Frequency division multiplexing multi-beam anti-jamming method for shaped antenna

Abstract

The application provides a frequency division multiplexing multi-beam anti-interference method for a shaped antenna, wherein each frequency band signal data is obtained through a filter corresponding to a frequency domain of each beam operating frequency band according to multi-beam space data received by the shaped antenna; a spatial spectrum estimation of high resolution is performed on each frequency band signal data by using a MUSIC algorithm to obtain a spatial direction of an incoming wave of an interference signal; adaptive weights of each same-frequency beam are obtained through a two-dimensional joint processing mode of a space domain and a frequency domain based on a preset multi-beam zeroing anti-interference strategy according to the spatial direction of the incoming wave of the interference signal; and the adaptive weights of each same-frequency beam are combined and processed to obtain a comprehensive directional diagram and a multi-beam interference zeroing result, so that the full space domain is covered by beams of different frequency points to realize joint space-frequency suppression of space domain complex interference.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of signal processing, and particularly relates to a frequency division multiplexing multi-beam anti-interference method for a shaped antenna. BACKGROUND

[0002] A shaped antenna is an antenna with a special shape and structure, used for receiving and transmitting radio wave signals. Its beam shape can be designed according to specific application requirements to improve the efficiency of signal reception and transmission. In the field of communication, the application of multi-beam technology in shaped antennas is an important research direction. In scenarios with high requirements for communication reliability and security, the application of multi-beam technology in shaped antennas can form multiple beams to cover different areas. In the face of the increasingly complex electromagnetic environment, communication satellites are required to have certain anti-interference ability, and by dynamically adjusting the response of the receiving and transmitting shaped antenna, the flexibility of command and dispatch can be improved. The application of multi-beam anti-interference technology in shaped antennas is the focus of current research, which is how to quickly and effectively select anti-interference strategies and take corresponding measures to form nulls in the interference incident angle to adapt to the high dynamic change of the battlefield environment in the background of complex electromagnetic environment and interference.

[0003] The current research on shaped antenna is mainly the traditional anti-jamming method of single-beam. The main methods include the least squares criterion, linearly constrained minimum mean square error criterion and maximum signal-to-interference noise ratio criterion. Wu Meicheng proposed an improved adaptive multi-beam anti-jamming algorithm based on differential constraint based on the linearly constrained minimum variance criterion (Wu Meicheng, Zhao Yan, Wang Juan, et al. An improved adaptive multi-beam anti-jamming algorithm based on differential constraint[J]. Firepower and command control, 2024, 49(4): 51-59, 70.). The paper 'Multi-beam anti-jamming processing method of deep coupling and space-time-frequency combination' (Ren Qi, Wan Xiang, Zhao Lu, et al. Multi-beam anti-jamming processing method of deep coupling and space-time-frequency combination[J]. Electronic design engineering, 2023, 31(19): 6-9, 14.) proposed an adaptive space-time-frequency anti-jamming method based on satellite broadband signal, which can improve the anti-jamming performance of the array for each frequency point in the broadband. Zheng Yaping proposed a dynamic pointing multi-beam anti-jamming method based on the minimum variance distortionless criterion, which obtains the prior information of the satellite signal direction, extracts the cluster center of the signal direction by using the K. means clustering algorithm, and uses the center as the constraint direction of beam forming to ensure that each satellite has a high output SINR and improve the overall anti-jamming performance. (Zheng Yaping, Zhao Lulu, Gong Wenbin, et al. Satellite navigation signal multi-beam anti-jamming method based on dynamic clustering[J]. Global positioning system, 2021, 46(2): 32-36.) The paper 'A fixed multi-beam anti-jamming method for satellite navigation receiver' (Ma Yanshu, Ma Zhongzhi, Li Xiaodong, et al. A fixed multi-beam anti-jamming method for satellite navigation receiver[J]. Navigation, positioning and timing, 2020, 7(1): 104-112.) proposed a fixed multi-beam forming anti-jamming method for satellite navigation antenna array, gave the engineering implementation architecture, analyzed the optimal beam space allocation scheme and satellite screening positioning strategy, and verified the performance through simulation test.

[0004] The traditional multi-beam anti-jamming technology has many limitations in the face of interference, and can only deal with simple and single-type interference. In the face of complex electromagnetic environment, different types of interference lack effective anti-jamming strategy selection and cannot play a good role. For example, there is no research on eliminating complex interference under the background of shaped antenna multi-beam. The existing anti-jamming algorithm only targets single-type interference, and single interference is different for different beams in the background of multi-beam, so a method cannot effectively realize multi-beam anti-jamming; for interference of different frequency points, the existing method needs to divide it into multiple narrowband signals, which not only increases the calculation complexity, but also greatly reduces the anti-jamming effect of different frequency points. SUMMARY

[0005] In order to solve the above problems in the prior art, the application provides a frequency division multiplexing multi-beam anti-interference method for a shaped antenna, and specifically comprises the following steps.

[0006] In a first aspect, the application provides a frequency division multiplexing multi-beam anti-interference method for a shaped antenna, comprising the following steps.

[0007] S1, obtaining frequency band signal data according to multi-beam space data received by the shaped antenna and a filter corresponding to a frequency domain of a working frequency band of each beam, wherein the filter corresponding to the frequency domain of the working frequency band of each beam is a frequency domain filter with a bandwidth of the working frequency band of the beam;

[0008] S2, performing high-resolution spatial spectrum estimation on the frequency band signal data by using a MUSIC algorithm to obtain a spatial direction of arrival of an interference signal;

[0009] S3, obtaining adaptive weights of each same-frequency beam based on a preset multi-beam zeroing anti-interference strategy according to the spatial direction of arrival of the interference signal;

[0010] S4, performing merging processing on the adaptive weights of each same-frequency beam to obtain a comprehensive directional diagram and obtain a multi-beam interference zeroing result.

[0011] In a second aspect, the application further provides a frequency division multiplexing multi-beam anti-interference device for a shaped antenna, comprising the following steps.

