Communication anti-interference method and device of navigation system and electronic equipment

Abstract

The invention discloses a communication anti-interference method and device of a navigation system and electronic equipment. The method relates to the communication field of the navigation system, and comprises the following steps: acquiring a first signal received by a satellite communication antenna of the navigation system and second signals received by a plurality of sampling antenna units in a preset antenna array; performing band-pass filtering on the first signal and the plurality of second signals to obtain a filtered first signal and a plurality of filtered second signals; performing nonlinear transformation processing on the plurality of filtered second signals to obtain interference cancellation signals; the filtered first signal and the interference cancellation signal are subjected to signal synthesis, a cancellation output signal is obtained, and the average power of the cancellation output signal is smaller than or equal to preset power. According to the invention, the technical problem of poor communication anti-interference capability of a navigation system in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the communication field of a navigation system, and in particular to a communication anti-interference method, device and electronic equipment of a navigation system. Background Art

[0002] With the widespread application of navigation systems (such as Beidou terminal products), the open environment of Beidou signal transmission and the disclosure of many core information, the threat of interference to Beidou satellite navigation and communication terminals has become increasingly obvious, mainly in terms of signal transmission, signal locking, combination code despreading, positioning and solving, etc., especially in the face of increased power and diversified means of hostile malicious interference in wartime (such as tracking interference, blocking interference, etc.), the existing anti-interference technology is difficult to fully cope with these new interference means, which leads to the poor ability of navigation system's communication anti-interference.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The embodiments of the present invention provide a method, device and electronic device for communication anti-interference of a navigation system, so as to at least solve the technical problem that the communication anti-interference capability of the navigation system in the related art is poor.

[0005] According to one aspect of an embodiment of the present invention, a communication anti-interference method for a navigation system is provided, comprising: acquiring a first signal received by a satellite communication antenna of the navigation system, and a second signal received by a plurality of sampling antenna units in a preset antenna array; performing bandpass filtering on the first signal and the plurality of second signals to obtain a filtered first signal and a plurality of filtered second signals; performing nonlinear transformation processing on the plurality of filtered second signals to obtain an interference cancellation signal; performing signal synthesis on the filtered first signal and the interference cancellation signal to obtain a cancellation output signal, wherein the average power of the cancellation output signal is less than or equal to the preset power.

[0006] Furthermore, a plurality of filtered second signals are subjected to nonlinear transformation processing to obtain an interference cancellation signal, including: performing nonlinear transformation processing on each filtered second signal to obtain a transformed second signal; and performing linear weighted synthesis on a plurality of transformed second signals to obtain an interference cancellation signal.

[0007] Furthermore, linear weighted synthesis is performed on multiple transformed second signals to obtain an interference cancellation signal, including: determining an initial weighted value of each transformed second signal; adjusting the initial weighted value through an adaptive filtering algorithm to obtain a target weighted value of each transformed second signal; and linear weighted synthesis is performed on multiple transformed second signals based on the target weighted value to obtain an interference cancellation signal.

[0008] Furthermore, the initial weighted value is adjusted by an adaptive filtering algorithm to obtain a target weighted value for each transformed second signal, including: using a cyclic multi-signal classification algorithm to estimate the signal source direction of the navigation system; adjusting the initial weighted value based on the signal source direction to obtain an adjusted weighted value; linearly weighting and synthesizing multiple transformed second signals based on the adjusted weighted value to obtain a synthesized signal; in response to the average power of the synthesized signal being greater than a preset power, using the adjusted weighted value as the initial weighted value, and repeatedly performing the steps of adjusting the initial weighted value based on the signal source direction to obtain an adjusted weighted value, and linearly weighting and synthesizing multiple transformed second signals based on the adjusted weighted value to obtain a synthesized signal, until the average power of the synthesized signal is less than or equal to the preset power; in response to the average power of the synthesized signal being less than or equal to the preset power, determining the adjusted weighted value as the target weighted value.

[0009] Furthermore, a cyclic multi-signal classification algorithm is used to estimate the signal source direction of the navigation system, including: establishing an array receiving model of the navigation system, wherein the array receiving model is used to characterize signals corresponding to multiple signal sources, and the multiple signal sources include: satellite expected signals, suppression interference, deceptive interference, and Gaussian additive white noise; based on the non-zero cyclic frequency of the navigation system, determining the cyclic autocorrelation matrix of the first signal; performing singular value decomposition on the cyclic autocorrelation matrix to obtain a signal subspace and a noise subspace; based on the signal subspace and the noise subspace, constructing a spatial spectrum expression; and determining the signal source direction based on the spatial spectrum expression.

[0010] Furthermore, the first signal and the multiple second signals are bandpass filtered to obtain a filtered first signal and multiple filtered second signals, including: filtering the first signal using a time domain filter and a frequency filter to obtain a filtered first signal; filtering each second signal using a time domain filter and a frequency filter to obtain a filtered second signal.

[0011] According to another aspect of an embodiment of the present invention, a communication anti-interference device of a navigation system is also provided, including: an acquisition module, used to acquire a first signal received by a satellite communication antenna of the navigation system, and a second signal received by multiple sampling antenna units in a preset antenna array; a filtering module, used to perform bandpass filtering on the first signal and multiple second signals to obtain a filtered first signal and multiple filtered second signals; a processing module, used to perform nonlinear transformation processing on the multiple filtered second signals to obtain an interference cancellation signal; and a synthesis module, used to perform signal synthesis on the filtered first signal and the interference cancellation signal to obtain a cancellation output signal, wherein the average power of the cancellation output signal is less than or equal to the preset power.

[0012] According to another aspect of an embodiment of the present invention, there is further provided an electronic device, comprising: a memory storing an executable program; and a processor for running the program, wherein the method in each embodiment of the present invention is executed when the program is running.

[0013] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the methods in various embodiments of the present invention.

[0014] According to another aspect of an embodiment of the present invention, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the method in each embodiment of the present invention is implemented.

[0015] According to another aspect of an embodiment of the present invention, a computer program product is provided, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method in each embodiment of the present invention is implemented.

