Miniaturized high-performance satellite navigation intelligent anti-interference method

Abstract

The application discloses a miniaturized high-performance satellite navigation intelligent anti-interference method, relates to the technical field of satellite navigation and artificial intelligence, and combines compressed sensing MUSIC super-resolution DOA estimation and manifold optimization to realize anti-interference. The method realizes super-resolution DOA estimation under unknown steering vectors through compressed sensing MUSIC, combines manifold optimization to solve optimal beamforming weights on a complex unit sphere, and finally realizes efficient anti-interference in the Beidou B1 and GPS L1 frequency bands. The application realizes the balance among miniaturization, low power consumption and high performance through algorithm innovation, breaks through the array processing Rayleigh limit constraint from the perspective of anti-interference signal processing algorithm, and can realize low power consumption, light-weight deployment and less resources while guaranteeing anti-interference performance.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of satellite navigation and artificial intelligence technology, and particularly relates to a miniaturized high-performance satellite navigation intelligent anti-interference method. BACKGROUND

[0002] In a satellite navigation system, a receiver is susceptible to the influence of co-frequency or adjacent frequency interference, resulting in a decrease in positioning accuracy or even failure. Traditional anti-interference technologies (such as spatial filtering and frequency domain filtering) have the following problems:

[0003] 1. Insufficient array degrees of freedom: the number of antenna array elements is limited (such as four array elements of 2x2), and traditional algorithms are difficult to distinguish dense interference signals.

[0004] 2. High computational complexity: super-resolution algorithms (such as MUSIC) have high requirements for hardware resources and are difficult to implement on a low-power platform.

[0005] 3. Limited anti-interference performance: traditional beam forming methods (such as MVDR) have insufficient null depth when the number of array elements is small, and the interference suppression effect is poor. SUMMARY

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

[0007] To achieve the above purpose, the present application adopts the following technical solutions, comprising:

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

[0009] S1, obtaining an array signal X, including a Beidou B1 signal and a GPS L1 signal;

[0010] S2, estimating and decomposing the covariance matrix R of the obtained array signal X, and providing a signal subspace and a noise subspace for subsequent super-resolution direction of arrival (DOA) estimation;

[0011] S3, performing super-resolution DOA estimation to separate the azimuth angle θ and the elevation angle φ of the Beidou B1 signal, the GPS L1 signal and the interference, and providing spatial information for subsequent manifold optimization beam forming;

[0012] S4, performing angle clustering and screening, removing redundant, merging and classifying the potential signal directions obtained by sparse reconstruction, eliminating false peaks, and accurately marking the real directions of the Beidou B1 signal, the GPS L1 signal and the interference signal, and providing angle input for manifold optimization beam forming;

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

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

[0015] Receiving the original time domain signal containing noise, interference and target signal;

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

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

[0018] S21, estimate the covariance matrix R of the array signal X:

[0019]

[0020] Wherein, the diagonal elements R ii of the covariance matrix R represent the signal power of the ith array element; the non-diagonal elements R ij represent the complex correlation between the array elements i and j, reflecting the spatial phase difference of the signal;

[0021] S22, perform eigenvalue decomposition on the covariance matrix R to realize subspace separation:

[0022] R=U∑U H

[0023] Wherein, U represents the eigenvector matrix, and the column vector is the characteristic direction; Σ represents the diagonal matrix, and the element is the eigenvalue, which is arranged in descending order;

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

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

[0026] S31, according to the noise subspace U n Construct an overcomplete dictionary, and the grid division of the overcomplete dictionary is constructed according to the following rules:

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

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

[0029] Generating over-complete dictionary Φ;

[0030] S32, constructing optimization problem:

[0031]

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

[0033] S33, solving the optimization problem of step S32 by using iterative hard threshold method, obtaining sparse angle vector s, iterative formula is as follows:

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

[0035] Wherein, H K Is the hard threshold operation of reserving the first K largest elements; s k+1 , s k Indicate the sparse angle vector of the k+1, k iteration; α represents coefficient.

