Miniaturized high-performance satellite navigation intelligent anti-interference method

Abstract

The invention discloses a miniaturized high-performance satellite navigation intelligent anti-interference method, which relates to the technical field of satellite navigation and artificial intelligence, realizes anti-interference by combining compressed sensing MUSIC super-resolution DOA estimation and manifold optimization, and realizes super-resolution DOA estimation under an unknown steering vector through compressed sensing MUSIC. And an optimal beam forming weight is solved on a complex unit spherical surface in combination with manifold optimization, and finally efficient interference resistance is realized in the Beidou B1 and GPS L1 frequency bands. Balance of miniaturization, low power consumption and high performance is achieved through algorithm innovation, array processing Rayleigh limit constraint is broken through from the perspective of an anti-interference signal processing algorithm, and low-power-consumption and low-resource lightweight deployment can be achieved while the anti-interference performance is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of satellite navigation and artificial intelligence technology, and in particular to a miniaturized high-performance satellite navigation intelligent anti-interference method. Background Art

[0002] In satellite navigation systems, receivers are susceptible to co-channel or adjacent-channel interference, which can lead to reduced positioning accuracy or even failure. Traditional anti-interference technologies (such as spatial filtering and frequency filtering) have the following problems:

[0003] 1. Insufficient array freedom: The number of miniaturized antenna array elements is limited (such as 2×2 four elements), and traditional algorithms have difficulty distinguishing dense interference signals.

[0004] 2. High computational complexity: Super-resolution algorithms (such as MUSIC) require high hardware resources and are difficult to implement on low-power platforms.

[0005] 3. Limited anti-interference performance: Traditional beamforming methods (such as MVDR) have insufficient null depth when the number of array elements is small, resulting in poor interference suppression effect. Summary of the Invention

[0006] In order to overcome the above-mentioned defects in the prior art, the present invention provides a miniaturized high-performance satellite navigation intelligent anti-interference method to achieve a balance between miniaturization, low power consumption and high performance.

[0007] To achieve the above object, the present invention adopts the following technical solutions, including:

[0008] A miniaturized high-performance satellite navigation intelligent anti-interference method comprises the following steps:

[0009] S1. Acquire array signal X, including BeiDou B1 signal and GPS L1 signal;

[0010] S2. Estimate and decompose the covariance matrix R of the acquired array signal X. The subsequent super-resolution direction of arrival (DOA) estimation provides the signal subspace and noise subspace.

[0011] S3. Perform super-resolution DOA estimation to separate the azimuth angle θ and elevation angle φ of BeiDou B1 signal, GPS L1 signal, and interference, providing spatial information for subsequent manifold optimization beamforming.

[0012] S4. Perform angle clustering and screening to remove redundancy, merge, and classify the potential signal directions obtained from sparse reconstruction, eliminate false peaks, and accurately mark the true directions of Beidou B1 signals, GPS L1 signals, and interference signals, providing angle input for manifold optimization beamforming.

[0013] S5, beamforming based on manifold optimization, solving the optimal beamforming weight w on the complex unit sphere manifold * , under the premise of ensuring the gain of Beidou B1 signal and GPS L1 signal, the interference direction is suppressed to the maximum extent, and an anti-interference navigation signal is finally output.

[0014] Preferably, step S1 is specifically as follows:

[0015] receiving an original time domain signal, wherein the original time domain signal includes noise, interference, and a target signal;

[0016] The original time domain signal is preprocessed by down-conversion, filtering and normalization to retain the effective components of the Beidou B1 and GPS L1 frequency bands.

[0017] Preferably, step S2 is specifically as follows:

[0018] S21. Estimate the covariance matrix R of the array signal X:

[0019]

[0020] Among them, the diagonal elements R in the covariance matrix R ii Represents the signal power of the i-th array element; the off-diagonal element R ij It represents the complex correlation between array elements i and j, reflecting the spatial phase difference of the signal;

[0021] S22, perform eigendecomposition on the covariance matrix R to achieve subspace separation:

[0022] R=U∑U H

[0023] Where U represents the eigenvector matrix, the column vectors are the eigendirections; Σ represents the diagonal matrix, the elements are the eigenvalues, and are arranged in descending order;

[0024] The eigenvectors U corresponding to the first K large eigenvalues ​​in the eigenvector matrix U s It represents the signal direction, that is, the signal subspace; the remaining eigenvectors in the eigenvector matrix U represent the orthogonal complementary space of noise and interference, that is, the noise subspace.