[0012] The acquisition module is configured to obtain frequency band signal data according to multi-beam space data received by the shaped antenna and a filter corresponding to a frequency domain of a working frequency band of each beam, wherein the filter corresponding to the frequency domain of the working frequency band of each beam is a frequency domain filter with a bandwidth of the working frequency band of the beam;

[0013] The processing module is configured to perform high-resolution spatial spectrum estimation on the frequency band signal data by using a MUSIC algorithm to obtain a spatial direction of arrival of a target signal and an interference signal, obtain adaptive weights of each same-frequency beam based on a preset multi-beam zeroing anti-interference strategy according to the spatial direction of arrival of the interference signal, and perform merging processing on the adaptive weights of each same-frequency beam to obtain a comprehensive directional diagram and obtain a multi-beam interference zeroing result.

[0014] In a third aspect, the application further provides an electronic device, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete communication with each other through the communication bus.

[0015] The memory is configured to store a computer program.

[0016] The processor is configured to execute the program stored on the memory to implement any method provided in the first aspect.

[0017] The application has the following beneficial effects:

[0018] The application provides a frequency division multiplexing multi-beam anti-interference method for a shaped antenna.

[0019] The application will be further described in detail below in combination with the drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 A flowchart of the frequency division multiplexing multi-beam anti-interference method for the shaped antenna is provided.

[0021] Figure 2 A structural diagram of the frequency division multiplexing multi-beam anti-interference device for the shaped antenna is provided.

[0022] Figures 3-25 A series of simulation effect diagrams are provided. DETAILED DESCRIPTION

[0023] The application will be further described in detail below in combination with the drawings and examples.

[0024] In a complex electromagnetic environment, different interferences have different directions of arrival and different frequency bandwidths. In order to suppress the influence of the interferences on the system and solve the problems in the prior art, the application provides a frequency division multiplexing multi-beam anti-interference method for a shaped antenna.

[0025] Figure 1 A flowchart of the frequency division multiplexing multi-beam anti-interference method for the shaped antenna is provided, as shown in Figure 1 The method comprises the following steps.

[0026] S1, obtaining frequency band signal data according to multi-beam space data received by the shaped antenna and a filter corresponding to a frequency domain of a working frequency band of each beam.

[0027] The filter of the frequency domain corresponding to the beam operating frequency band is a frequency domain filter with a bandwidth of the beam operating frequency band.

[0028] That is, based on the special demand signal model, the spatial data received by the shaped antenna is filtered through the filter of each beam operating frequency band to obtain signal data of each frequency band.

[0029] In a possible implementation, the step S1 includes: transforming the multi-beam spatial data received by the shaped antenna from the time domain to the frequency domain through a fast Fourier transform to obtain frequency domain spatial data; filtering the frequency domain spatial data through the filter of the frequency domain corresponding to each beam operating frequency band to obtain a plurality of groups of narrowband frequency domain signals; and transforming the plurality of groups of narrowband frequency domain signals from the frequency domain to the time domain through an inverse Fourier transform to obtain signal data of each frequency band.

[0030] The signal data of each frequency band is expressed as:

[0031] X(f, t) = A(f, θ)S(f, t) + N(t),

[0032] wherein N(t) ∈ M × N represents a zero-mean Gaussian white noise, N represents the number of time shots; S(f, t) represents a signal vector; A(f, θ) represents a matrix composed of steering vectors of interference signals, f represents a center frequency point of the signal, t represents time, and θ represents an angle in the spatial domain.

[0033] Specifically, in array signal processing, it is generally assumed that the signal received by the array antenna is a narrowband signal and an ideal far-field source. The signal is a four-dimensional function of time, frequency, and space, and its complex form is:

[0034]

[0035] wherein k is a beam vector, t is a time variable, r is a position coordinate vector, A represents a signal amplitude, e represents a constant, f represents a signal frequency, and T represents a transpose, r the position coordinate vector is represented as: k the beam vector is represented as.

[0036] Because the distance between the array element and the signal source is very far, and the signal source is a narrowband signal, the signals reaching each array element have approximately the same complex envelope, only the phase is different.

[0037] For an equidistant linear array, there are M antenna elements, the first element (located at the origin of the rectangular coordinate system) is selected as the reference point, and the signal expression reaching each element after spatial propagation is:

[0038]

[0039] where a is the slow vector, w denotes the angular frequency, and the phase difference between the array element and the reference element is

[0040]

[0041] The signal is then represented as

[0042]

[0043] The signal received by each array element is therefore

[0044]

[0045] The vector form of the array received signal is

[0046]

[0047] where a(θ) is called the direction vector or steering vector, i.e.

[0048]

[0049] It can be seen from the formula that under the condition of a narrowband signal, the steering vector is only related to the geometric position of the array element and the propagation direction of the signal, where the time delay of the mth array element is: m = 2π / λd(m-1)sinθ.

[0050] The time delay τ m is related to the geometric position of the array element and the propagation direction of the signal, and the time delay τ m of the signal arriving at each array element is:

[0051]

[0052] where θ is the spatial azimuth angle, is the spatial elevation angle, and (x m , y m , z m ) is the geometric position of the array element. For a linear array, it is only related to the spatial azimuth angle θ. For a planar array, it is related to both θ and

[0053] Suppose there are K signals incident to the antenna array, and the signal model is represented as:

[0054] X(f,t) = A(f,θ)S(f,t) + N(t),

[0055] ​Wherein, N(t)∈M×N represents zero-mean Gaussian white noise, M represents time array element number, N represents time snapshot number, S(f, t) represents signal vector, A(f, θ) represents matrix composed of steering vector, mainly influenced by array geometry, signal frequency, array element coupling degree (in the application, coupling effect is considered to be ignored under ideal condition), etc., f represents signal center frequency point, t represents time, and θ represents angle in space.

[0056] The space domain anti-interference technology is to make weighted summation processing to all array element outputs, reduce gain at interference space direction by steering vector at the same time, and suppress interference output as follows:

[0057] y(f, t) = w H X(f, t) = S(f, t)w H A(f, θ),

[0058] Wherein, P(f, θ) = w H A(f, θ) is called directivity diagram, i.e. array directivity diagram, y(f, t) represents array output, and w represents weighting to all array elements.