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

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

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

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

[0040]

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

[0042] S43, traversing each point p, finding all points N ∈ (p) in its ε neighborhood:

[0043] N ∈ (p)={q∈Points∣d(p,q)≤∈}

[0044] If |N ∈ If (p)|≥MinPts, where MinPts is the minimum number of points set, then create a new cluster C and add p to cluster C; otherwise, do not create a new cluster.

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

[0046] Points that cannot be contained in any cluster are marked as noise and discarded;

[0047] S44. Cluster the scattered grid points into several clusters, each cluster representing a real signal source. For each cluster C... k Take their average as the final direction estimate:

[0048]

[0049] The direction of BeiDou B1 signals, GPS L1 signals, and interference signals is 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 as follows:

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

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

[0053] S52, Initialize w0 = C(C H C) -1 1. Projected onto a complex unit sphere;

[0054] S53. Perform gradient calculation Indicates gradient calculation;

[0055] S54. Perform tangent space projection and remove normal components to preserve manifold constraints: Proj w (·) denotes the projection onto the tangent space; Re(·) denotes the real part of the complex number;

[0056] S55, iteration in the negative gradient direction, and re-normalization, weight update is realized:

[0057]

[0058] Wherein, w k+1 , w k Indicate the weight of the k+1, k iteration; Alpha indicates the coefficient;

[0059] S56, according to the constraint condition of C H w=1, constraint correction is carried out:

[0060] w←w-C(C H C) -1 (C H w-1)

[0061] According to the optimal beam forming weight w * , the signal y=w *H X after interference is finally formed.

[0062] The application also provides a readable storage medium, which has a computer program stored thereon, and the computer program is executed to realize the miniaturized high-performance satellite navigation intelligent anti-interference method.

[0063] The application also provides an electronic device, which comprises a processor, a memory, and a computer program stored on the memory and executable on the processor, and the processor executes the computer program to realize the miniaturized high-performance satellite navigation intelligent anti-interference method.

[0064] The application also provides a computer program product, which comprises computer programs / instructions, and the computer programs / instructions are executed by a processor to realize the miniaturized high-performance satellite navigation intelligent anti-interference method.

[0065] The application has the following advantages:

[0066] (1) The application provides a miniaturized, low-power-consumption and high-performance intelligent anti-interference method, which realizes anti-interference by combining compressed sensing MUSIC super-resolution DOA estimation and manifold optimization, and realizes the balance among miniaturization, low power consumption and high performance through algorithm innovation.

[0067] (2) The application breaks through the Rayleigh limit through compressed sensing MUSIC, realizes super-resolution DOA estimation (minimum resolution angle 5°) in a four-element array, adopts sparse reconstruction and manifold optimization to reduce the calculation complexity, is suitable for embedded platform deployment, and adopts the manifold optimization beam forming null depth of-88dB, which is improved by more than 20dB compared with traditional methods.

[0068] (3) The application realizes super-resolution DOA estimation by compressed sensing MUSIC, combines manifold optimization to solve optimal beamforming weight, and realizes efficient anti-interference of Beidou B1 and GPS L1 bands under the limitation of four arrays. BRIEF DESCRIPTION OF DRAWINGS

[0069] Figure 1 A flowchart of the method of the application.

[0070] Figure 2 A graph of experimental results of the application. DETAILED DESCRIPTION

[0071] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the application.

[0072] Embodiment 1

[0073] As shown in FIG. Figure 1 A miniaturized high-performance satellite navigation intelligent anti-interference method includes the following steps:

[0074] S1, array signal reception and preprocessing, through the preprocessing of down-conversion, filtering, normalization, providing high-quality signal input for subsequent DOA estimation and beamforming.