[0016] According to another aspect of the embodiments of the present invention, a computer program is further provided. When the computer program is executed by a processor, the methods in the embodiments of the present invention are implemented.

[0017] In an embodiment of the present invention, a first signal received by a satellite communication antenna of a navigation system and a second signal received by a plurality of sampling antenna units in a preset antenna array are obtained; the first signal and the plurality of second signals are bandpass filtered to obtain a filtered first signal and a plurality of filtered second signals; the plurality of filtered second signals are subjected to nonlinear transformation processing to obtain an interference cancellation signal; the filtered first signal and the interference cancellation signal are synthesized to obtain a cancellation output signal, wherein the average power of the cancellation output signal is less than or equal to the preset power. It is easy to notice that by performing bandpass filtering and nonlinear transformation processing on the received first signal and the plurality of second signals, the interference in the signal can be effectively reduced and the purity of the signal can be improved. Furthermore, by performing signal synthesis on the filtered first signal and the interference cancellation signal, the signal quality can be further optimized, so that the obtained cancellation output signal is more stable and reliable, and the purpose of improving the communication anti-interference capability of the navigation system is achieved, thereby realizing the technical effect that the navigation system can perform stable communication with a higher communication anti-interference capability, thereby solving the technical problem of poor communication anti-interference capability of the navigation system in the related art. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0019] Figure 1 is a flow chart of a communication anti-interference method of a navigation system according to an embodiment of the present invention;

[0020] Figure 2 is a schematic diagram of an optional time domain anti-interference technology according to an embodiment of the present invention;

[0021] Figure 3 is a schematic diagram of an optional frequency domain anti-interference technology according to an embodiment of the present invention;

[0022] Figure 4 is a schematic diagram of an anti-interference method based on an array receiving model according to an embodiment of the present invention;

[0023] Figure 5 is a schematic diagram of a communication anti-interference device for a navigation system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. 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 creative work should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] According to an embodiment of the present invention, an embodiment of a communication anti-interference method for a navigation system is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0027] Figure 1 is a flow chart of a communication anti-interference method of a navigation system according to an embodiment of the present invention. Figure 1 As shown, the method comprises the following steps:

[0028] Step S102, obtaining a first signal received by a satellite communication antenna of a navigation system and a second signal received by a plurality of sampling antenna units in a preset antenna array.

[0029] The above-mentioned navigation system may be a global navigation satellite system, a regional navigation system, or a surface navigation system. In this embodiment, the global navigation satellite system is used as an example for description, but it is not limited to it. Among them, the types of global navigation satellite systems may include but are not limited to: global positioning system, global navigation satellite system, Galileo and Beidou satellite navigation system. In this embodiment, the Chinese Beidou satellite navigation system is used as an example for description, but it is not limited to it. The above-mentioned first signal may be a satellite expected signal.

[0030] The above-mentioned preset antenna array can be an antenna system set in advance by a technician, which is composed of multiple antenna units arranged in a specific geometric layout, and is used to receive and process signals from different directions. Among them, the preset antenna array includes the following main functions: directivity enhancement, beamforming, multipath signal processing, interference source positioning and suppression, signal synthesis and optimization, and increased system robustness. Among them, directivity enhancement includes: the array antenna can form a main beam and a sub-beam in a specific direction by adjusting the phase and amplitude of each antenna unit, thereby enhancing the directivity of the required signal and improving the gain and signal-to-noise ratio (SNR) of signal reception. This is especially important for satellite signals because satellite signals usually come from a specific zenith or sky direction. Beamforming includes: the preset antenna array can form a dynamically adjustable beam, which allows the system to adjust the beam pointing in real time according to the environmental changes of the received signal (such as the change in the position of the interference source) to improve the reception of the desired signal while suppressing interference signals from other directions. Multipath signal processing includes: in an environment with multipath propagation, the preset antenna array can capture signals from different paths and use these signals to improve the detection and positioning accuracy of the signal. This is because the signals received by each antenna unit may be different, and these differences can be used to eliminate the multipath effect. Interference source positioning and suppression include: by analyzing the signals received by the preset antenna array, the direction of the interference source (DOA) can be estimated, so as to actively suppress these interferences from specific directions during the reception process. Cyclic Multiple Signal Classification (Cyclic-MUSIC or CMUSIC) based on cyclostationary signal processing is one of the technologies for implementing interference suppression based on the signals received by the preset antenna array. Signal synthesis and optimization include: the signals received by the preset antenna array can be optimized by weighted combination, filtering and other signal processing techniques to improve the overall performance of the system. For example, nonlinear transformation through nonlinear space-time beamformer can retain or enhance the desired signal while suppressing interference. Increasing system robustness includes: the use of preset antenna array increases the redundancy of the system. Even if some antenna units are interfered or damaged, the system can still use the signals received by other antenna units to work normally, thereby improving the robustness and reliability of the system. The preset antenna array is particularly important in complex electromagnetic environments. It enables the navigation system to accurately receive and process satellite signals in the presence of multiple interference sources, and is one of the key technologies to improve anti-interference capabilities.Among them, the Cyclic-MUSIC algorithm is an extension of the traditional Multiple Signal Classification (MUSIC) algorithm. The traditional MUSIC algorithm is based on the instantaneous covariance of the signal, while Cyclic-MUSIC uses the cyclic covariance characteristics of the signal, that is, the periodic correlation of the signal at a certain cyclic frequency (related to the delay or modulation characteristics of the signal). For satellite navigation signals, such as those used by the Beidou system, their cyclic frequency is usually related to the spread spectrum code period.

[0031] In an optional embodiment, in order to solve the technical problem of poor anti-interference ability of communication of the navigation system in the prior art, an array reception model can be first established in this embodiment, wherein the array reception model at least includes: a satellite communication antenna and a preset antenna array. At this time, the first signal received by the satellite communication antenna of the navigation system and the second signal received by multiple sampling antenna units in the preset antenna array can be obtained through the array reception model. For example, the satellite desired signal received by the satellite communication antenna of the navigation system and the second signal received by multiple sampling antenna units in the preset antenna array can be obtained through the array reception model.