[0059] In the application, the multi-beam shaped antenna is a frequency division multiplexing anti-interference system, which can suppress narrowband interference and wideband interference in space domain.

[0060] The narrowband interference is interference with much smaller frequency band than spread spectrum signal bandwidth, which means that power spectrum is concentrated in a small frequency band near a center frequency, and the frequency band is much smaller than the center frequency. The frequency band is relatively narrow compared with spread spectrum bandwidth, and the frequency band only occupies a small part of spread spectrum transmission frequency band. The wideband interference refers to interference with larger ratio of bandwidth to transmission signal bandwidth or larger absolute bandwidth. It covers multiple frequency bands, so compared with narrowband interference, it has more extensive and complex influence on signal processing system.

[0061] The signal obtained by the shaped antenna is transformed from time domain to frequency domain, and the signal spectrum diagram is obtained by fast Fourier transform. In order to realize the purpose of frequency division multiplexing of the shaped antenna, with the help of spectrum characteristics, the signal is filtered by different bandwidth frequency domain filters, the spectrum is segmented, the narrowband frequency domain signal is still narrowband frequency domain signal after filtering, and the wideband frequency domain signal becomes multiple narrowband frequency domain signals after filtering. Finally, the signal is transformed from frequency domain to time domain by inverse Fourier transform, and the signal data of each frequency band is obtained, i.e. time domain signal. The multi-beam directivity diagram of the shaped antenna is zeroed to realize anti-interference.

[0062] The frequency division multiplexing multi-beam anti-interference algorithm for the shaped antenna provided by the application can enhance the ability of the system to suppress complex interference from multiple dimensions through the frequency domain filtering mode, and realize the space-frequency joint anti-interference of the frequency division multiplexing multi-beam of the shaped antenna.

[0063] The multi-beam of the shaped antenna covers the whole space with beams of different frequency bandwidths according to the frequency bandwidth requirement of the operation, which can not only improve the multi-target detection and tracking ability of the system in the space from the time point of view, but also effectively identify and eliminate the influence of interference of different bandwidths from the frequency domain point of view.

[0064] S2, using the MUSIC algorithm, performing high-resolution spatial spectrum estimation on the signal data of each frequency band to obtain the spatial direction of arrival of the target signal and the interference signal.

[0065] In the signal direction of arrival estimation algorithm (DOA) based on the eigenvalue decomposition of the antenna array covariance matrix, the MUSIC algorithm has universal applicability, as long as the array form of the antenna array is known, whether it is a linear array or a surface array, whether the array elements are equally spaced or not, a high-resolution estimation result can be obtained. The array covariance matrix R x A high-resolution estimation result can be obtained. The array covariance matrix R x can be divided into two spaces, namely:

[0066]

[0067] Further

[0068]

[0069] Wherein, U S is a subspace composed of the larger (number of signal sources) eigenvalues of all eigenvalues of R, called signal subspace, Σ S is a diagonal matrix composed of large eigenvalues; U N is a subspace composed of the smaller (number of array elements-number of signal sources) eigenvalues of all eigenvalues of R, called noise subspace, Σ N is a diagonal matrix composed of small eigenvalues.

[0070] For example, if the number of signals is 1, the MxM-dimensional matrix R x is eigenvalue decomposed, M eigenvalues and corresponding eigenvectors can be obtained, Σ S represents a diagonal matrix composed of larger eigenvalues, U S represents the eigenvectors corresponding to the larger eigenvalues, and represents the signal subspace; Σ N represents a diagonal matrix composed of smaller eigenvalues, U Ndenotes the eigenvector corresponding to the smaller eigenvalue, and denotes the noise subspace.

[0071] According to the above formula, we have

[0072] A(f, θ)RA H (f, θ)U N = 0,

[0073] The matrix R is a full rank matrix, and is non-singular, so there is an inverse.

[0074] Thus the above formula can be changed to A H (f, θ)U N = 0, which shows that each column vector in the matrix A(f, θ) is orthogonal to the noise subspace, so we have:

[0075]

[0076] According to the orthogonal relationship between the noise eigenvector and the signal vector, we obtain the array spatial spectrum function

[0077]

[0078] Correspondingly, in a possible implementation manner, the step S2 includes the following steps S21-S24:

[0079] S21, according to each frequency band signal data X(f, t) received by the conformal antenna, the estimated covariance matrix is obtained, denoted as:

[0080]

[0081] Wherein, R represents the covariance matrix, N represents the number of time snapshots, n represents the time snapshot index, the superscript H represents the conjugate transpose of the matrix, and X(f, n) represents the n-point sampling value of X(f, t).

[0082] S22, the eigenvalue decomposition is performed on the covariance matrix R, to obtain the eigenvalue, denoted as:

[0083] R = UΣU H ,

[0084] Wherein, U represents the matrix composed of the eigenvectors after the eigenvalue decomposition of R, and Σ represents the diagonal matrix composed of the eigenvalues after the eigenvalue decomposition of R.

[0085] S23, according to the size order of the eigenvalue, the eigenvectors corresponding to the maximum eigenvalues with equal number of signals are determined as the signal subspace, and the eigenvectors corresponding to the remaining eigenvalues are determined as the noise subspace, denoted as:

[0086]

[0087] Wherein, U Sdenotes the signal subspace, i.e., the subspace composed of the eigenvectors corresponding to the larger eigenvalues of R, Σ S denotes the diagonal matrix composed of the larger eigenvalues of R, U N denotes the noise subspace, i.e., the subspace composed of the eigenvectors corresponding to the smaller (number of array elements-number of signal sources) eigenvalues of R, Σ N denotes the diagonal matrix composed of the smaller eigenvalues of R.

[0088] S24, determining the MUSIC spectrum function according to the noise subspace and, determining the spatial direction of the incoming wave of the interference signal according to the peak value of the MUSIC spectrum function, denoted as:

[0089]

[0090] P MCSIC denotes the MUSIC spectrum function, U N denotes the subspace composed of the eigenvectors corresponding to the smaller (number of array elements-number of signal sources) eigenvalues of R, called the noise subspace, a(f, θ) denotes the steering vector, θ denotes the angle, and f denotes the center frequency point of the signal.