[0075] Step S1 is specifically as follows:

[0076] S11, according to the input four-array original time-domain signal (including noise, interference and target signal), first down-conversion processing is performed, the received high-frequency signal (such as Beidou B1 1561.098 MHz, GPS L1 1575.42 MHz) is shifted to baseband or intermediate frequency, which is convenient for subsequent digital signal processing;

[0077] S12, use a band-pass filter to remove out-of-band noise and irrelevant frequency band signals, and retain the effective information of the target frequency band (such as Beidou B1, GPS L1);

[0078] S13, normalize the signal power, output the preprocessed signal only retaining the effective components of Beidou B1 and GPS L1 frequency bands, balancing the power of each channel, avoiding amplitude / phase distortion between arrays.

[0079] S2, estimate and decompose the covariance matrix, the subsequent super-resolution DOA estimation provides the signal subspace and noise subspace, so as to realize high-precision estimation of the azimuth θ and the elevation angle φ of the interference and satellite signals.

[0080] Step S2 is specifically as follows:

[0081] S21, according to the received signal (4-element matrix × T sampling points) to estimate the covariance matrix:

[0082]

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

[0084] S22, eigenvalue decomposition of the covariance matrix R is carried out to realize subspace separation:

[0085] R = U∑U H

[0086] Among them, U represents the eigenvector matrix, and the column vector is the characteristic direction; Σ represents the diagonal matrix, and the element is the eigenvalue (arranged in descending order, λ1≥λ2≥…≥λ4).

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

[0088] S3, under the condition that the number of elements is limited (such as 2×2 four elements), the traditional Rayleigh resolution limit is broken through, and super-resolution direction of arrival (DOA) estimation is realized, so as to accurately separate the azimuth θ and the elevation angle φ of the Beidou B1, GPS L1 signal and interference, and provide high-precision spatial information for subsequent manifold optimization beam forming.

[0089] Step S3 is specifically as follows:

[0090] S31, according to the noise subspace (K is the number of signals) to construct an over-complete dictionary, and the grid division of the over-complete dictionary is according to the following rules:

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

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

[0093] Therefore, the total number of grids is 32760, and a steering vector dictionary (over-complete dictionary) is generated

[0094]

[0095] S32, construct an optimization problem:

[0096]

[0097] wherein, denotes a sparse angle vector (only a few non-zero values correspond to the true signal direction); s.t. denotes the constraint condition; || ||1 denotes the 1-norm; || ||2 denotes the 2-norm; ∈ is a threshold value; denotes U n , the conjugate transpose of the matrix, and the superscript H denotes the conjugate transpose of the matrix.

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

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

[0100] wherein, H K is a hard threshold operation that retains the first K largest elements; s k+1 , s k denotes the sparse angle vector of the k+1, k iteration; α denotes a coefficient.

[0101] S4, angle clustering and screening, de-redundancy, merging and classification of the potential signal direction obtained by sparse reconstruction, elimination of false peaks, accurate marking of the true direction of Beidou B1, GPS L1 signal and interference, and provision of reliable angle input for manifold optimization beam forming.

[0102] Step S4 is specifically as follows:

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

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

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

[0106]

[0107] where Δθ max = 360°, Δφ max = 90° (normalized to [0, 1] interval), and the threshold ∈ is usually taken as 0.05-0.1 (corresponding to 5°-10° actual angular interval).

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

[0109] N ∈ (p) = {q ∈ Points | d(p, q) ≤ ∈}

[0110] If |N ∈ (p)| ≥ MinPts (e.g. MinPts = 2), create a new cluster C and add p to C; otherwise, do not create a new cluster.

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

[0112] Mark points that cannot be included in any cluster as noise and remove them.

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

[0114]

[0115] According to prior information such as signal frequency band, distinguish between Beidou B1, GPS L1 and interference direction. If the signal direction matches the known satellite orbit or frequency band, mark it as Beidou / GPS; otherwise, mark it as interference, which needs to be suppressed.

[0116] S5, implement manifold optimization-based beamforming, solve the optimal beamforming weight w * on the complex unit sphere manifold, maximize the suppression of interference directions under the premise of ensuring the gain of Beidou B1 and GPS L1 signals, and finally output a navigation signal with high signal-to-interference-and-noise ratio (SINR).

