6. The method for improving GNSS anti-interference based on optimal weight coefficients of a multi-antenna array according to claim 5, characterized in that: Convert formula (1) to the constraint condition (2): w H [V I ,V S+N ]=[f I ,f S+N ] (2) Let V = [V I ,V S+N ], formula (2) can be written as shown in the following formula (3): w H V=[f I ,f S+N ] (3)。 7. The method for improving GNSS anti-interference based on optimal weight coefficients of a multi-antenna array according to claim 2, characterized in that: The optimal weight coefficient obtained is shown in the following formula (4): where f = [f I ,f S+N ].

8. The method for improving GNSS anti-interference based on optimal weight coefficients of a multi-antenna array according to claim 1, characterized in that: The received signal is weighted using the optimal weight vector as shown in formula (5): y=w H x(5), where y is the output signal after weighted processing.

Images