[0032] Step S104: bandpass filter the first signal and the plurality of second signals to obtain a filtered first signal and a plurality of filtered second signals.

[0033] In an optional embodiment, when the first signal and the plurality of second signals are obtained, the first signal and the plurality of second signals may be bandpass filtered to obtain a filtered first signal and a plurality of filtered second signals. For example, the first signal may be filtered using a time domain filter and a frequency filter to obtain a filtered first signal; and each second signal may be filtered using a time domain filter and a frequency filter to obtain a filtered second signal.

[0034] It should be noted that when the first signal and multiple second signals are bandpass filtered by a bandpass filter, an adaptive filtering technology can be used, that is, using the least mean square algorithm or the recursive least squares method to adjust the filter coefficients in real time to cancel the interference signal.

[0035] Step S106: performing nonlinear transformation processing on the plurality of filtered second signals to obtain interference cancellation signals.

[0036] The above-mentioned interference cancellation signal is a signal used in communication, radar and navigation systems to suppress or eliminate the influence of interference sources on the desired signal. In a complex electromagnetic environment, the communication system may be subject to various types of interference, including narrowband interference, broadband interference, multipath interference, and intentional interference (such as blocking, tracking or deceptive interference). In order to protect system performance and improve the reception quality and reliability of the signal, a special signal, namely the interference cancellation signal, is designed and generated to be combined with the received signal to achieve the effect of suppressing or eliminating interference.

[0037] In an optional embodiment, when a plurality of filtered second signals are obtained, a nonlinear transformation process can be performed on the plurality of filtered second signals to obtain an interference cancellation signal. For example, first, a plurality of filtered second signals can be subjected to signal preprocessing, and then a nonlinear transformation can be performed on the plurality of filtered second signals after preprocessing, for example, a nonlinear transformation can be performed using high-order statistics, square law detection, absolute value detection and other technologies and signal features can be extracted to obtain signal features after nonlinear transformation, and then an interference signal can be obtained through the signal features after nonlinear transformation, and finally, an interference cancellation process can be performed on the interference signal to obtain an interference cancellation signal. For another example, a nonlinear transformation process can be performed on each filtered second signal to obtain a transformed second signal; and a plurality of transformed second signals can be subjected to linear weighted synthesis to obtain an interference cancellation signal.

[0038] Step S108: synthesize the filtered first signal and the interference cancellation signal to obtain a cancellation output signal, wherein the average power of the cancellation output signal is less than or equal to a preset power.

[0039] The cancellation output signal is a result signal that suppresses or eliminates specific interference components by synthesizing one or more signals with the interference cancellation signal in phase or amplitude. In communication, radar, navigation and other signal processing systems, the generation and use of cancellation output signals is an important means to improve signal quality and communication reliability, especially in environments with severe interference.

[0040] The above-mentioned preset power can be set in advance by a technician and is used to control the average power of the cancellation output signal. The specific value of the preset power is not specifically limited in this embodiment, and the technician can set it according to actual needs.

[0041] In an optional embodiment, when the interference cancellation signal is obtained, the filtered first signal and the interference cancellation signal can be synthesized to obtain the cancellation output signal. For example, the interference cancellation signal and the first signal can be phase-aligned first, so that at the phase reference point of the first signal, the phase of the interference cancellation signal can be opposite to the interference part of the first signal to achieve accurate cancellation. Secondly, a weighted algorithm can be applied, which usually involves weighting the cancellation signal to ensure that the cancellation signal can effectively suppress interference while avoiding weakening the strength of the first signal during synthesis. The weighting coefficient may need to be adjusted in time according to the signal environment and interference characteristics. Then the filtered first signal and the weighted interference cancellation signal can be superimposed in phase, that is, vector synthesis, to achieve the purpose of interference cancellation. This step can be implemented in the signal processing unit by simple vector addition. Finally, when the cancellation output signal is synthesized, the average power of the cancellation output signal can be measured. This can be done by calculating the instantaneous power of the signal over a period of time and then averaging.

[0042] It should be noted that, when the cancellation output signal is obtained, the average power of the cancellation output signal can be compared with the preset power. If the average power of the synthesized signal exceeds the preset power, adjustment is required to avoid signal overload. The adjustment may involve adjusting the gain during signal synthesis, the parameters of the filter, or the amplitude of the cancellation signal.

[0043] In an embodiment of the present invention, a first signal received by a satellite communication antenna of a navigation system and a second signal received by a plurality of sampling antenna units in a preset antenna array are obtained; the first signal and the plurality of second signals are bandpass filtered to obtain a filtered first signal and a plurality of filtered second signals; the plurality of filtered second signals are subjected to nonlinear transformation processing to obtain an interference cancellation signal; the filtered first signal and the interference cancellation signal are synthesized to obtain a cancellation output signal, wherein the average power of the cancellation output signal is less than or equal to the preset power. It is easy to notice that by performing bandpass filtering and nonlinear transformation processing on the received first signal and the plurality of second signals, the interference in the signal can be effectively reduced and the purity of the signal can be improved. Furthermore, by performing signal synthesis on the filtered first signal and the interference cancellation signal, the signal quality can be further optimized, so that the obtained cancellation output signal is more stable and reliable, and the purpose of improving the communication anti-interference capability of the navigation system is achieved, thereby realizing the technical effect that the navigation system can perform stable communication with a higher communication anti-interference capability, thereby solving the technical problem of poor communication anti-interference capability of the navigation system in the related art.

[0044] Optionally, a plurality of filtered second signals are subjected to nonlinear transformation processing to obtain an interference cancellation signal, including: performing nonlinear transformation processing on each filtered second signal to obtain a transformed second signal; and performing linear weighted synthesis on a plurality of transformed second signals to obtain an interference cancellation signal.

