A multi-array spatial anti-interference method, system and device

Abstract

The application provides a multi-array space anti-interference method, system and device, which utilizes an array antenna to receive N signals, and obtains N digital baseband signals through preprocessing; determines an interference power estimation value through time-frequency domain interference detection; takes a covariance matrix of each digital baseband signal at a current time and performs eigenvalue decomposition to obtain eigenvalues of the covariance matrix of each digital baseband signal at the current time; determines an iteration step length of each digital baseband signal at the current time; determines a zero error mean square value of each digital baseband signal at a next time; takes a weight value when the zero error mean square value of each digital baseband signal at the next time reaches a preset cut-off threshold as an optimal zero weight value of each digital baseband signal; and determines an anti-interference output signal according to the optimal zero weight value and the digital baseband signal corresponding to the optimal zero weight value. The application fully utilizes interference strength estimation and covariance matrix eigenvalues, and realizes adaptive weight adjustment through real-time optimization of an iteration step length.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of communication anti-jamming, in particular to a multi-array spatial anti-jamming method, system and device. BACKGROUND

[0002] Due to the openness of the communication link and the existence in the complex electromagnetic environment, the space-based information system is extremely vulnerable to malicious interference and even destruction. The current satellite communication system is facing multiple electromagnetic threats such as unintentional interference, illegal use and strong game confrontation, and there are problems of mixed legitimate signals and illegal signals, and frequent satellite network disturbance. The spatial anti-jamming nulling processing is based on the principle of spatial adaptive beam forming, and uses a multi-array antenna, each array element has an independent receiving channel. The adaptive nulling algorithm processing unit first samples the signals received by the antenna, and then performs weighted summation processing on the sampling signals of each channel, that is, the amplitude and phase of each receiving signal are changed to make the output of each array element of the combined antenna array form the expected beam, so as to ensure that the beam generates a wave zero point in the direction of the interference signal, thereby achieving the purpose of enhancing the desired signal and suppressing the interference. Compared with the pure time domain and frequency domain filtering technology, the spatial filtering has obvious advantages and is relatively simple to implement and has small amount of calculation.

[0003] The classical adaptive nulling algorithm can be divided into open-loop algorithm (direct solution algorithm) and closed-loop algorithm (feedback control algorithm) according to whether direction finding processing is performed, wherein the traditional least mean square (LMS) closed-loop nulling algorithm is simple to process, can produce a faster adaptive response, reduces the real-time calculation load of the adaptive algorithm, and is more conducive to on-board implementation, but the algorithm has problems of slow convergence speed, limited nulling effect by step selection, and sensitivity to interference power, which need to be improved. SUMMARY

[0004] In view of the deficiencies in the prior art, the purpose of the present application is to provide a multi-array spatial anti-jamming method, system and device to solve the technical problems of slow convergence speed, limited nulling effect by step selection and sensitivity to interference power of the LMS closed-loop nulling algorithm in the prior art.

[0005] To achieve the above purpose, the technical scheme adopted by the present application is as follows:

[0006] A multi-array spatial anti-jamming method, comprising the following steps:

[0007] Step 1, receiving N signals by using an array antenna to obtain N radio frequency signals;

[0008] Step 2, pre-processing the N radio frequency signals to obtain N digital baseband signals;

[0009] Step 3, taking any one of the N digital baseband signals as a reference signal, performing time-frequency domain interference detection on the reference signal to obtain an interference power estimation value;

[0010] Step 4, obtaining a covariance matrix of each digital baseband signal at the current time and performing eigenvalue decomposition to obtain eigenvalues of the covariance matrix of each digital baseband signal at the current time, the eigenvalues including a maximum eigenvalue λ max and a minimum eigenvalue λ min ;

[0011] Step 5, determining an iteration step length of each digital baseband signal at the current time according to the interference power estimation value obtained in step 3 and the eigenvalues of the covariance matrix of each digital baseband signal at the current time obtained in step 4;

[0012] Step 6, calculating the weight value of each digital baseband signal at the next time according to the iteration step length of each digital baseband signal at the current time, and determining the zero error mean square value of each digital baseband signal at the next time according to the calculated weight value of each digital baseband signal at the next time;

[0013] Step 7, repeating steps 4-6 until the zero error mean square value of each digital baseband signal at the next time reaches a preset cutoff threshold, taking the weight value when the preset cutoff threshold is reached as the optimal zero weight value of each digital baseband signal, and obtaining a total of N optimal zero weight values;

[0014] Step 8, determining an anti-interference output signal according to the optimal zero weight value and the digital baseband signal corresponding to the optimal zero weight value.