[0117] Step S5 is as follows:

[0118] S51, construct the objective function of manifold optimization as minimizing the interference and noise power, i.e. (equivalent to maximizing SINR), where 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 the weight; W represents the complex unit sphere.

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

[0120] S53, gradient calculation represents the gradient calculation.

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

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

[0123]

[0124] where w k+1 , w k represents the weight of the k+1, k iteration; alpha represents the coefficient;

[0125] S56, constraint correction according to the constraint condition of C H w=1:

[0126] w←w-C(C H C) -1 (C H w-1)

[0127] According to the optimal beamforming weight The final interference-resistant signal y=w *H X is formed.

[0128] Figure 2 is the experimental effect diagram (interference null) of the application.

[0129] Embodiment 2

[0130] An electronic device comprising a processor, a memory, and a computer program stored on the memory and executable on the processor, the processor implementing the method of embodiment 1 when executing the computer program.

[0131] The electronic device of the embodiments 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 therefrom and send the selected target decision behavior thereto.

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

[0133] The electronic device can further include an input device and an output device.

[0134] Embodiment 3

[0135] In addition to the above method and device, the embodiments of the present application can also be a computer program product including computer program instructions, which, when executed by a processor, cause the processor to perform the steps of the decision behavior decision method according to various embodiments of the present application described in Embodiment 1 above.

[0136] The computer program product can be written in any combination of one or more programming languages, including object-oriented programming languages, such as Java, C++, and the like, and conventional procedural programming languages, such as the "C" programming language, or the like. The program code can execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server.

[0137] Embodiment 4

[0138] The embodiments of the present application can also be a computer readable storage medium, having stored thereon computer program instructions, which, when executed by a processor, cause the processor to perform the steps described above in the decision-making method according to various embodiments of the present application.

[0139] The computer readable storage medium can take the form of one or more combinations of any type of computer readable medium. The computer readable medium can be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can include, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or any suitable combination of the above. More specific examples (a non-exhaustive list) of the computer readable storage medium include an electrical connection having one or more wires, a portable disc, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0140] The above merely describes the preferred embodiments of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, and improvement within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A miniaturized high-performance satellite navigation intelligent anti-interference method, characterized in that, The method comprises the following steps: S1, acquiring an array signal X, including a Beidou B1 signal and a GPS L1 signal; S2, estimating and decomposing a covariance matrix R of the acquired array signal X, and providing a signal subspace and a noise subspace for subsequent super-resolution direction of arrival (DOA) estimation; S3, performing super-resolution DOA estimation to separate the azimuth angle θ and the elevation angle φ of the Beidou B1 signal, the GPS L1 signal and interference, and providing spatial information for subsequent manifold optimization beam forming; S4, performing angle clustering and screening to remove redundancy, merge and classify potential signal directions obtained by sparse reconstruction, eliminate false peaks, accurately mark the real directions of the Beidou B1 signal, the GPS L1 signal and interference signals, and provide angle input for manifold optimization beam forming; S5, manifold optimization based beamforming, on the complex unit sphere manifold, solve the optimal beamforming weight w * Under the premise of ensuring the gain of Beidou B1 signal and GPS L1 signal, the interference direction is maximally suppressed, and finally the anti-interference navigation signal is output. Step S4 is specifically as follows: S41, extracting the azimuth angle θ and the elevation angle φ corresponding to the non-zero positions in the sparse angle vector s whose amplitudes are significantly higher than a noise threshold value: Points={(θ1,φ1),(θ2,υ2),…,(θ N ,f N )} S42, performing normalization processing on the azimuth angle θ and the elevation angle φ: where Δθ max = 360°, Δφ max = 90°; S43, traverse each point p, find all points N within its ε neighborhood ∈ (p): N ∈ (p) = {q e Points | d(p, q) < e} If |N ∈ (p) >= MinPts, MinPts is the minimum number of points set, then a new cluster C is built, and p is added to the cluster C; otherwise, no cluster is built. To N ∈ (p) For each point q in P, if q is not visited, recursively expand its neighborhood and add q to the current cluster C; otherwise, do not add q to the current cluster. Marking points that cannot be included in any cluster as noise and eliminating them; S44, clustering the dispersed grid points into several clusters, each cluster representing a real signal source, for each cluster C k , taking the mean value as the final direction estimate: According to the signal frequency band, distinguishing the directions of the Beidou B1 signal, the GPS L1 signal and interference signals, if the signal direction matches the known satellite orbit or frequency band, marking it as a Beidou / GPS signal; otherwise, marking it as an interference signal, which needs to be suppressed; Step S5 is specifically as follows: S51, the objective function of the manifold optimization is constructed to minimize the interference and noise power, i.e. where 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 the weights; represents the complex unit sphere; S52, initialize w0 = C(C H C) -1 1, project to complex unit sphere; S53, perform gradient calculation denotes a gradient calculation; S54. Perform a cut-space projection to remove the normal component to maintain manifold constraints: Proj w (·) denotes a cut-space projection; Re(·) denotes the real part of a complex number; S55, iteratively updating the weight along the negative gradient direction and re-normalizing to realize weight updating: where w k+1 , w k denote the weights of the k+1, k iteration; a denotes a coefficient; S56, according to C H The constraint condition of w = 1 is used to constrain the correction: w <- w - C(C H C) -1 (C H w - 1) According to the optimal beamforming weight w * The final anti-interference signal y = w *H X.