[0045] In an optional embodiment, when a plurality of filtered second signals are obtained, the plurality of filtered second signals may first be subjected to nonlinear transformation processing to obtain transformed second signals. The purpose of the nonlinear transformation is to reveal the nonlinear characteristics in the plurality of second signals, which are particularly important in interference suppression. The nonlinear transformation may be performed in a variety of ways, depending on the difference between the interference signal and the desired signal and the design requirements of the system. For example, the square law transformation, absolute value detection, high-order statistics, and wavelet transformation may be used to perform nonlinear transformation processing to obtain the transformed second signal.

[0046] Furthermore, when a plurality of transformed second signals are obtained, the plurality of transformed second signals may be linearly weighted and synthesized to obtain an interference cancellation signal. For example, the initial weight value of each transformed second signal may be determined first, and then the initial weight value may be adjusted by an adaptive filtering algorithm to obtain a target weight value of each transformed second signal, and finally, the plurality of transformed second signals may be linearly weighted and synthesized based on the target weight value to obtain an interference cancellation signal.

[0047] Optionally, linear weighted synthesis is performed on multiple transformed second signals to obtain an interference cancellation signal, including: determining an initial weighted value of each transformed second signal; adjusting the initial weighted value through an adaptive filtering algorithm to obtain a target weighted value of each transformed second signal; and linearly weighted synthesis is performed on multiple transformed second signals based on the target weighted value to obtain an interference cancellation signal.

[0048] In an optional embodiment, the interference cancellation signal may be obtained by the following formula:

[0049]

[0050] in, j j (k) are the Ith first signal, the fth deceptive interference signal among the multiple second signals, and the jth suppression interference, respectively. The superscripts s and a represent the first signal and the deceptive interference, a(θ1), a(θ f ) and b(θ j ) are their corresponding steering vectors (i.e., target weighted values).

[0051] In an optional embodiment, the initial weighted value may be adjusted by the following formula to obtain the target weighted value of each transformed second signal:

[0052]

[0053] Among them, j represents the jth suppression interference, M represents the number of sampling filling units, λ represents the characteristic value of satellite and deceptive interference, and θ1 represents the initial weighted weight.

[0054] Optionally, the initial weighted value is adjusted by an adaptive filtering algorithm to obtain a target weighted value for each transformed second signal, including: using a cyclic multi-signal classification algorithm to estimate the signal source direction of the navigation system; adjusting the initial weighted value based on the signal source direction to obtain an adjusted weighted value; linearly weighted synthesizing multiple transformed second signals based on the adjusted weighted value to obtain a synthesized signal; in response to the average power of the synthesized signal being greater than a preset power, using the adjusted weighted value as the initial weighted value, and repeatedly performing the steps of adjusting the initial weighted value based on the signal source direction to obtain an adjusted weighted value, and linearly weighted synthesizing multiple transformed second signals based on the adjusted weighted value to obtain a synthesized signal, until the average power of the synthesized signal is less than or equal to the preset power; in response to the average power of the synthesized signal being less than or equal to the preset power, determining the adjusted weighted value as the target weighted value.

[0055] The above-mentioned cyclic multi-signal classification algorithm is a signal processing technology based on cyclostationarity, which is mainly used to detect and classify signals with known cyclic frequency characteristics in complex electromagnetic environments. This algorithm is particularly suitable for the detection and analysis of communication signals, especially for those signals that exhibit periodic structures at certain specific times or frequencies.

[0056] In an optional embodiment, first, the cyclic autocorrelation matrix of the signal source can be calculated by a cyclic multiple signal classification (Cyclic-MUSIC) algorithm, with special attention paid to the non-zero cyclic frequency characteristics of the first signal and the interference signal. Secondly, the calculated cyclic autocorrelation matrix can be subjected to singular value decomposition (SVD) to separate the signal subspace and the noise subspace. Then, the spatial spectrum function of the Cyclic-MUSIC algorithm can be used to search for the direction of the signal source, i.e., DOA (Direction of Arrival) estimation. Finally, the estimated direction of the signal source can be obtained by analyzing the peak value of the spatial spectrum function.

[0057] Furthermore, when the direction of the signal source is obtained, the signal source direction information obtained by the Cyclic-MUSIC algorithm can be used. First, a set of weighted weights can be initially set, and these weighted weights are used in subsequent linear weighted synthesis to emphasize or suppress signals in specific directions. Secondly, the adjusted weighted weights can be applied to each nonlinearly transformed second signal based on the signal source direction, and then all weighted signals can be linearly synthesized to obtain a synthesized signal.

[0058] Furthermore, when a synthesized signal is obtained, the average power of the synthesized signal can be measured first. If the average power of the synthesized signal is greater than a preset power threshold, the weighted value needs to be iteratively adjusted. Among them, an adaptive algorithm (such as LMS, RLS) can be used to adjust the weighted value to reduce the average power of the synthesized signal. Repeat the steps of signal source direction estimation, weighted value adjustment and linear weighted synthesis until the average power of the synthesized signal meets the preset power limit. When the average power of the synthesized signal is less than or equal to the preset power threshold, the iterative process is stopped. The final adjusted weighted value is determined as the target weighted value for subsequent signal processing.

[0059] This series of steps aims to achieve efficient signal processing and interference suppression in navigation systems by utilizing the DOA estimation capability of the Cyclic-MUSIC algorithm, combined with linear weighted synthesis and adaptive power control. In particular, in satellite navigation and communication systems, this method can effectively improve the system's anti-interference capability and signal reception quality in complex electromagnetic environments, ensuring the system's stable operation and reliable performance. When implemented, this is usually implemented on a digital signal processor (DSP) or dedicated hardware (such as FPGA) to meet the needs of real-time processing and dynamic adjustment.

[0060] Optionally, a cyclic multi-signal classification algorithm is used to estimate the signal source direction of a navigation system, including: establishing an array receiving model of the navigation system, wherein the array receiving model is used to characterize signals corresponding to multiple signal sources, and the multiple signal sources include: satellite expected signals, suppression interference, deceptive interference, and Gaussian additive white noise; based on the non-zero cyclic frequency of the navigation system, determining the cyclic autocorrelation matrix of the first signal; performing singular value decomposition on the cyclic autocorrelation matrix to obtain a signal subspace and a noise subspace; based on the signal subspace and the noise subspace, constructing a spatial spectrum expression; and determining the signal source direction based on the spatial spectrum expression.