[0025] Preferably, step S3 is specifically as follows:

[0026] S31, noise subspace U obtained by decomposing the covariance matrix R n Construct an overcomplete dictionary. The grid division of the overcomplete dictionary is constructed according to the following rules:

[0027] Azimuth angle θ∈[0°,360°), the grid division step is 1°;

[0028] The pitch angle φ∈[0°,90°], the grid division step is 1°;

[0029] Generate an overcomplete dictionary Φ;

[0030] S32. Construct optimization problem:

[0031]

[0032] Where s represents the sparse angle vector; st represents the constraint condition; || ||1 represents the 1-norm; || ||2 represents the 2-norm; ∈ is the threshold; Indicates U n The conjugate transpose of , the superscript H represents the conjugate transpose of the matrix;

[0033] S33. Solve the optimization problem of step S32 using an iterative hard threshold method to obtain a sparse angle vector s. The iterative formula is as follows:

[0034] s k+1 =H K (s k +αΦ H (U n -Φs k ))

[0035] Among them, H K A hard threshold operation to retain the top K largest elements; k+1 、s k represents the sparse angle vector of the k+1, kth iteration; α represents the coefficient.

[0036] Preferably, step S4 is specifically as follows:

[0037] S41. Extract the azimuth angle θ and pitch angle φ corresponding to the non-zero position whose amplitude is significantly higher than the noise threshold in the sparse angle vector s:

[0038] Points={(θ1,φ1),(θ2,φ2),…,(θ N ,φ N )}

[0039] S42, normalize the azimuth angle θ and the pitch angle φ:

[0040]

[0041] Where Δθ max =360°, Δφ max =90°;

[0042] S43. Traverse each point p and find all points N in its ε neighborhood ∈ (p):

[0043] N ∈ (p)={q∈Points∣d(p,q)≤∈}

[0044] If |N ∈ (p)|≥MinPts, MinPts is the set minimum number of points, then a new cluster C is created and p is added to cluster C; otherwise, no new cluster is created;

[0045] N ∈ For each point q in (p), if q has not been visited, recursively expand its neighborhood and add q to the current cluster C; otherwise, do not add it to the current cluster;

[0046] Mark the points that cannot be included in any cluster as noise and remove them;

[0047] S44. Cluster the scattered grid points into several clusters, each cluster represents a real signal source, and for each cluster C k , take its mean as the final direction estimate:

[0048]

[0049] The directions of Beidou B1 signals, GPS L1 signals, and interference signals are distinguished based on the signal frequency band. If the signal direction matches a known satellite orbit or frequency band, it is marked as a Beidou / GPS signal; otherwise, it is marked as an interference signal and needs to be suppressed.

[0050] Preferably, step S5 is specifically as follows:

[0051] S51. The objective function of constructing manifold optimization is to minimize interference and noise power, that is, Among them, R i+n is the interference plus noise covariance matrix;

[0052] The manifold constraint is that the weights lie on the complex unit sphere w represents weight; represents the complex unit sphere;

[0053] S52, initialize w0=C(C H C) -1 1. Projection onto the complex unit sphere;

[0054] S53, perform gradient calculation Represents gradient calculation;

[0055] S54. Perform tangent space projection and remove the normal component to maintain the manifold constraint: Project w (·) represents the tangent space projection; Re(·) represents the real part of the complex number;

[0056] S55, iterate along the negative gradient direction and renormalize to achieve weight update:

[0057]

[0058] Among them, w k+1 、w k represents the weight of the k+1, kth iteration; α represents the coefficient;

[0059] S56, according to C H Constraint correction is performed based on the constraint condition w=1:

[0060] w←wC(C H C) -1 (C H w-1)

[0061] According to the optimal beamforming weight w * , and finally form the anti-interference signal y=w *H X.

[0062] The present invention also provides a readable storage medium on which a computer program is stored. When the computer program is executed, the miniaturized high-performance satellite navigation intelligent anti-interference method is implemented.