[0015] The application also has the following technical features:

[0016] Specifically, the preprocessing in step 2 specifically includes: mixing the N radio frequency signals to obtain N analog intermediate frequency signals; AD sampling the N analog intermediate frequency signals to obtain N digital intermediate frequency signals, and performing digital down-conversion processing on the N digital intermediate frequency signals to obtain the N digital baseband signals.

[0017] Further, the iteration step length at the current time in step 5 is determined by the following formula:

[0018]

[0019] Wherein, μ(t) is the iteration step length at the current time, R xx (t) is the covariance matrix at the current time, is the interference power estimation value, λ max is the maximum eigenvalue, λ min is the minimum eigenvalue.

[0020] Further, the weight value of the next time in step 6 is determined by the following formula:

[0021] w(t+1)=w(t)-μ(t)x(t)f(t)

[0022] Wherein, w(t+1) is the weight value of the next time, w(t) is the weight value of the current time, w(1) is the initial value, μ(t) is the iteration step of the current time, f(t) is the error of the array output signal and the expected signal at t time, and f(t)=x(t) H w(t)-d * (t), d * (t) is assigned to 0.

[0023] Further, the cut-off threshold in step 7 is 1x10 -4 .

[0024] Further, the array antenna in step 1 is a 6-element hexagonal phased array antenna, and the coverage range of the array antenna in the elevation direction and the azimuth direction is-60°-60°.

[0025] The application also protects a multi-element spatial domain anti-jamming system for implementing the above multi-element spatial domain anti-jamming method, comprising an array antenna, a digital processing module, an adaptive weight adjustment module and a digital combining module.

[0026] The array antenna is used for receiving radio frequency signals.

[0027] The digital processing module is used for mixing, AD converting, digitally down-converting and processing the received radio frequency signals received by the array antenna to obtain digital baseband signals.

[0028] The adaptive weight adjustment module is used for obtaining the covariance matrix of the digital baseband signals, decomposing the covariance matrix to obtain eigenvalues, determining the iteration step of the baseband signals according to the eigenvalues, and further determining the optimal nulling weight of the baseband signals.

[0029] The digital combining module is used for calculating the anti-jamming output signals according to the optimal nulling weight of each digital baseband signal and each input signal.

[0030] The application also protects a multi-element spatial domain anti-jamming device, comprising a processor and a memory, wherein the memory stores a computer program, and the computer program is executed by the processor to implement the above multi-element spatial domain anti-jamming method.

[0031] Compared with the prior art, the application has the following beneficial effects:

[0032] (1) The method is based on a 16-element hexagonal phased array antenna, and under the condition that the coverage ranges in the elevation direction and the azimuth direction are both -60°-60°, adaptive spatial closed-loop notch processing for high-power interference is realized, reliable communication under complex electromagnetic environment conditions can be effectively ensured, and compared with pure time domain or frequency domain filtering technology, the method is simple to implement and suitable for satellite engineering applications.

[0033] (2) The method uses time-frequency domain interference detection results to guide spatial null processing, fully utilizes interference strength estimation information and covariance matrix eigenvalues of a received signal, and realizes adaptive weight adjustment through real-time optimization and updating of an iteration step, so that the problem that a traditional LMS adaptive algorithm has slow convergence speed and is not suitable for space-based spatial anti-interference applications with high real-time processing requirements can be effectively overcome.

[0034] (3) The adaptive weight step closed-loop null processing mode proposed in the method can effectively improve algorithm convergence accuracy and speed while ensuring spatial null depth performance, has strong robustness to jammer-to-noise ratios, meets the requirements of fast adaptive null processing under the condition of dynamic changes of interference power, and has a simple step update formula and is easy to implement on a satellite. BRIEF DESCRIPTION OF DRAWINGS

[0035] The present application can be better understood by reference to the following description taken in conjunction with the accompanying drawings, which together with the detailed description below, form a part of the present description. In the drawings:

[0036] Figure 1 is a flowchart of the method of the present application;

[0037] Figure 2 is a 16-element hexagonal antenna arrangement of the present application;

[0038] Figure 3 is a null depth map obtained in Example 1;

[0039] Figure 4 is a null contour map obtained in Example 1;

[0040] Figure 5 is a convergence accuracy performance map of Example 1 under the condition of a jammer-to-noise ratio of 20 dB;

[0041] Figure 6 is a convergence accuracy performance map of Example 1 under the condition of a jammer-to-noise ratio of 40 dB;

[0042] Figure 7 is a convergence accuracy performance map of Example 1 under the condition of a jammer-to-noise ratio of 60 dB;

[0043] Figure 8Convergence accuracy performance chart of example 1 under the condition of 80dB dry noise ratio;

[0044] Figure 9 Convergence speed performance chart under different dry noise ratio conditions. DETAILED DESCRIPTION

[0045] In the following, exemplary embodiments of the present application will be described with reference to the accompanying drawings. In the description, not all features of a practical embodiment are described for the sake of clarity and conciseness. It should be appreciated, however, that many embodiment-specific decisions can be made in the course of developing any such practical embodiment in order to achieve the specific objectives of the developer, and these decisions can vary from embodiment to embodiment.