[0061] The array receiving model mentioned above includes: a satellite communication antenna, a plurality of sampling antenna units, a bandpass filter, a nonlinear space-time beamformer and an adaptive filtering algorithm.

[0062] In an optional embodiment, when the array receiving model is established, the cyclic autocorrelation matrix of the first signal can be determined based on the non-zero cyclic frequency of the navigation system through the array receiving model. The autocyclic correlation matrix R of the first signal can be obtained by the following steps: XX :

[0063] Among them, the deceptive jammer receives the satellite desired signal in advance, and transmits it in a specific direction after increasing its power and delay. Therefore, the deceptive jammer has the same properties as the satellite signal, with the same cycle frequency and repetition period. The formula is as follows:

[0064]

[0065] The first term of the formula is composed of N satellite signals and deceptive interference, and the second term is still the linear superposition of J suppression interference. As can be seen from the previous text, for Beidou-2 satellite signals and deceptive interference, when delayed by one spread spectrum code period, they show a periodic delay characteristic:

[0066]

[0067] Among them, T SCC is the delay spread spectrum code period, the first term of the formula is the delay of one spread spectrum code period T SCC When there are N satellite signals and deceptive interference, the second term is the delay of one spread spectrum code period T SCC When J suppression interferences are linearly superimposed.

[0068] Self-circular correlation matrix R XX The formula is:

[0069] R xx =Ex(k)x H (kT scc )}

[0070] =R s +R j +R n ;

[0071] Among them, R xx is the autocorrelation matrix, x(k) is a vector, and E is the energy of each symbol of the satellite signal.

[0072] For Gaussian white noise, since n(k)≠n(kT ss ) Its correlation value is very small, so the Rn term contributes less to the autocorrelation matrix Rxx. However, as mentioned above, the periodic delay characteristic can also be regarded as a special case of the cyclostationary characteristic with a cyclic frequency ε = 0. At this time, some interferences such as single frequency, pulse, and swept frequency still have components based on the autocorrelation of the periodic delay. At this time, if the matrix R xxPerforming eigenvalue decomposition, we can get:

[0073]

[0074] Among them, λ i (i=1,…,N) are the N largest eigenvalues ​​corresponding to the satellite desired signal and deceptive interference, and are the J largest eigenvalues ​​corresponding to the suppression interference. The remaining MNJ eigenvalues ​​are equal, all of which are σ 2 n At this point, we can see that the subspace U spanned by the eigenvectors belonging to the noise n = span{e1,…,e M-J-N+1} and the direction vector of the suppression interference B = [b(θ),…,b(θ J )] are perpendicular to each other. It can be seen that Cyclic-MUSIC based on periodic delay can only suppress noise that is not related to the satellite signal cycle, and the frequency selectivity of the suppressive interference and the extended performance are limited. In addition, it can be seen from the above that the Beidou satellite signal has a cycle frequency of 1 / T, the inverse of the spread spectrum code period. scc There is also a strong autocorrelation when . Then the cyclic autocorrelation matrix of vector x(k) is:

[0075]

[0076] in:

[0077]

[0078] Among them, T s is the time domain sampling interval, NT s is the total delay time, and the positive integer N satisfies: when τ≤NT s hour, is not zero and changes rapidly, This is the covariance matrix constructed in practice. At this time, due to the suppression of interference and noise at ε = 1 / T scc There is no spectral correlation characteristic at this point, so the above formula can be rewritten as:

[0079]

[0080] In the above formula, γ j , σ 2 n Ideally, it is zero, but in practice, it is smaller, and larger values ​​of λ i It only belongs to satellite signals and deceptive interference. At this time, the subspace composed of eigenvectors corresponding to smaller values ​​is only orthogonal to the array response of satellite signals and deceptive interference. In addition, unlike the traditional MUSIC algorithm that performs eigenvalue decomposition on the covariance matrix of the received signal, due to the circulant correlation matrix R xx(ε; τ) is not a Hermitian matrix, so singular value decomposition is used to obtain the subspace of signal and noise.

[0081] R xx (ε;τ)=UΣV H ;

[0082] Among them, they are left and right singular matrices, respectively, and the left singular column vector u i and the right singular column vector v i ∑ is a diagonal matrix, and the diagonal elements are exactly the matrix R xx The singular values ​​of (ε; τ) are R xx The singular values ​​of (ε; τ) are arranged from large to small:

[0083] λ1>λ2>…>λ M >0;

[0084] Because R xx (ε; τ) has N larger singular values ​​belonging to satellite signals and deceptive interference, denoted by Σ s =diagλ1,λ2,…,λ N ) The remaining MN smaller singular values ​​belong to suppression interference and noise, denoted as Σ n =diag(λ N+1 ,λ N+2 , …, M ) , so U s and V s It is composed of the singular vectors corresponding to the first E larger singular values: U s =[u1,…,u E ],V s =[v1,…,v E ],U n and V n It is composed of singular vectors corresponding to MN smaller singular values: U n =[u N+1 …,u M ],V n =[v N+1 , …, v M ]. Among them, U s and V s The space formed by column vectors is called signal subspace; n and V n The space formed by the column vector is called the suppressed interference and noise subspace. According to the perpendicular relationship between the subspaces and the fact that the space formed by the column vectors of the signal subspace and the direction vector A is the same subspace, it can be obtained that the suppressed interference and noise subspace and the direction vector of the signal are also orthogonal to each other:

[0085]

[0086] Therefore, the spatial spectrum expression of the Cyclic-MUSIC algorithm is:

[0087]

[0088] Among them, P Cyclic-MUSIC (θ) is also called the cyclic space spectrum. The maximum value is found by searching the space with the above formula, which realizes the DOA estimation of the algorithm.