[0063] The present invention also provides an electronic device, which includes a processor, a memory, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, it implements the miniaturized high-performance satellite navigation intelligent anti-interference method.

[0064] The present invention also provides a computer program product, which includes a computer program / instruction, and when the computer program / instruction is executed by a processor, it implements the miniaturized high-performance satellite navigation intelligent anti-interference method.

[0065] The advantages of the present invention are:

[0066] (1) The present invention proposes a miniaturized, low-power, high-performance intelligent anti-interference method, which combines compressed sensing MUSIC super-resolution DOA estimation with manifold optimization to achieve anti-interference, and achieves a balance between miniaturization, low power consumption and high performance through algorithm innovation.

[0067] (2) The present invention breaks through the Rayleigh limit through compressed sensing MUSIC and realizes super-resolution DOA estimation (minimum resolution angle 5°) in a four-element array; and adopts sparse reconstruction and manifold optimization to reduce computational complexity, making it suitable for embedded platform deployment; the manifold optimized beamforming null depth reaches -88dB, which is more than 20dB higher than the traditional method.

[0068] (3) This invention achieves super-resolution DOA estimation through compressed sensing MUSIC and combines manifold optimization to solve the optimal beamforming weights, achieving efficient anti-interference in the BeiDou B1 and GPS L1 frequency bands under the four-element constraint. This invention has the advantages of high resolution, zero depth of interference, and low power consumption, making it suitable for small anti-interference navigation terminals. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 Flow chart of the method of the present invention.

[0070] Figure 2 It is an experimental effect diagram of the present invention. DETAILED DESCRIPTION

[0071] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0072] Example 1

[0073] Depend on Figure 1 As shown, a miniaturized high-performance satellite navigation intelligent anti-interference method includes the following steps:

[0074] S1. Array signal reception and preprocessing are performed. Through down-conversion, filtering, and normalization preprocessing, high-quality signal input is provided for subsequent DOA estimation and beamforming.

[0075] Step S1 is specifically as follows:

[0076] S11, according to the input four-element original time domain signal First, down-convert the received high-frequency signal (such as 1561.098MHz for BeiDou B1 and 1575.42MHz for GPS L1) to baseband or intermediate frequency for subsequent digital signal processing.

[0077] S12. Use a bandpass filter to remove out-of-band noise and irrelevant frequency band signals, retaining valid information in the target frequency band (such as Beidou B1 and GPS L1);

[0078] S13, normalize the signal power and output the pre-processed signal Only the effective components of the Beidou B1 and GPS L1 frequency bands are retained, and the power of each channel is balanced to avoid amplitude / phase distortion between array elements.

[0079] S2. Estimate and decompose the covariance matrix. The subsequent super-resolution DOA estimation provides the signal subspace and noise subspace, thereby achieving high-precision estimation of the azimuth angle θ and pitch angle φ of the interference and satellite signals.

[0080] Step S2 is specifically as follows:

[0081] S21, according to the received signal (Matrix form, 4 array elements × T sampling points) to estimate the covariance matrix:

[0082]

[0083] Among them, the diagonal elements R in the covariance matrix R ii represents the signal power (including noise) of the i-th array element; the off-diagonal element R ij It represents the complex correlation between array elements i and j, reflecting the spatial phase difference of the signal (determined by the direction of arrival).

[0084] S22. Perform eigendecomposition on the covariance matrix R to achieve subspace separation:

[0085] R=U∑U H

[0086] Where U represents the eigenvector matrix, the column vectors are the eigendirections; Σ represents the diagonal matrix, the elements are the eigenvalues ​​(arranged in descending order, λ1≥λ2≥…≥λ4).

[0087] The eigenvectors U corresponding to the first K large eigenvalues s =U(:,1:K) represents the signal direction, and the remaining eigenvector U n =U(:,K+1:4) represents the orthogonal complementary space of noise and interference (noise subspace).

[0088] S3. Under the condition of a limited number of array elements (such as a 2×2 four-element array), we break through the traditional Rayleigh resolution limitation and achieve super-resolution direction of arrival (DOA) estimation, thereby accurately separating the azimuth angle θ and pitch angle φ of Beidou B1 and GPS L1 signals from interference, providing high-precision spatial information for subsequent manifold optimized beamforming.