[0046] The technical concept of the present application is to minimize the array output power by iteratively adjusting the nulling weights of the array, so that the array pattern eventually produces nulls in the direction of interference. In the iteration process, the iteration step is adaptively adjusted based on the interference power characteristic information obtained by the front-end time-frequency domain interference detection. As the iteration enters the steady state, the energy of the final array output is minimized, and the optimal nulling weights are obtained.

[0047] It should be understood that the present application is not limited to the described embodiments by virtue of the following description with reference to the drawings. In this context, the embodiments can be combined with each other, features can be replaced or borrowed between different embodiments, and one or more features can be omitted in one embodiment.

[0048] Example 1

[0049] In accordance with the above technical solution, as shown in Figure 1 A multi-element spatial domain anti-interference method is disclosed in the present embodiment, including the following steps:

[0050] Step 1, receiving N-way signals using an array antenna to obtain N-way radio frequency signals;

[0051] In the present embodiment, a phased array antenna with a hexagonal arrangement of 16 receiving elements is used to receive signals, and 16-way radio frequency signals are obtained.

[0052] As shown in Figure 2 , the coverage range of the phased array antenna in the elevation direction and the azimuth direction is -60°-60°, the step d is 0.5°, the interference type is a typical continuous single-tone signal, and the signal is randomly incident within the coverage range of the antenna. The element arrangement and the corresponding wave path difference are shown in Figure 2 .

[0053] Step 2, 16 analog intermediate frequency signals are obtained by mixing the 16 radio frequency signals, 16 digital intermediate frequency signals are obtained by AD sampling the 16 analog intermediate frequency signals, and 16 digital baseband signals are obtained by digitally down-converting the 16 digital intermediate frequency signals; wherein each signal includes 8192 acquisition points, i.e. corresponding to 8192 sampling time points.

[0054] Step 3, taking any one of the 16 digital baseband signals as a reference signal, performing time-frequency domain interference detection on the reference signal to obtain an interference power estimation value;

[0055] Step 4, obtaining a covariance matrix of the 16 digital baseband signals at the current time and performing eigenvalue decomposition to obtain a covariance matrix corresponding to the current time, and then obtaining eigenvalues of the covariance matrix corresponding to the current time through eigenvalue decomposition; the eigenvalues include a maximum eigenvalue λ max and a minimum eigenvalue λ min .

[0056] Specifically, the covariance matrix at the current time is represented as follows:

[0057] R xx (t)=E[x(t)x(t) H ]

[0058] Wherein, R xx (t) is the covariance matrix at the current time, x(t) is the digital baseband signal at the current time, x(t)=[x1(t),x2(t),…,x N (t)] Τ , the superscript H is the conjugate transpose, and the superscript T is the transpose.

[0059] Then, the eigenvalue decomposition of the calculated covariance matrix is performed by the following formula:

[0060]

[0061] Wherein, V x =[υ1,υ2,...,υ N ,] is an orthogonal matrix composed of eigenvalues of the covariance matrix, Λ x is a diagonal matrix composed of eigenvalues of the covariance matrix, and the maximum eigenvalue λ max and the minimum eigenvalue λ min are obtained by solving.

[0062] Step 5, according to the interference power estimation value obtained in step 3 and the eigenvalues (i.e. the maximum eigenvalue λ max and the minimum eigenvalue λ min) determine the iteration step length of each digital baseband signal at the current time; the iteration step length is the convergence factor, which controls the convergence precision and speed of the adaptive nulling algorithm, and the calculation formula of the iteration step length is as follows:

[0063]

[0064] Wherein, μ(t) is the iteration step length at the current time, R xx (t) is the covariance matrix at the current time, is the interference power estimation value, λ max is the maximum eigenvalue, λ min is the minimum eigenvalue.

[0065] Since the dispersion of the eigenvalues of the covariance matrix has a significant influence on the convergence speed and stability of the LMS algorithm, the ratio of the maximum eigenvalue and the minimum eigenvalue of the covariance matrix is introduced in the iteration step length formula to measure the distribution of the eigenvalues, and the interference power estimation value is introduced to consider the influence of the interference signal strength, so as to complete the real-time update of the iteration step length and effectively improve the convergence precision and speed of the LMS algorithm.