[0091] The frequency division multiplexing multi-beam anti-interference algorithm for the shaped antenna provided by the application can effectively select a more suitable anti-interference strategy to achieve a deeper null and a lower sidelobe, and realize the optimal selection of the spatial anti-interference method of the shaped antenna, compared with the traditional spatial anti-interference method.

[0092] S3, obtaining the adaptive weight of each same-frequency beam based on the preset multi-beam zeroing anti-interference strategy according to the spatial direction of the incoming wave of the interference signal.

[0093] In a possible implementation manner, the step S3 comprises: adopting an OP adaptive anti-interference algorithm based on the interference subspace to zero the interference in the main lobe, and obtaining the adaptive beam weight against the main lobe interference.

[0094] Optionally, the OP adaptive anti-interference algorithm based on the interference subspace is adopted to zero the interference in the main lobe, and the adaptive beam weight against the main lobe interference is obtained, comprising the following steps a1-a5:

[0095] a1, performing eigenvalue decomposition on the covariance matrix to obtain a target eigenvalue, denoted as:

[0096]

[0097] wherein, λ i denotes the larger eigenvalue of R after eigenvalue decomposition, v i denotes the eigenvector of R after eigenvalue decomposition, and P denotes the number of interferences. represents a small eigenvalue of R after characteristic decomposition.

[0098] a2, arrange the target eigenvalues from large to small, and obtain a target eigenvector according to the arrangement result, denoted as:

[0099]

[0100] v1, v2,..., v P , P+1 ..., v N ,

[0101] wherein v1, v2,..., v P , P+1 ..., v N denotes the target eigenvector.

[0102] a3, determine a target interference subspace according to the eigenvalue size, denoted as:

[0103] U J = span{v1, v2,..., v P},

[0104] wherein U J denotes the target interference subspace, and span{} denotes a vector space, which is spanned by the vectors in the set {v1, v2,..., v P}.

[0105] a4, determine an orthogonal complement space of the interference according to the target interference subspace, denoted as:

[0106]

[0107] wherein denotes the orthogonal complement space of the interference, and I denotes a diagonal matrix with all 1s.

[0108] a5, determine an adaptive beam weight value for anti-main lobe interference according to the orthogonal complement space of the interference, denoted as:

[0109]

[0110] wherein w opt denotes the adaptive beam weight value (weight vector) for anti-main lobe interference, a(f, θ0) denotes a steering vector of a beam main lobe, and θ0 denotes a beam main lobe pointing direction.

[0111] In another possible implementation, the step S3 includes: using a least mean square error (LMS) algorithm to null the interference in the sidelobe, and obtaining an adaptive beam weight value for anti-sidelobe interference.

[0112] Optionally, the least mean square error LMS algorithm is used to null the interference in the sidelobe, and adaptive beam weight values are obtained to resist the sidelobe interference, which is expressed as:

[0113] e(f,t) = y(f,t) - d(f,t),

[0114]

[0115] w(k+1) = w(k) - 2μX(k)e * (k),

[0116] wherein y(f,t) represents the output after beam forming, d(f,t) represents the expected signal, δ represents the mean square error, E[·] represents the mathematical expectation, e(f,t) represents the error between the output y(f,t) after beam forming and the expected signal d(f,t), represents the gradient operator of w, represents the transient power error gradient, represents the partial derivative of w, w represents the weight of the array, w(k) represents the array weight of the kth iteration, μ represents the iteration step factor, R = E[X(f,t)X H (f,t)] represents the autocorrelation matrix of the input branches of the array, r Xd = E[d(f,t)X H (f,t)] represents the cross-correlation matrix of X(f,t) and d(f,t), and the superscript * represents the conjugate, and e(k) represents the error between the array output y(f,t) after the kth iteration of beam forming and the expected signal d(f,t).

[0117] Specifically, the least mean square error (LMS) algorithm is widely used in the field of signal processing due to its small amount of calculation and easy implementation, and the LMS algorithm can be applied to realize adaptive beam forming in the field of array signal processing. The LMS algorithm needs to generate a reference signal according to the prior knowledge of the useful signal, uses the minimum mean square error (MMSE) as the optimization criterion, adjusts the weight value by using the error between the reference signal and the output after beam weighting, realizes spatial filtering, and strengthens or forms a notch to the wave coming from a specific direction. The minimum mean square error criterion (MMSE): In radar applications, the array covariance matrix usually contains the expected signal, and the criterion is proposed based on this condition. The mean square error between the array output and a certain expected response is minimized, and the direction of arrival of the expected signal does not need to be known.

[0118] Let the error between the expected signal d(t) and the output y(t) after beam forming be e(t), and the mean square error be δ, then:

[0119] e(f,t) = y(f,t) - d(f,t),

[0120] The mean square error δ is:

[0121]

[0122] where Re denotes taking the real part, where R = E[X(f,t)X H (f,t)] is the autocorrelation matrix of the input to each array element, r Xd = E[d(f,t)X H (f,t)] is the cross-correlation matrix of X(f,t) and d(f,t), and E[·] denotes the mathematical expectation.

[0123] Using the steepest descent method, an initial value w0 of w is assumed. When the weight is adjusted in the direction of the decrease of δ, the optimal weight vector w opt can be found. Because the negative gradient direction is the direction in which δ decreases most rapidly, the gradient of δ with respect to w is:

[0124]

[0125] Because the negative gradient direction is the direction in which δ decreases most rapidly, the recursive formula of the optimal weight vector can be obtained:

[0126]

[0127] In the formula, μ is an iteration step factor, which affects the convergence rate and stability of the iteration process.

[0128] The idea applied in the LMS algorithm in engineering practice is to replace the steady-state value with a transient value, i.e., to estimate the steady-state mean square error using the gradient of the transient output power error The gradient of the transient power error is:

[0129]

[0130] Using to replace the gradient and then sampling the numerical value, the recursive formula of the LMS algorithm is obtained:

[0131] w(k+1) = w(k) - 2μX(k)e * (k),

[0132] In the formula, k is the iteration number in the LMS algorithm.