[0089] Step S3 is as follows:

[0090] S31, noise subspace decomposed according to covariance matrix R (K is the number of signals) to build an overcomplete dictionary. The grid division of the overcomplete dictionary is based on the following rules:

[0091] Azimuth angle θ∈[0°,360°), step size 1° (360 points in total);

[0092] Pitch angle φ∈[0°,90°], step size 1° (91 points in total);

[0093] Therefore, the total number of grids is 32760, and the steering vector dictionary (overcomplete dictionary) is generated.

[0094]

[0095] S32. Construct optimization problem:

[0096]

[0097] in, represents a sparse angle vector (only a few non-zero values ​​correspond to the true signal direction); st represents the constraint condition; || ||1 represents the 1-norm; || ||2 represents the 2-norm; ∈ is the threshold; Indicates U n The superscript H represents the conjugate transpose of the matrix.

[0098] S33, using the iterative hard threshold method to solve the optimization problem of step S32, and obtain the sparse angle vector The iteration formula is as follows:

[0099] s k+1 =H K (s k +αΦ H (U n -Φs k ))

[0100] Among them, H K A hard threshold operation to retain the top K largest elements; k+1 、s k represents the sparse angle vector of the k+1, kth iteration; α represents the coefficient.

[0101] S4. Perform angle clustering and screening to remove redundancy, merge, and classify the potential signal directions obtained by sparse reconstruction, eliminate false peaks, accurately mark the true directions of Beidou B1, GPS L1 signals and interference, and provide reliable angle input for manifold optimized beamforming.

[0102] Step S4 is specifically as follows:

[0103] S41. Extract the azimuth angle θ and pitch angle φ corresponding to the non-zero position whose amplitude is significantly higher than the noise threshold in the sparse solution s:

[0104] Points={(θ1,φ1),(θ2,φ2),…,(θ N ,φ N )}

[0105] S42, normalize the azimuth angle θ and the pitch angle φ:

[0106]

[0107] Where Δθ max =360°, Δφ max =90° (normalized to the interval [0,1]), and the threshold ∈ is usually set to 0.05~0.1 (corresponding to the actual angle interval of 5°~10°).

[0108] S43. Traverse each point p and find all points N in its ε neighborhood ∈ (p):

[0109] N ∈ (p)={q∈Points∣d(p,q)≤∈}

[0110] If |N ∈ If (p)|≥MinPts (e.g., MinPts=2), a new cluster C is created and p is added to cluster C; otherwise, no new cluster is created.

[0111] N ∈ For each point q in (p): if q has not been visited, recursively expand its neighborhood and add q to the current cluster C; otherwise, do not add it to the current cluster C.

[0112] Points that cannot be included in any cluster are marked as noise and removed.

[0113] S44. Cluster the scattered grid points into several clusters, each cluster represents a real signal source, and for each cluster C k , take its mean as the final direction estimate:

[0114]

[0115] Beidou B1, GPS L1 and interference directions are distinguished based on prior information such as signal frequency band. If the signal direction matches the known satellite orbit or frequency band, it is marked as Beidou / GPS; otherwise, it is marked as interference and needs to be suppressed.

[0116] S5. Implement beamforming based on manifold optimization and solve the optimal beamforming weight w on the complex unit sphere manifold * , while ensuring the signal gain of Beidou B1 and GPS L1, it maximizes the suppression of interference directions and ultimately outputs a navigation signal with a high signal-to-interference-and-noise ratio (SINR).

[0117] Step S5 is specifically as follows:

[0118] S51. The objective function of constructing manifold optimization is to minimize interference and noise power, that is, (equivalent to maximizing SINR), where R i+n is the interference plus noise covariance matrix. The manifold constraint is that the weights are located on the complex unit sphere w represents weight; W represents the complex unit sphere.

[0119] S52, initialize w0=C(C H C) -1 1, projected onto the complex unit sphere.

[0120] S53, perform gradient calculation Represents gradient calculation.

[0121] S54. Perform tangent space projection and remove the normal component to maintain the manifold constraint: Project w (·) represents the tangent space projection; Re(·) represents the real part of the complex number.