[0066] Step 6, calculate the weight value at the next time according to the iteration step length at the current time, and determine the zero error mean square value at the next time according to the calculated weight value at the next time;

[0067] Specifically, the weight value at the next time is determined by the following formula:

[0068] w(t+1)=w(t)-μ(t)x(t)f(t)

[0069] Wherein, w(t+1) is the weight value at the next time, w(t) is the weight value at the current time, t is a natural number greater than or equal to 1, and the initial value of w(1) is 1, μ(t) is the iteration step length at the current time, f(t) is the error of the array output signal and the expected signal at t time, and f(t)=x(t) H w(t)-d * (t), d * (t) is assigned to 0.

[0070] The zero error mean square value at the next time is determined by the following formula:

[0071] δ=E[|W H (+1)(+1)| 2 ]

[0072] Step 7, repeating step 4~step 6 until the zero error mean square value of each digital baseband signal at the next time reaches the preset cut-off threshold, and taking the weight value when the preset cut-off threshold is reached as the optimal zero weight value of each digital baseband signal; that is, 16 optimal zero weight values are finally obtained in the embodiment;

[0073] Step 8, determining the anti-interference output signal according to the optimal zero weight value and the digital baseband signal corresponding to the optimal zero weight value, specifically, taking the obtained optimal zero weight value and the digital baseband signal corresponding to the receiving line into the following formula to obtain the anti-interference output signal:

[0074]

[0075] Wherein, w i * (t) is the optimal zero weight value of the i-th signal at the current time, x(t) is the digital baseband signal at the current time, x(t)=[x1(t), x2(t), …, x N (t)] Τ The superscript H is the conjugate transpose.

[0076] In the embodiment, the MATLAB software is used to verify the estimation convergence accuracy and speed performance of the method under different jammer-to-noise ratios. The simulation statistics 1000 times Monte Carlo experiment results, the jammer-to-noise ratio is 20dB~80dB, the snapshot number is 8192, and the interference incident direction is ±60°. The results are shown in the following table: Figures 3 to 9 As can be seen from the figure, the spatial null processing of three interferences can be realized by using the method, and the zero depth is better than 60dB. Especially, as can be seen from the figure: Figure 5 The convergence speed can be realized by only 53 iterations under the condition of a jammer-to-noise ratio of 20dB, which is 61% higher than the convergence speed of the traditional static weight step scheme of 137 iterations, and is higher than the traditional weight step scheme based on the matrix trace. As can be seen from the figure: Figure 8 Under the condition of a jammer-to-noise ratio of 80dB, the embodiment can realize convergence by 68 iterations, which is 77% higher than the convergence speed of the traditional static weight step scheme of 302 iterations, and the iteration number is also significantly less than the weight step scheme based on the matrix trace. As can be seen from the figure: Figure 9 As can be seen from the figure, under the condition of more than 50 iterations, the convergence accuracy (steady-state error) performance of the method under different jammer-to-noise ratios is significantly better than that of the existing traditional algorithm.

[0077] In summary, the multi-element spatial anti-interference method introduced in the present application can effectively improve the algorithm convergence accuracy and speed by introducing the time-frequency domain interference power estimation, and has strong robustness to the jammer-to-noise ratio, and meets the fast adaptive zero processing demand under the condition of dynamic change of interference power.

[0078] Embodiment 2

[0079] The embodiment discloses a multi-array spatial anti-jamming system, comprising an array antenna, a digital processing module, an adaptive weight adjustment module and a digital combining module.

[0080] The array antenna is configured to receive radio frequency signals.

[0081] The digital processing module is configured to perform mixing, AD conversion and digital down-conversion processing on the received radio frequency signals received by the array antenna to obtain digital baseband signals.

[0082] The adaptive weight adjustment module is configured to obtain a covariance matrix of the digital baseband signals, decompose the covariance matrix to obtain eigenvalues, determine an iteration step size of the baseband signals according to the eigenvalues, and further determine optimal nulling weights of the baseband signals.

[0083] The digital combining module is configured to calculate anti-jamming output signals according to the optimal nulling weights of the digital baseband signals and the input signals.

[0084] Embodiment 3

[0085] The embodiment provides a multi-array spatial anti-jamming device, comprising a processor and a memory, wherein the memory stores a computer program, and the computer program is executed by the processor to implement the multi-array spatial anti-jamming method disclosed in embodiment 1.