[0133] As can be seen from the expression of the LMS algorithm, in the process of approaching the optimal weight vector from the transient weight vector, the new weight vector is obtained by subtracting the product of the output error value and the input signal vector from the weight vector of the last iteration. The instantaneous gradient value is used to replace the accurate statistical gradient value, which is an unbiased estimate.

[0134] In yet another possible implementation, step S3 comprises: employing a linearly constrained minimum variance (LCMV) criterion to null the interference within the main lobe and the side lobes to obtain the adaptive beam weight value against the joint interference of the main lobe and the side lobes.

[0135] Optionally, the linearly constrained minimum variance (LCMV) criterion is employed to null the interference within the main lobe and the side lobes to obtain the adaptive beam weight value against the joint interference of the main lobe and the side lobes, which is denoted as:

[0136]

[0137] w opt = R -1 C(C H R -1 C) -1 F,

[0138] wherein C = [a(f, θ0), a(f, θ1), …, a(f, θp-1)], a(f, θ0) represents a steering vector of the main lobe pointing, a(f, θi) represents an interference steering vector, i = 1, 2, …, p-1, i R represents a covariance matrix, and w opt represents the adaptive beam weight value against the joint interference of the main lobe and the side lobes.

[0139] The same-frequency beam of the shaped antenna eliminates the influence of the interference from the perspective of the frequency domain through the filter of the working frequency band of each beam for the main lobe interference and the side lobe interference of different frequencies; for the same-frequency interference, the influence of the interference cannot be eliminated from the perspective of the frequency domain, different anti-interference strategies are taken for different incident angles to eliminate the influence of the interference from the perspective of the space domain, and the interference of different frequency bands and different incident angles in a complex environment is effectively counteracted.

[0140] S4, the adaptive weight values of each same-frequency beam are combined to obtain a comprehensive directivity pattern, and a multi-beam interference nulling result is obtained.

[0141] Optionally, S4 is denoted as:

[0142] y(f, t) = w H X(f, t) = S(f, t)w H A(f, θ)

[0143] wherein P(f, θ) = W H A(f, θ) represents the comprehensive directivity pattern, w represents the adaptive beam weight value against the interference, A(f, θ) represents the array manifold vector of the signal, y(f, t) represents the signal after the interference is suppressed, X(f, t) represents the signal data of each frequency band, i.e., the signal received by the array, and S(f, t) represents the signal vector.

[0144] ​The application provides a frequency division multiplexing multi-beam interference suppression algorithm for a shaped antenna.

[0145] The application provides a frequency division multiplexing multi-beam interference suppression method for a shaped antenna, which comprises the following steps: obtaining frequency band signal data according to multi-beam space data received by the shaped antenna and a filter corresponding to a frequency domain of a working frequency band of each beam; the filter corresponding to the frequency domain of the working frequency band of each beam is a frequency domain filter with a bandwidth of the working frequency band of the beam; performing high-resolution spatial spectrum estimation on the frequency band signal data by using a MUSIC algorithm to obtain a spatial direction of an incoming wave of an interference signal; obtaining adaptive weights of each same-frequency beam based on a preset multi-beam nulling interference suppression strategy according to the spatial direction of the incoming wave of the interference signal; and performing merging processing on the adaptive weights of each same-frequency beam to obtain a comprehensive directional diagram and obtain a multi-beam interference nulling result.

[0146] Figure 2 A structure diagram of a frequency division multiplexing multi-beam interference suppression device for a shaped antenna is shown in the figure. Figure 2 The device comprises:

[0147] The acquisition module 21 is configured to obtain frequency band signal data according to multi-beam space data received by the shaped antenna and a filter corresponding to a frequency domain of a working frequency band of each beam; the filter corresponding to the frequency domain of the working frequency band of each beam is a frequency domain filter with a bandwidth of the working frequency band of the beam.

[0148] The processing module 22 is used to perform high-resolution spatial spectrum estimation on signal data of each frequency band using the MUSIC algorithm to obtain the spatial direction of arrival of the target signal and the interference signal; based on the spatial direction of arrival of the interference signal and the preset multi-beam nulling anti-interference strategy, it obtains the adaptive weights of each co-frequency beam; it merges the adaptive weights of each co-frequency beam to obtain a comprehensive radiation pattern and obtains the multi-beam interference nulling result.

[0149] To further demonstrate the beneficial effects of the present invention, a series of simulation experiments are also provided, as follows:

[0150] The airspace was divided into 52 regions according to different operating frequency bands, and each region was covered by a beam. For example... Figure 3 As shown, beams 1, 2, 3, and 4 form a group of beams with the same frequency; beams 5, 17, 30, 34, 38, and 42 form a group of beams with the same frequency; beams 6, 18, 29, 33, 37, and 41 form a group of beams with the same frequency; beams 7, 19, 32, 36, 40, and 44 form a group of beams with the same frequency; beams 8, 20, 31, 35, 39, and 43 form a group of beams with the same frequency; and beam 9... Beams 13, 22, 26, 46, and 50 form a group with the same frequency; beams 9, 13, 22, 26, 46, and 50 form a group with the same frequency; beams 10, 14, 21, 25, 45, and 49 form a group with the same frequency; beams 11, 15, 24, 28, 48, and 52 form a group with the same frequency; and beams 12, 16, 23, 27, 47, and 51 form a group with the same frequency.

[0151] Experiment 1:

[0152] Considering that different interference frequencies in the spatial domain have different bandwidths, spatial zeroing can be used to suppress interference with the same bandwidth as the beam, while frequency domain filtering is required for interference with different bandwidths. To verify that the array's anti-interference effect remains effective after the signal is converted to a frequency domain signal by Discrete Fourier Transform and then filtered in the frequency domain, a simulation experiment was set up. The first simulation used one type of interference, while this simulation uses two types of interference, a difference in approach.