[0122] S55, iterate along the negative gradient direction and renormalize to achieve weight update:

[0123]

[0124] Among them, w k+1 、w k represents the weight of the k+1, kth iteration; α represents the coefficient;

[0125] S56, according to C H Constraint correction is performed based on the constraint condition w=1:

[0126] w←wC(C H C) -1 (C H w-1)

[0127] According to the optimal beamforming weight Finally, the anti-interference signal y=w is formed *H X.

[0128] Figure 2 This is an experimental effect diagram of the present invention (interference nulling).

[0129] Example 2

[0130] An electronic device comprises a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the method of the first embodiment is implemented when the processor executes the computer program.

[0131] The electronic device of the embodiment of the present application can be the mobile device itself, or a stand-alone device independent of the mobile device, which can communicate with the mobile device to receive the collected input signals from them and send the selected target decision behavior to them.

[0132] The electronic device includes one or more processors and memory. The processor may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. The memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor may execute the program instructions to implement the decision-making behavior decision-making method and / or other desired functions of the various embodiments of the present application described above.

[0133] The electronic device may further include input means and output means.

[0134] Example 3

[0135] In addition to the above-mentioned methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps in the decision-making behavior decision-making method according to various embodiments of the present application described in the above-mentioned embodiment 1 of this specification.

[0136] The computer program product may be written in any combination of one or more programming languages ​​to implement the program code for performing the operations of the embodiments of the present application, including object-oriented programming languages ​​such as Java, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0137] Example 4

[0138] An embodiment of the present application may also be a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, causes the processor to execute the steps of the decision-making behavior decision-making method according to various embodiments of the present application described in the above-mentioned embodiment 1.

[0139] The computer-readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can, for example, include but is not limited to a system, device or component of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0140] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A miniaturized high-performance satellite navigation intelligent anti-interference method, characterized in that: The following steps are involved: S1. Acquire array signal X, including BeiDou B1 signal and GPS L1 signal; S2. Estimate and decompose the covariance matrix R of the acquired array signal X. The subsequent super-resolution direction of arrival (DOA) estimation provides the signal subspace and noise subspace. S3. Perform super-resolution DOA estimation to separate the azimuth angle θ and elevation angle φ of BeiDou B1 signal, GPS L1 signal, and interference, providing spatial information for subsequent manifold optimization beamforming. S4. Perform angle clustering and screening to remove redundancy, merge, and classify the potential signal directions obtained from sparse reconstruction, eliminate false peaks, and accurately mark the true directions of Beidou B1 signals, GPS L1 signals, and interference signals, providing angle input for manifold optimization beamforming. S5, beamforming based on manifold optimization, solving the optimal beamforming weight w on the complex unit sphere manifold * , under the premise of ensuring the gain of Beidou B1 signal and GPS L1 signal, the interference direction is suppressed to the maximum extent, and an anti-interference navigation signal is finally output.

2. A miniaturized high-performance satellite navigation intelligent anti-interference method according to claim 1, characterized in that: Step S1 is specifically as follows: receiving an original time domain signal, wherein the original time domain signal includes noise, interference, and a target signal; The original time domain signal is preprocessed by down-conversion, filtering and normalization to retain the effective components of the Beidou B1 and GPS L1 frequency bands.

3. The miniaturized high-performance satellite navigation intelligent anti-interference method according to claim 1, characterized in that: Step S2 is specifically as follows: S21. Estimate the covariance matrix R of the array signal X: Among them, the diagonal elements R in the covariance matrix R ii Represents the signal power of the i-th array element; the off-diagonal element R ij It represents the complex correlation between array elements i and j, reflecting the spatial phase difference of the signal; S22, perform eigendecomposition on the covariance matrix R to achieve subspace separation: R=U∑U H Where U represents the eigenvector matrix, the column vectors are the eigendirections; Σ represents the diagonal matrix, the elements are the eigenvalues, and are arranged in descending order; The eigenvectors U corresponding to the first K large eigenvalues ​​in the eigenvector matrix U s It represents the signal direction, that is, the signal subspace; the remaining eigenvectors in the eigenvector matrix U represent the orthogonal complementary space of noise and interference, that is, the noise subspace.