[0086] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can also be implemented in other manners. The device embodiments described above are only schematic. For example, the flowcharts and block diagrams in the drawings show the possible implementation architectures, functions and operation of the devices, methods and computer program products according to the embodiments of the present application. In this regard, each block in the flowcharts or block diagrams can represent a module, a program segment or a part of code, which contains one or more executable instructions for implementing the specified logical function.

[0087] The above describes only various embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A multi-element spatial domain anti-interference method, characterized in that, Includes the following steps Step 1: Receive N signals using an array antenna to obtain N radio frequency signals; Step 2: Preprocess the N radio frequency signals to obtain N digital baseband signals; Step 3: Using any one of the N digital baseband signals as a reference signal, perform time-frequency domain interference detection on the reference signal to obtain the interference power estimate. Step 4: Obtain the covariance matrix of each digital baseband signal at the current time and perform eigenvalue decomposition to obtain the eigenvalues ​​of the covariance matrix of each digital baseband signal at the current time. The eigenvalues ​​include the largest eigenvalue λ. max and the minimum eigenvalue λ min ; Step 5: Determine the iteration step size of each digital baseband signal at the current moment based on the interference power estimate obtained in Step 3 and the eigenvalues ​​of the covariance matrix of each digital baseband signal obtained in Step 4 at the current moment. Step 6: Calculate the weight of each digital baseband signal at the next time step based on the iteration step size of each digital baseband signal at the current time step; determine the mean square value of the zero-adjustment error of each digital baseband signal at the next time step based on the calculated weight of each digital baseband signal at the next time step. Step 7: Repeat steps 4 to 6 until the mean square value of the zero-adjustment error of each digital baseband signal reaches the preset cutoff threshold at the next moment. The weight when the preset cutoff threshold is reached is used as the optimal zero-adjustment weight of each digital baseband signal, and a total of N optimal zero-adjustment weights are obtained. Step 8: Determine the anti-interference output signal based on the optimal zero-adjustment weight and the corresponding digital baseband signal with the optimal zero-adjustment weight; The iteration step size at the current moment, as described in step 5, is determined by the following formula: Where μ(t) is the iteration step size at the current time, and R xx (t) is the covariance matrix at the current time. Let λ be the estimated interference power. max λ is the largest eigenvalue. min It is the smallest eigenvalue.

2. The multi-element spatial domain anti-interference method as described in claim 1, characterized in that, The preprocessing described in step 2 specifically includes: performing frequency mixing on the N radio frequency signals to obtain N analog intermediate frequency signals; performing AD sampling on the N analog intermediate frequency signals to obtain N digital intermediate frequency signals; and performing digital down-conversion on the N digital intermediate frequency signals to obtain N digital baseband signals.

3. The multi-element spatial domain anti-interference method as described in claim 1, characterized in that, The weights for the next time step, as described in step 6, are determined using the following formula: w(t+1)=w(t)-μ(t)x(t)f(t) Where w(t+1) is the weight at the next time step, w(t) is the weight at the current time step, w(1) is initially set to 1, μ(t) is the iteration step size at the current time step, f(t) is the error between the array output signal and the desired signal at time t, and f(t) = x(t). H w(t)-d * (t), d * (t) is assigned the value 0.

4. The multi-element spatial domain anti-interference method as described in claim 1, characterized in that, The cutoff threshold mentioned in step 7 is 1×10 -4 .

5. The multi-element spatial domain anti-interference method as described in claim 1, characterized in that, The array antenna mentioned in step 1 is a 6-element hexagonal phased array antenna, and the coverage range of the array antenna in both the elevation and azimuth directions is -60° to 60°.

6. A multi-element spatial anti-interference system, characterized in that, The multi-element spatial anti-interference method as described in any one of claims 1 to 5 includes an array antenna, a digital processing module, an adaptive weight adjustment module, and a digital combining module. The array antenna is used to receive radio frequency signals; The digital processing module is used to perform mixing, AD conversion, and digital down-conversion processing on the received radio frequency signals received by the array antenna to obtain digital baseband signals. The adaptive weight adjustment module is used to obtain the covariance matrix of the digital baseband signal and decompose the covariance matrix to obtain eigenvalues. The iteration step size at the current time is determined based on the eigenvalues; the optimal zeroing weight for the next time step is determined based on the iteration step size at the current time. The digital merging module is used to calculate the anti-interference output signal based on the optimal zero-adjustment weights of each digital baseband signal and each input signal.

7. A multi-element spatial anti-interference device, characterized in that, It includes a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, it implements the multi-element spatial domain anti-interference method as described in any one of claims 1 to 5.

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