Multi-array-element airspace anti-interference method, system and device

Abstract

The invention provides a multi-array-element airspace anti-interference method, system and device, and the method comprises the steps: receiving N paths of signals through an array antenna, and obtaining N paths of digital baseband signals through preprocessing; determining an interference power estimation value through time-frequency domain interference detection; taking a covariance matrix of each path of digital baseband signal at the current moment and performing eigenvalue decomposition to obtain an eigenvalue of the covariance matrix of each path of digital baseband signal at the current moment; determining the iteration step length of each path of digital baseband signal at the current moment; determining a zero setting error mean square value of each path of digital baseband signal at the next moment; taking the weight of each digital baseband signal when the zero setting error mean square value of the digital baseband signal at the next moment reaches a preset cut-off threshold as the optimal zero setting weight of each digital baseband signal; and determining an anti-interference output signal according to the optimal zeroing weight and the digital baseband signal corresponding to the optimal zeroing weight. According to the method, interference intensity estimation and covariance matrix eigenvalues are fully utilized, and adaptive weight adjustment is realized through real-time optimization of an iteration step length.

Description

Technical Field

[0001] The present application relates to the technical field of communication anti-interference, and in particular, to a multi-element spatial domain anti-interference method, system and device. Background Art

[0002] Space-based information systems are vulnerable to malicious interference or even destruction due to the openness of communication links and the existence of a complex electromagnetic environment. The current satellite communication system faces multiple electromagnetic threats such as unintentional interference, illegal theft and strong game confrontation. There are problems such as the mixing of useful signals and illegal signals, and frequent interference of satellite networks. Spatial anti-interference nulling processing is based on the principle of spatial adaptive beamforming, using multi-element antennas, each of which has an independent receiving channel. The adaptive nulling algorithm processing unit first digitally samples the signal received by the antenna, and then performs weighted summation processing on the sampled signals of each channel, that is, controls the amplitude and phase of each received signal to change, so that the output of each element of the combined antenna array forms the desired beam, ensuring that the generated beam produces a notch zero point in the direction of the interference signal, thereby achieving the purpose of enhancing the desired signal and suppressing interference. Spatial filtering has obvious advantages over simple time domain and frequency domain filtering technologies, and is relatively simple to implement and has a small amount of calculation.

[0003] Classic adaptive zeroing algorithms can be divided into open-loop algorithms (direct solution algorithms) and closed-loop algorithms (feedback control algorithms) according to whether direction finding processing is performed. Among them, the traditional least mean square (LMS) closed-loop zeroing algorithm is simple to process and can produce faster adaptive response, which reduces the real-time calculation load of the adaptive algorithm and is more conducive to satellite implementation. However, the algorithm has the problems of slow convergence speed, zeroing effect is limited by step size selection, and is sensitive to interference power, which urgently needs to be improved. Summary of the invention

[0004] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a multi-element spatial domain anti-interference method, system and device to solve the technical problems in the prior art that the LMS closed-loop zeroing algorithm has a slow convergence speed, the zeroing effect is limited by the step size selection and is sensitive to interference power.

[0005] To achieve the above object, the present invention adopts the following technical scheme:

[0006] A multi-element spatial domain anti-interference method comprises the following steps:

[0007] Step 1: Receive N signals using an array antenna to obtain N radio frequency signals;

[0008] Step 2: preprocessing the N-channel RF signals to obtain N-channel digital baseband signals;

[0009] 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 an interference power estimation value;

[0010] Step 4: Obtain the covariance matrix of each digital baseband signal at the current moment and perform eigenvalue decomposition to obtain the eigenvalues ​​of the covariance matrix of each digital baseband signal at the current moment, wherein the eigenvalues ​​include the maximum eigenvalue λ max and the minimum eigenvalue λ min ;

[0011] Step 5, determining the iteration step length of each digital baseband signal at the current moment according to the eigenvalue of the covariance matrix of each digital baseband signal at the current moment obtained according to the interference power estimate obtained in step 3 and step 4;

[0012] Step 6: Calculate the weight of each digital baseband signal at the next moment according to the iteration step of each digital baseband signal at the current moment; determine the zeroing error mean square value of each digital baseband signal at the next moment according to the calculated weight of each digital baseband signal at the next moment;

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

[0014] Step 8: Determine the anti-interference output signal according to the optimal zeroing weight and the digital baseband signal corresponding to the optimal zeroing weight.

[0015] The present invention also has the following technical features:

[0016] Specifically, the preprocessing described in step 2 specifically includes: mixing N RF signals to obtain N analog intermediate frequency signals; AD sampling the N analog intermediate frequency signals to obtain N digital intermediate frequency signals, and digitally down-converting the N digital intermediate frequency signals to obtain N digital baseband signals.

[0017] Furthermore, the iteration step size at the current moment described in step 5 is determined by the following formula:

[0018]

[0019] Among them, μ(t) is the iteration step size at the current moment, R xx (t) is the covariance matrix at the current moment, is the estimated interference power, λ max is the maximum eigenvalue, λ min is the minimum eigenvalue.