[0153] Simulation Experiment Conditions: This simulation experiment uses co-frequency beams 11, 15, 24, 28, 48, and 52, with an operating frequency band of 1668MHz-1675MHz. The experimental signal parameters are set as follows: Interference 1 incident elevation angle is 30°, azimuth angle is -55°, u-domain incident angle is 0.2877°, v-domain incident angle is -0.4090°, center frequency is 1668MHz, interference 1 bandwidth is 7MdB, and interference-to-noise ratio is 30dB.

[0154] The simulation experiment result: the embodiment of the application constructs data according to the interference signal model, detects the incoming wave direction of the interference, then selects a suitable anti-interference strategy to zero, and finally synthesizes the directional diagram of the same frequency beam.

[0155] The spatial domain signal received by the shaped antenna, through the frequency spectrum diagram of fast Fourier transform, the frequency domain distribution of interference 1 in the frequency spectrum diagram can be seen, as shown in the following formula (1). Figure 4

[0156] The frequency spectrum diagram of the spatial domain signal received by the shaped antenna after filtering by the frequency domain filter with a bandwidth of 7MHz, as shown in the following formula (2). Figure 5

[0157] The MUSIC spectrum diagram obtained by the MUSIC algorithm direction finding after frequency domain filtering, as shown in the following formula (3). Figure 6

[0158] As can be seen from the following formula (4), in the MUSIC spectrum diagram, there is a spectrum peak at the coordinates of u=0.2877, v=-0.4090, and the direction finding result is accurate. Figure 6

[0159] The anti-main lobe interference directional diagram when the interference is incident in the beam with the beam sequence number 11, as shown in the following formula (5). Figure 7

[0160] The cross section diagram of the anti-main lobe interference directional diagram in the interference incident direction u domain and v domain, as shown in the following formula (6). Figure 8

[0161] As can be seen from the following formula (7), the directional diagram of the beam with the beam sequence number 11 produces a null of-45.6158dB in the incoming wave direction of interference 1. Figure 8

[0162] The anti-side lobe interference directional diagram when the interference is incident in the beam with the beam sequence number 15, as shown in the following formula (8). Figure 9

[0163] The cross section diagram of the anti-side lobe interference directional diagram in the interference incident direction u domain and v domain, as shown in the following formula (9). Figure 10

[0164] As can be seen from the following formula (10), the synthesized directional diagram produces a null of-46.0389dB in the incoming wave direction of interference 1. Figure 10

[0165] The same frequency beam synthesis directional diagram of the beams with the beam sequence numbers 11, 15, 24, 28, 28 and 32, as shown in the following formula (11). Figure 11

[0166] Experiment two:

[0167] ​​​​​​​​​​​Considering that there are no less than one interference in the space domain, the main lobe and sidelobe interference may occur at the same time for the co-frequency beam, which is not applicable to the anti-main lobe interference algorithm or the anti-sidelobe interference algorithm. In order to verify that the algorithm is still effective when there is main lobe interference and sidelobe interference at the same time, the number of interference is increased for simulation experiment. The number of interference in the simulation of one embodiment is 1, and the number of interference set in this simulation is 2.

[0168] Simulation experiment condition: the co-frequency beams 11, 15, 24, 28, 48 and 52 in this simulation experiment have a working frequency band of 1668-1675 MHz. The signal parameters set in the experiment are as follows: the incident elevation angle of interference 1 is 30°, the azimuth angle is -55°, the u-domain incident angle is 0.2877, the v-domain incident angle is -0.4090, the center frequency point is 1668 MHz, and the interference-to-noise ratio is 30 dB; the incident elevation angle of interference 2 is -30°, the azimuth angle is 40°, the u-domain incident angle is -0.3816, the v-domain incident angle is -0.3229, the center frequency point is 1670 MHz, and the interference-to-noise ratio is 30 dB.

[0169] Simulation experiment result: the application embodiment constructs data according to the interference signal model, detects the direction of arrival of the interference, selects a suitable anti-interference strategy for nulling, and finally synthesizes the directional diagram of the co-frequency beam.

[0170] The space domain signal received by the shaped antenna can be seen from the frequency spectrum diagram of the fast Fourier transform that interference 1 is distributed in the frequency domain in the frequency spectrum diagram, as shown in Figure 12 .

[0171] The frequency spectrum diagram of the space domain signal received by the shaped antenna after being filtered by a frequency domain filter with a bandwidth of 7 MHz, as shown in Figure 13 .

[0172] The MUSIC spectrum diagram obtained by the MUSIC algorithm after frequency domain filtering, as shown in Figure 14 .

[0173] As can be seen from Figure 14 , there is a spectrum peak in the MUSIC spectrum diagram at the coordinates of u=0.2877 and v=-0.4090, and there is a spectrum peak at the coordinates of u=-0.3816 and v=-0.3229, and the direction finding result is accurate.

[0174] The anti-main-sidelobe combined interference directional diagram of the interference incident in the beam with the beam serial number 11, as shown in Figure 15 .

[0175] The cross-sectional view of the main lobe interference in the u-domain and v-domain incident direction of the interference, as shown in Figure 16 .

[0176] As can be seen from Figure 16It can be seen that the radiation pattern of beam sequence number 11 produced a null of -44.8872dB in the direction of the incoming wave of interference 1.

[0177] Cross-sectional views of sidelobe interference in the u-domain and v-domain along the incident direction of interference, as shown below. Figure 17 As shown.

[0178] Depend on Figure 17 It can be seen that the radiation pattern of beam sequence number 11 produced a null of -70.3313dB in the direction of arrival of interference 2.

[0179] The beamforming patterns of co-frequency beams with spatial beam sequence numbers 11, 15, 24, 28, 28, and 32 are shown below. Figure 18 As shown.

[0180] Experiment 3:

[0181] Considering interference at different frequencies in the airspace, there may be interfering frequencies outside the operating frequency band for co-frequency beams. To verify that the algorithm can handle interference outside the operating frequency band, we now add interference outside the operating frequency band for simulation experiments. The frequencies simulated in Example 1 were 1668MHz and 1670MHz, while the interference frequencies set in this simulation are 1068MHz and 1668MHz, which is different.