[0089] From the above derivation, it can be seen that since the non-zero cyclic frequency of the satellite signal replaces the periodic delay characteristics, the suppressive interference and noise do not show spectral correlation at the cyclic frequency ε=1 / T, so that the Cyclic-MUSIC algorithm based on non-zero cyclic frequency is superior to the traditional MUSIC algorithm and the algorithm based on periodic delay. After the Cyclic-MUSIC algorithm is used to estimate the arrival angle information of each source, it can be used to assist the deceptive interference identification process and further confirm the deceptive interference.

[0090] Optionally, the first signal and multiple second signals are bandpass filtered to obtain a filtered first signal and multiple filtered second signals, including: filtering the first signal using a time domain filter and a frequency filter to obtain a filtered first signal; filtering each second signal using a time domain filter and a frequency filter to obtain a filtered second signal.

[0091] In an optional embodiment, the first signal may be filtered using a time domain filter and a frequency filter to obtain a filtered first signal, and each second signal may be filtered using a time domain filter and a frequency filter to obtain a filtered second signal.

[0092] Further, Figure 2 is a schematic diagram of an optional time domain anti-interference technology according to an embodiment of the present invention, such as Figure 2 As shown, first, noise and interference can be added to the signal to obtain a mixed signal. Secondly, the mixed signal can be subjected to RF processing to obtain a processed signal. Then, the processed signal can be integrated through the chip period to obtain an integrated signal. Then, the integrated signal can be input into a time domain filter to obtain a filtered signal. Then, a pseudorandom noise code (PRN) can be synchronized with the filtered signal. Finally, the synchronized signal can be summed and outputted, that is, an anti-interference signal after time domain processing can be obtained.

[0093] Further, Figure 3 is a schematic diagram of an optional frequency domain anti-interference technology according to an embodiment of the present invention, such as Figure 3 As shown, first, noise and interference can be added to the signal to obtain a mixed signal, and then the mixed signal can be windowed to obtain a processed signal, and then the processed signal can be subjected to Fast Fourier Transform (Fast Fourier Transform, FFT) to obtain a transformed frequency domain signal, and then the transformed frequency domain signal can be subjected to notch processing to obtain a processed signal, and finally the processed signal can be subjected to Inverse Fast Fourier Transform (Inverse Fast Fourier Transform, IFFT) to obtain an anti-interference signal after frequency domain processing.

[0094] Figure 4 is a schematic diagram of an anti-interference method based on an array receiving model according to an embodiment of the present invention, such as Figure 4 As shown in FIG, the array receiving model includes: a satellite communication antenna, multiple sampling antenna units, a bandpass filter, a nonlinear space-time beamformer, and an adaptive filtering algorithm. Among them, the nonlinear space-time beamformer includes: nonlinear transformation and linear weighting. Figure 4 As shown, first, the received signal x of the satellite communication antenna can be 0(n) (i.e., satellite desired signal), the received signal x of multiple sampling antenna units 1(n) , …, x N(n) (i.e., suppression interference, deceptive interference, and Gaussian additive white noise) are respectively input into corresponding bandpass filters to obtain filtered satellite desired signals d(n) and multiple filtered received signals v(n) of sampling antenna units. Secondly, the multiple filtered received signals v(n) of sampling antenna units can be input into nonlinear transformation to obtain multiple nonlinear transformed signals u(n). Then, the multiple nonlinear transformed signals u(n) can be linearly weighted to obtain weighted signals y(n). Then, the filtered satellite desired signal d(n) and the weighted signal y(n) can be synthesized to obtain a synthesized signal e(n). Finally, the multiple nonlinear transformed signals u(n), the weighted signal y(n), and the synthesized signal e(n) can be input into an adaptive filtering algorithm to obtain a cancellation output signal.

[0095] The invention realizes a satellite navigation communication anti-interference technology based on the Cyclic-MUSIC algorithm of the non-zero cyclic frequency of the satellite signal.

[0096] First, in order to achieve the purpose of ensuring the anti-narrowband performance of the satellite navigation system in a complex electromagnetic environment, the technical solution of the present invention is as follows:

[0097] 1) Signal processing technology: Adopt filtering technology based on time domain and frequency domain to effectively suppress narrowband interference. The time domain filter adopts adaptive transverse FIR filter, and the frequency domain filter adopts adaptive filter based on FFT transform.

[0098] 2) Intelligent algorithm technology: The introduction of intelligent algorithms such as neural networks, genetic algorithms, and fuzzy logic can adaptively counteract narrowband interference signals and improve the ability to weaken interference signals.

[0099] 3) Anti-interference algorithm: Cyclic-MUSIC algorithm based on the non-zero cyclic frequency of satellite signals improves the system's anti-interference ability in complex electromagnetic environments.

[0100] Second, how to break through the conventional polarization domain conversion, array null filtering, and radio frequency electromagnetic protection technology to improve the system positioning timing and anti-spoofing performance. The technical solution of the present invention is as follows:

[0101] 1) Array RF technology: Utilize the beamforming capability of the array antenna to enhance the directivity of the desired signal by adjusting the phase and amplitude of each antenna unit, while suppressing interference signals; utilize array null filtering technology to suppress interference by forming a signal zero point in the interference direction while maintaining the ability to receive useful signals.

[0102] 2) Polarization domain conversion technology: Mining signal features from multiple fields such as time domain, frequency domain, time-frequency domain, and polarization domain to construct a high-dimensional feature space, and improving small target detection performance through feature fusion technology; applying polarization domain conversion technology, utilizing the difference between the target and the interference signal in the polarization domain, designing a highly efficient polarization filter to distinguish and suppress the interference signal.

[0103] 3) Artificial intelligence fusion: Combining digital signal processing technology and artificial intelligence algorithms to intelligently identify and suppress interference signals and improve the system's anti-interference ability.

[0104] Third, in order to realize the extremely fast and accurate situational awareness and cancellation function of unknown interference signals, the technical solution of the present invention is as follows:

[0105] 1) Adaptive filtering technology: Use the least mean square (LMS) algorithm or recursive least squares (RLS) method to adjust the filter coefficients in real time to eliminate interference signals.