2. The 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 containing noise, interference and target signals; Performing preprocessing such as frequency down-conversion, filtering and normalization on the original time domain signal to retain effective components in 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, estimating the covariance matrix R of the array signal X: wherein the diagonal elements R ii of the covariance matrix R represent the signal power of the i-th array element; the off-diagonal elements R ij represent the complex correlation between the array elements i and j, reflecting the spatial phase difference of the signal. S22, performing eigenvalue decomposition on the covariance matrix R to realize subspace separation: R = U∑U H Wherein, U represents an eigenvector matrix, and the column vector is a characteristic direction; Σ represents a diagonal matrix, and the elements are eigenvalues arranged in descending order; The first K eigenvectors of the eigenvector matrix U corresponding to the largest eigenvalues s The eigenvectors in the eigenvector matrix U that do not correspond to the largest eigenvalues characterize the orthogonal complement of the signal subspace, i.e., 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、According to the noise subspace U obtained by decomposing the covariance matrix R n The over-complete dictionary is constructed, and the grid division of the over-complete dictionary is constructed according to the following rules: The azimuth angle θ is in the range of [0°, 360°), and the grid division step is 1°; The elevation angle φ is in the range of [0°, 90°], and the grid division step is 1°; Generating an over-complete dictionary Φ; S32, constructing an optimization problem: where s denotes a sparse angle vector; s.t. denotes a constraint condition; || ||1 denotes a 1-norm; || ||2 denotes a 2-norm; and ∈ is a threshold value; denotes U n denotes a conjugate transpose of U, and a superscript H denotes a conjugate transpose of a matrix. S33, solving the optimization problem of step S32 by using an iterative hard threshold value method to obtain a sparse angle vector s, and the iterative formula is as follows: s k+1 = H K (s k + aΦ H (U n - Φs k )) where H K is a hard thresholding operation that retains the top K largest elements; s k+1 , s k denotes the sparse angle vector of the k+1, k iteration; a denotes the coefficient.

5. A readable storage medium characterized by, The computer program is stored thereon, and the computer program is executed to realize the small-sized high-performance satellite navigation intelligent anti-interference method of any one of claims 1-4.

6. An electronic device, comprising: It comprises a processor, a memory and a computer program stored on the memory and executable on the processor, and the processor executes the computer program to realize the small-sized high-performance satellite navigation intelligent anti-interference method of any one of claims 1-4.

7. A computer program product, characterised in that, It comprises a computer program / instruction, which is executed by a processor to realize the small-sized high-performance satellite navigation intelligent anti-interference method of any one of claims 1-4.

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