[0182] Simulation Experiment Conditions: This simulation experiment uses co-frequency beams 11, 15, 24, 28, 48, and 52, with an operating frequency band of 1668MHz-1675MHz. The experimental signal parameters are set as follows: Interference 1: incident elevation angle 30°, azimuth angle -55°, u-domain incident angle 0.2877°, v-domain incident angle -0.4090°, center frequency 1068MHz, and interference-to-noise ratio (IRR) 30dB; Interference 2: incident elevation angle -30°, azimuth angle 40°, u-domain incident angle -0.3816°, v-domain incident angle -0.3229°, center frequency 1668MHz, and INR 30dB.

[0183] Simulation results: The invention embodiment constructs data based on the interference signal model, detects the direction of the incoming interference wave, selects an appropriate anti-interference strategy to zero it, and finally synthesizes the radiation pattern of the same frequency beam.

[0184] The spatial signal received by the shaped antenna, when analyzed using a Fast Fourier Transform (FFT) spectrum plot, reveals the frequency domain distribution of interference 1 within the spectrum plot, as shown below. Figure 19 As shown.

[0185] The spectrum of the spatial signal received by the shaped antenna, after being filtered by a frequency domain filter with a bandwidth of 7MHz, is as follows: Figure 20 As shown.

[0186] Depend onFigure 20 It can be seen that in the spectrum after the green bottle, there is a signal only within the operating frequency band, and the signals outside the operating frequency band have been filtered out.

[0187] The MUSIC spectrum obtained after frequency domain filtering and direction finding using the MUSIC algorithm, as shown below. Figure 21 As shown.

[0188] Depend on Figure 21 It can be seen that there is a spectral peak at the coordinates u = -0.3816, v = -0.3229 in the MUSIC spectrum, indicating that the direction finding result is accurate.

[0189] The interference pattern incident on beam number 11 in the spatial domain, with anti-main-sidelobe combined interference pattern, is as follows: Figure 22 As shown.

[0190] The cross-sectional views of the main lobe interference in the u-domain and v-domain along the incident direction of the interference are shown below. Figure 23 As shown.

[0191] Depend on Figure 23 It can be seen that the beam pattern with beam number 11 has a gain of 15.54dB in the direction of interference 1, and no null is generated.

[0192] Cross-sectional views of sidelobe interference in the u-domain and v-domain along the incident direction of interference, as shown below. Figure 24 As shown.

[0193] Depend on Figure 24 It can be seen that the radiation pattern of beam sequence number 11 produced a null of -75.3746dB in the direction of arrival of interference 2.

[0194] The beamforming patterns of co-frequency beams with spatial beam sequence numbers 11, 15, 24, 28, 28, and 32 are shown below. Figure 25 As shown.

[0195] The present invention also provides an electronic device structure, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus.

[0196] Memory, used to store computer programs;

[0197] When a processor executes a program stored in memory, it implements the steps provided in the above method embodiments.

[0198] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0199] The method provided by the embodiment of the present application can be applied to an electronic device. Specifically, the electronic device can be a desktop computer, a portable computer, a smart mobile terminal, a server, etc. Herein, no limitation is made, and any electronic device that can implement the present application falls within the protection scope of the present application.

[0200] The present application also provides a computer readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps provided in the above method embodiments are implemented.

[0201] For the device / electronic device / storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the specific contents, beneficial effects, and the like are described in the method embodiments.

[0202] The terms "first", "second", etc. are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise specifically limited.

[0203] The above is a further detailed description of the present application in combination with specific preferred embodiments, and the specific implementation of the present application cannot be limited to these descriptions. For ordinary skilled persons in the art to which the present application belongs, a number of simple deductions or replacements can be made without departing from the concept of the present application, and all of them should be regarded as falling within the protection scope of the present application.

Claims

1. A frequency division multiplexing multi-beam anti-jamming method for a conformal antenna, characterized in that, Comprise: S1, according to the filter of the corresponding frequency domain of the beam operating frequency band of the multi-beam space data received by the shaped antenna, obtain each frequency band signal data, the filter of the corresponding frequency domain of the beam operating frequency band is the frequency domain filter with the bandwidth of the beam operating frequency band;The S1 comprises: by fast Fourier transform, the multi-beam space data received by the shaped antenna is transformed from time domain to frequency domain, and frequency domain space data is obtained;The frequency domain space data is filtered through the filter of the corresponding frequency domain of each beam operating frequency band, and a plurality of narrowband frequency domain signals are obtained;The plurality of narrowband frequency domain signals are transformed from frequency domain to time domain by inverse Fourier transform, and the each frequency band signal data is obtained;The each frequency band signal data is expressed as: ， wherein denotes a zero-mean Gaussian white noise, denotes the number of time bins, denotes the number of time taps, denotes a signal vector, denotes a matrix of steering vectors, denotes a center frequency of the signal, denotes time, denotes an angle in space; S2, using MUSIC algorithm, high-resolution spatial spectrum estimation is carried out on the each frequency band signal data, and the incoming space direction of interference signal is obtained; S3, according to the incoming space direction of interference signal, based on the preset nulling anti-interference strategy of multi-beam, the adaptive weight of each same frequency beam is obtained;S3 includes: using OP adaptive anti-interference algorithm based on interference subspace, the interference in the main lobe is nulled, and the adaptive beam weight value resisting the main lobe interference is obtained;And / or, using least mean square error LMS algorithm, the interference in the side lobe is nulled, and the adaptive beam weight value resisting the side lobe interference is obtained;And / or, using linearly constrained minimum variance LCMV criterion, the interference in the main lobe and the side lobe is nulled, and the adaptive beam weight value resisting the joint interference of main lobe and side lobe is obtained; S4, the adaptive weight of each same frequency beam is processed, and the comprehensive directional diagram is obtained, and the multi-beam interference nulling result is obtained.