[0106] 2) Interference monitoring and identification technology: Establish satellite navigation interference monitoring technology to promptly detect interference sources and take corresponding countermeasures to monitor signal parameters such as signal absolute power / power change rate, signal quality, C / N0, etc.

[0107] 3) RF interference cancellation technology: Research and apply RF interference cancellation technology to suppress interference signals by constructing RF signals with the same amplitude and opposite phase as the interference signals for synthetic cancellation.

[0108] Fourth, in order to improve the anti-interference tolerance of the Beidou satellite navigation system and ensure the reliable operation of the system, the technical solution of the present invention is as follows:

[0109] 1) Enhance signal power and optimize signal structure: By increasing the power of satellite transmission signals, the signal has a stronger signal strength near the enemy interference source, thereby resisting suppressive interference. At the same time, more complex modulation methods and signal processing technologies are used to optimize the structure and coding of the signal, improve the signal's anti-interference ability, and make it more difficult to be interfered and destroyed during transmission.

[0110] 2) Use spot beam technology: Use spot beam or shaped beam technology to concentrate signal energy in a specific area, improve the signal-to-interference ratio, and enhance the signal's anti-interference ability. Spot beam technology can make the signal energy more concentrated and reduce interference to other areas, thereby improving signal quality and system stability.

[0111] 3) Upgraded anti-interference algorithms: Use advanced anti-interference algorithms such as adaptive filtering and space-time adaptive processing (STAP) to identify and suppress interference signals. These algorithms can analyze the signal environment in real time, automatically adjust parameters to minimize the impact of interference, and improve the system's anti-interference performance.

[0112] The present invention is based on the anti-interference technology of the Cyclic-MUSIC algorithm of the non-zero cyclic frequency of the satellite signal, and the specific implementation process is as follows:

[0113] 1) Inputting the satellite communication antenna receiving signal and the sampling antenna unit receiving signal into a bandpass filter to filter out the interference signal and noise out of the communication signal band, and obtaining the filtered satellite communication antenna receiving signal d and the filtered sampling antenna unit receiving signal to filter out the interference and noise signal out of the communication signal band;

[0114] 2) Inputting the filtered sampling antenna unit receiving signal into a nonlinear space-time beamformer to implement nonlinear transformation and obtain a nonlinear transformed sampling signal;

[0115] 3) Performing linear weighted synthesis on the sampled signal after nonlinear transformation to obtain an interference cancellation signal;

[0116] 4) Synthesize the interference cancellation signal with the filtered satellite communication antenna receiving signal to achieve interference suppression;

[0117] 5) Adopt adaptive filtering algorithm to iteratively adjust the weighted value of the sampled signal to minimize the average power of the cancellation output signal.

[0118] Compared with the existing technology, this solution has the following advantages:

[0119] 1. Strong adaptability to electromagnetic environment: Aiming at the complex and changeable electromagnetic environment in which the Beidou satellite communication and navigation system is located, the Cyclic-MUSIC algorithm based on the non-zero cyclic frequency of satellite signals is adopted, breaking through key technologies such as polarization domain conversion, array null filtering, and radio frequency electromagnetic cancellation, which significantly improves the stability and reliability of the system in complex electromagnetic environments.

[0120] 2. Technological innovation: The Cyclic-MUSIC algorithm based on the non-zero cyclic frequency of satellite signals has achieved breakthroughs in key technologies such as non-zero cyclic frequency conversion, polarization domain conversion, array null filtering and RF electromagnetic cancellation, thereby improving the system's anti-interference capability.

[0121] 3. Excellent anti-interference performance: The anti-interference tolerance is improved by more than 27dB, and interference waveform detection and AI cancellation are completed within 20ms, which proves the Beidou system's rapid response and processing capabilities when facing interference.

[0122] According to an embodiment of the present invention, an embodiment of a communication anti-interference device for a navigation system is provided. It should be noted that the device can be used to execute the communication anti-interference method for the navigation system mentioned above. Figure 5 is a schematic diagram of a communication anti-interference device of a navigation system according to an embodiment of the present invention. Figure 5 As shown, the device includes: an acquisition module 52, which is used to acquire a first signal received by a satellite communication antenna of a navigation system, and a second signal received by a plurality of sampling antenna units in a preset antenna array; a filtering module 54, which is used to perform bandpass filtering on the first signal and the plurality of second signals to obtain a filtered first signal and a plurality of filtered second signals; a processing module 56, which is used to perform nonlinear transformation processing on the plurality of filtered second signals to obtain an interference cancellation signal; and a synthesis module 58, which is used to perform signal synthesis on the filtered first signal and the interference cancellation signal to obtain a cancellation output signal, wherein the average power of the cancellation output signal is less than or equal to the preset power.

[0123] Optionally, the processing module includes: a processing unit, used to perform nonlinear transformation processing on each filtered second signal to obtain a transformed second signal; and a synthesis unit, used to perform linear weighted synthesis on multiple transformed second signals to obtain an interference cancellation signal.

[0124] Optionally, the synthesis unit includes: a determination subunit, used to determine the initial weighted value of each transformed second signal; an adjustment subunit, used to adjust the initial weighted value through an adaptive filtering algorithm to obtain a target weighted value of each transformed second signal; and a synthesis subunit, used to perform linear weighted synthesis of multiple transformed second signals based on the target weighted value to obtain an interference cancellation signal.

[0125] Optionally, the adjustment subunit is also used to: estimate the signal source direction of the navigation system using a cyclic multi-signal classification algorithm; adjust the initial weighted value based on the signal source direction to obtain an adjusted weighted value; linearly weighted synthesize multiple transformed second signals based on the adjusted weighted value to obtain a synthesized signal; in response to the average power of the synthesized signal being greater than a preset power, use the adjusted weighted value as the initial weighted value, and repeat the steps of adjusting the initial weighted value based on the signal source direction to obtain an adjusted weighted value, and linearly weighted synthesizing multiple transformed second signals based on the adjusted weighted value to obtain a synthesized signal, until the average power of the synthesized signal is less than or equal to the preset power; in response to the average power of the synthesized signal being less than or equal to the preset power, determine the adjusted weighted value as the target weighted value.