4. The miniaturized high-performance satellite navigation intelligent anti-interference method according to claim 1, characterized in that: Step S3 is specifically as follows: S31, noise subspace U obtained by decomposing the covariance matrix R n Construct an overcomplete dictionary. The grid division of the overcomplete dictionary is constructed according to the following rules: Azimuth angle θ∈[0°,360°), the grid division step is 1°; The pitch angle φ∈[0°,90°], the grid division step is 1°; Generate an overcomplete dictionary Φ; S32. Construct optimization problem: Where s represents the sparse angle vector; st represents the constraint condition; ||||1 represents the 1-norm; ||||2 represents the 2-norm; ∈ is the threshold; Indicates U n The conjugate transpose of , the superscript H represents the conjugate transpose of the matrix; S33. Solve the optimization problem of step S32 using an iterative hard threshold method to obtain a sparse angle vector s. The iterative formula is as follows: s k+1 =H K (s k +aF H (U n -Φs k )) Among them, H K A hard threshold operation to retain the top K largest elements; k+1 、s k represents the sparse angle vector of the k+1, kth iteration; α represents the coefficient.

5. The miniaturized high-performance satellite navigation intelligent anti-interference method according to claim 4, characterized in that: Step S4 is specifically as follows: S41. Extract the azimuth angle θ and pitch angle φ corresponding to the non-zero position whose amplitude is significantly higher than the noise threshold in the sparse angle vector s: Points={(θ1,φ1),(θ2,φ2),…,(θ N ,f N )} S42, normalize the azimuth angle θ and the pitch angle φ: Where Δθ max =360°, Δφ max =90°; S43. Traverse each point p and find all points N in its ε neighborhood ∈ (p): N ∈ (p)={q∈Points∣d(p,q)≤∈} If |N ∈ (p)|≥MinPts, MinPts is the set minimum number of points, then a new cluster C is created and q is added to cluster C; otherwise, no new cluster is created; N ∈ For each point q in (p), if q has not been visited, recursively expand its neighborhood and add q to the current cluster C; otherwise, do not add it to the current cluster; Mark the points that cannot be included in any cluster as noise and remove them; S44. Cluster the scattered grid points into several clusters, each cluster represents a real signal source, and for each cluster C k , take its mean as the final direction estimate: The directions of Beidou B1 signals, GPS L1 signals, and interference signals are distinguished based on the signal frequency band. If the signal direction matches a known satellite orbit or frequency band, it is marked as a Beidou / GPS signal; otherwise, it is marked as an interference signal and needs to be suppressed.

6. The miniaturized high-performance satellite navigation intelligent anti-interference method according to claim 5, characterized in that: Step S5 is specifically as follows: S51. The objective function of constructing manifold optimization is to minimize interference and noise power, that is, Among them, R i+n is the interference plus noise covariance matrix; The manifold constraint is that the weights lie on the complex unit sphere w represents weight; represents the complex unit sphere; S52, initialize w0=C(C H C) -1 1. Projection onto the complex unit sphere; S53, perform gradient calculation Represents gradient calculation; S54. Perform tangent space projection and remove the normal component to maintain the manifold constraint: Project w (·) represents the tangent space projection; Re(·) represents the real part of the complex number; S55, iterate along the negative gradient direction and renormalize to achieve weight update: Among them, w k+1 、w k represents the weight of the k+1, kth iteration; α represents the coefficient; S56, according to C H Constraint correction is performed based on the constraint condition w=1: w←w-C(C H C) -1 (C H w-1) According to the optimal beamforming weight w * , and finally form the anti-interference signal y=w* H X.

7. A readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed, the miniaturized high-performance satellite navigation intelligent anti-interference method according to any one of claims 1 to 6 is implemented.

8. An electronic device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and capable of running on the processor. When the processor executes the computer program, it implements a miniaturized high-performance satellite navigation intelligent anti-interference method as described in any one of claims 1 to 6.

9. A computer program product, characterized in that It includes a computer program / instruction, which, when executed by a processor, implements a miniaturized high-performance satellite navigation intelligent anti-interference method as described in any one of claims 1 to 6.

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