2. The method of claim 1, wherein, The S2 comprises: The signal data of each frequency band received by the array antenna The estimated covariance matrix is represented as: wherein denotes the covariance matrix, the superscript denotes the conjugate transpose of a matrix, denotes of point sample values; The eigenvalue decomposition is carried out on the covariance matrix, and the eigenvalue is obtained, expressed as: ， wherein denotes a matrix composed of eigenvectors after eigen decomposition, denotes a diagonal matrix composed of eigenvalues after eigen decomposition; According to the size order of the eigenvalue, the eigenvector corresponding to the maximum eigenvalue with equal signal number is determined as the signal subspace, and the eigenvector corresponding to the remaining eigenvalue is determined as the noise subspace, expressed as: ， wherein denotes the signal subspace, denotes the diagonal matrix of large eigenvalues, denotes the noise subspace, denotes the diagonal matrix of small eigenvalues; According to the orthogonal relationship between the noise subspace and the signal subspace, the MUSIC spectrum function is determined, and the incoming space direction of interference signal is determined according to the peak value of the MUSIC spectrum function, expressed as: ， denotes the MUSIC spectral function, denotes the angle, denotes the center frequency of the signal, denotes the steering vector.

3. The method of claim 2, wherein, The OP adaptive anti-interference algorithm based on interference subspace is used to null the interference in the main lobe, and the adaptive beam weight value resisting the main lobe interference is obtained, which comprises: The eigenvalue decomposition is carried out on the covariance matrix, and the target eigenvalue is obtained, expressed as: ， wherein, represents large eigenvalues after eigen decomposition, represents eigenvectors after eigen decomposition, represents the number of interference, represents small eigenvalues after eigen decomposition; The target eigenvalue is arranged from large to small, and the target eigenvector is obtained according to the arrangement result, expressed as: ， ， wherein, represents the target feature vector; According to the size of the eigenvalue, the target interference subspace is determined, expressed as: ， wherein, represents the target interference subspace, represents the vector space spanned by the set of vectors in the set According to the target interference subspace, the orthogonal complement space of interference is determined, expressed as: ， wherein denotes the orthogonal complement space of the interference, denotes a diagonal matrix with all ones; According to the orthogonal complement space of interference, the adaptive beam weight value resisting the main lobe interference is determined, expressed as: ， wherein, denotes an adaptive beam weight against main lobe interference, denotes a steering vector of a beam main lobe pointing, denotes a direction of a beam main lobe pointing.

4. The method of claim 2, wherein, The least mean square error LMS algorithm is used to null the interference in the side lobe, and the adaptive beam weight value resisting the side lobe interference is obtained, expressed as: ， ， ， ， ， ， wherein represents the beamformed output, represents the desired signal, represents the mean square error, represents the mathematical expectation, represents the beamformed output error with the desired signal , represents the gradient operator, represents the gradient of the transient power error, represents the partial derivative, represents the weighting of the array, represents the array weighting of the th iteration, represents the array weighting of the th iteration, represents the iteration step factor, represents the autocorrelation matrix of the input of each array element branch, represents the cross-correlation matrix of and , the superscript represents the conjugate, represents the array output after the th iteration of beamforming error with the desired signal .

5. The method of claim 2, wherein, The linearly constrained minimum variance LCMV criterion is used to null the interference in the main lobe and the side lobe, and the adaptive beam weight value resisting the joint interference of main lobe and side lobe is obtained, expressed as: ， ， wherein, , denotes a steering vector of the main lobe, denotes an interference steering vector, , denotes an adaptive beam weight against the main side lobe joint interference.

6. The method of claim 5, wherein, The S4 is expressed as: ， wherein, represents a comprehensive directional diagram, represents an anti-interference adaptive beam weight, represents an array manifold vector of a signal, represents a signal after interference suppression, represents each frequency band signal data, represents a signal vector.

7. A frequency division multiplexing multi-beam anti-jamming device for a conformal antenna, characterized in that, The method comprises the following steps: An acquisition module is configured to obtain frequency band signal data according to the multi-beam space data received by the shaped antenna and a filter corresponding to the frequency domain of each beam operating frequency band, wherein the filter corresponding to the frequency domain of each beam operating frequency band is a frequency domain filter with a bandwidth equal to the beam operating frequency band; and the acquisition module is specifically configured to: transform the multi-beam space data received by the shaped antenna from time domain to frequency domain by fast Fourier transform to obtain frequency domain space data; obtain multiple sets of narrowband frequency domain signals by filtering the frequency domain space data through the filter corresponding to the frequency domain of each beam operating frequency band; and transform the multiple sets of narrowband frequency domain signals from frequency domain to time domain by inverse Fourier transform to obtain the frequency band signal data, which is represented as: ， wherein, denotes a zero-mean Gaussian white noise, denotes the number of time elements, denotes the number of time snapshots, denotes a signal vector, denotes a matrix composed of steering vectors, denotes a center frequency point of a signal, denotes time, denotes an angle in a space domain; a processing module is configured to perform high-resolution spatial spectrum estimation on the signal data of each frequency band by using a MUSIC algorithm to obtain a spatial direction of a target signal and an interference signal; based on a preset multi-beam zeroing anti-interference strategy, adaptive weights of each same-frequency beam are obtained according to the spatial direction of the interference signal; and the adaptive weights of each same-frequency beam are combined to obtain a comprehensive directional diagram, thereby obtaining a multi-beam interference zeroing result. When the processing module obtains the adaptive weight value of each co-frequency beam based on the preset multi-beam zeroing anti-interference strategy according to the incoming wave space direction of the interference signal, the processing module is specifically configured to: An OP adaptive anti-interference algorithm based on the interference subspace is adopted to zero the interference in the main lobe to obtain adaptive beam weight values against the main lobe interference; and / or, a least mean square error (LMS) algorithm is adopted to zero the interference in the sidelobe to obtain adaptive beam weight values against the sidelobe interference; and / or, a linearly constrained minimum variance (LCMV) criterion is adopted to zero the interference in the main lobe and the sidelobe to obtain adaptive beam weight values against the joint interference of the main lobe and the sidelobe.

8. An electronic device, comprising: The device comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete mutual communication through the communication bus; The memory is configured to store a computer program; The processor is configured to execute the program stored on the memory to implement the method in any one of claims 1-6.

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