[0126] Optionally, the adjustment subunit is also used to: establish an array reception model of the navigation system, wherein the array reception model is used to characterize signals corresponding to multiple signal sources, and the multiple signal sources include: satellite expected signals, suppressed interference, deceptive interference, and Gaussian additive white noise; based on the non-zero cyclic frequency of the navigation system, determine the cyclic autocorrelation matrix of the first signal; perform singular value decomposition on the cyclic autocorrelation matrix to obtain a signal subspace and a noise subspace; based on the signal subspace and the noise subspace, construct a spatial spectrum expression; and determine the direction of the signal source based on the spatial spectrum expression.

[0127] Optionally, the filtering module includes: a first filtering unit, used to filter the first signal using a time domain filter and a frequency filter to obtain a filtered first signal; a second filtering unit, used to filter each second signal using a time domain filter and a frequency filter to obtain a filtered second signal.

[0128] An embodiment of the present application further provides an electronic device, comprising: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods in various embodiments of the present invention when running.

[0129] An embodiment of the present application further provides a computer-readable storage medium, which includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the methods in various embodiments of the present invention.

[0130] An embodiment of the present application further provides a computer program product, including a computer program, which implements the methods in various embodiments of the present invention when executed by a processor.

[0131] An embodiment of the present application further provides a computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium is used to store a computer program, and when the computer program is executed by a processor, the method in each embodiment of the present invention is implemented.

[0132] The embodiments of the present application further provide a computer program, which implements the methods in the above-mentioned embodiments of the present invention when executed by a processor.

[0133] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0134] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0135] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0136] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0137] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0138] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A communication anti-interference method for a navigation system, characterized in that: include: Acquire a first signal received by a satellite communication antenna of a navigation system and a second signal received by a plurality of sampling antenna units in a preset antenna array; Performing bandpass filtering on the first signal and the plurality of second signals to obtain a filtered first signal and a plurality of filtered second signals; Performing nonlinear transformation processing on the plurality of filtered second signals to obtain an interference cancellation signal; The filtered first signal and the interference cancellation signal are synthesized to obtain a cancellation output signal, wherein an average power of the cancellation output signal is less than or equal to a preset power.

2. The method according to claim 1, characterized in that: Performing nonlinear transformation processing on the plurality of filtered second signals to obtain interference cancellation signals includes: Performing nonlinear transformation processing on each filtered second signal to obtain a transformed second signal; A plurality of transformed second signals are subjected to linear weighted synthesis to obtain the interference cancellation signal.

3. The method according to claim 2, characterized in that Performing linear weighted synthesis on the plurality of transformed second signals to obtain the interference cancellation signal includes: determining an initial weighting value for each transformed second signal; The initial weighted value is adjusted by an adaptive filtering algorithm to obtain a target weighted value of each transformed second signal; The plurality of transformed second signals are linearly weighted and synthesized based on the target weighted value to obtain the interference cancellation signal.

4. The method according to claim 3, characterized in that The initial weighted value is adjusted by an adaptive filtering algorithm to obtain a target weighted value of each transformed second signal, including: estimating the signal source direction of the navigation system using a cyclic multi-signal classification algorithm; Adjusting the initial weighted value based on the signal source direction to obtain an adjusted weighted value; Performing linear weighted synthesis on the plurality of transformed second signals based on the adjusted weighted values ​​to obtain a synthesized signal; In response to the average power of the synthesized signal being greater than the preset power, taking the adjusted weighted value as the initial weighted value, and repeatedly performing the steps of adjusting the initial weighted value based on the signal source direction to obtain the adjusted weighted value, and performing linear weighted synthesis on the plurality of transformed second signals based on the adjusted weighted value to obtain the synthesized signal, until the average power of the synthesized signal is less than or equal to the preset power; In response to the average power of the composite signal being less than or equal to the preset power, the adjusted weighted value is determined to be the target weighted value.

5. The method according to claim 4, characterized in that The signal source direction of the navigation system is estimated by using a cyclic multi-signal classification algorithm, including: Establishing an array receiving model of the navigation system, wherein the array receiving model is used to characterize signals corresponding to multiple signal sources, wherein the multiple signal sources include: satellite desired signals, suppression interference, deceptive interference, and Gaussian additive white noise; determining a cyclic autocorrelation matrix of the first signal based on a non-zero cyclic frequency of the navigation system; Performing singular value decomposition on the circulant autocorrelation matrix to obtain a signal subspace and a noise subspace; Constructing a spatial spectrum expression based on the signal subspace and the noise subspace; The signal source direction is determined based on the spatial spectrum expression.

6. The method according to claim 1, characterized in that Bandpass filtering the first signal and the plurality of second signals to obtain a filtered first signal and a plurality of filtered second signals comprises: Filtering the first signal using a time domain filter and a frequency filter to obtain the filtered first signal; Each second signal is filtered using the time domain filter and the frequency filter to obtain a filtered second signal.

7. A communication anti-interference device for a navigation system, characterized in that: include: An acquisition module, used to acquire a first signal received by a satellite communication antenna of a navigation system and a second signal received by a plurality of sampling antenna units in a preset antenna array; A filtering module, configured to perform bandpass filtering on the first signal and the plurality of second signals to obtain a filtered first signal and a plurality of filtered second signals; A processing module, configured to perform nonlinear transformation processing on the plurality of filtered second signals to obtain an interference cancellation signal; A synthesis module is used to perform signal synthesis on the filtered first signal and the interference cancellation signal to obtain a cancellation output signal, wherein the average power of the cancellation output signal is less than or equal to a preset power.

8. An electronic device, characterized in that: include: A memory storing an executable program; A processor, configured to run the program, wherein the program executes the method according to any one of claims 1 to 6 when running.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored executable program, wherein when the executable program is executed, the device where the storage medium is located is controlled to execute the method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The invention comprises a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 6.

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