[0020] Furthermore, the weight at the next moment described in step 6 is determined by the following formula:

[0021] w(t+1)=w(t)-μ(t)x(t)f(t)

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

[0023] Furthermore, the cutoff threshold in step 7 is 1×10 -4 .

[0024] Furthermore, the array antenna described in step 1 is a phased array antenna with 6 elements arranged in a hexagonal shape, and the coverage range of the array antenna in the elevation direction and the azimuth direction is -60° to 60°.

[0025] The present invention also protects a multi-element spatial domain anti-interference system, which is used to implement the multi-element spatial domain anti-interference method mentioned above, and includes an array antenna, a digital processing module, an adaptive weight adjustment module and a digital combiner;

[0026] The array antenna is used to receive radio frequency signals;

[0027] The digital processing module is used to perform mixing, AD conversion, and digital down-conversion processing on the radio frequency signal received by the array antenna to obtain a digital baseband signal;

[0028] The adaptive weight adjustment module is used to obtain the covariance matrix of the digital baseband signal, decompose the covariance matrix to obtain eigenvalues; determine the iteration step size of the baseband signal according to the eigenvalues, and then the optimal zeroing weight of the baseband signal;

[0029] The digital combining module is used to calculate the anti-interference output signal according to the optimal zeroing weight of each digital baseband signal and each input signal.

[0030] The present invention also protects a multi-element spatial domain anti-interference device, including a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the multi-element spatial domain anti-interference method described above is implemented.

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

[0032] (1) The method of the present invention is based on a 16-element hexagonally arranged phased array antenna. Under the condition that the coverage range in both the elevation direction and the azimuth direction is -60° to 60°, adaptive spatial closed-loop notch processing for high-power interference is realized, which can effectively ensure reliable communication under complex electromagnetic environment conditions. Compared with simple time domain or frequency domain filtering technology, the method of the present invention is simple to implement and is suitable for space-borne engineering applications.

[0033] (2) The present invention uses the interference detection results in the time-frequency domain to guide the spatial zeroing processing, makes full use of the interference intensity estimation information and the eigenvalues ​​of the covariance matrix of the received signal, and realizes the adjustment of the adaptive weights by real-time optimization and updating of the iteration step size. It can effectively overcome the problem that the traditional LMS adaptive algorithm has a slow convergence speed and is not suitable for space-based spatial anti-interference applications that require high real-time processing.

[0034] (3) The adaptive weight stepping closed-loop zeroing processing method proposed in the method of the present invention can effectively improve the convergence accuracy and speed of the algorithm while ensuring the spatial domain zeroing depth performance, and has strong robustness to the interference-to-noise ratio, meeting the needs of fast adaptive zeroing processing under the condition of dynamic changes in interference power. The step size update formula is simple to calculate and easy to implement in satellite engineering. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The present application may be better understood by referring to the following description given in conjunction with the accompanying drawings, which together with the following detailed description are included in this specification and form a part of this specification. In the drawings:

[0036] Figure 1 is a flow chart of the method of the present invention;

[0037] Figure 2 This is a layout diagram of a 16-element hexagonal antenna of the present invention;

[0038] Figure 3 This is the zeroing depth map obtained in Example 1;

[0039] Figure 4 This is the zeroing contour map obtained in Example 1;

[0040] Figure 5 This is a convergence accuracy performance diagram of Example 1 under the condition of an interference-to-noise ratio of 20dB;

[0041] Figure 6 This is a convergence accuracy performance diagram of Example 1 under the condition of an interference-to-noise ratio of 40dB;

[0042] Figure 7 This is a convergence accuracy performance diagram of Example 1 under the condition of an interference-to-noise ratio of 60dB;

[0043] Figure 8This is a convergence accuracy performance diagram of Example 1 under the condition of an interference-to-noise ratio of 80dB;

[0044] Fig. 9 It is the convergence speed performance diagram under different interference-to-noise ratio conditions. DETAILED DESCRIPTION

[0045] The exemplary embodiments of the present application will be described below in conjunction with the accompanying drawings. For the sake of clarity and conciseness, not all features of the actual embodiments are described in the specification. However, it should be understood that many implementation-specific decisions can be made in the process of developing any such actual embodiments in order to achieve the specific goals of the developer, and these decisions may vary from embodiment to embodiment.

[0046] The technical concept of the present invention is: the array output power is minimized by iteratively adjusting the zeroing weight of the array, and finally the array pattern produces a null in the interference direction. In the iterative process, the iteration step size 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 output by the array is minimized, and the optimal zeroing weight is obtained.

[0047] It should be understood that the present application is not limited to the described implementation forms due to the following description with reference to the accompanying drawings. In this article, where feasible, the embodiments can be combined with each other, features between different embodiments can be replaced or borrowed, and one or more features can be omitted in one embodiment.

[0048] Example 1

[0049] Following the above technical solution, Figure 1 As shown, this embodiment discloses a multi-element spatial domain anti-interference method, including the following steps:

[0050] Step 1: Receive N signals using an array antenna to obtain N radio frequency signals;

[0051] In this embodiment, a hexagonally arranged phased array antenna with 16 receiving array elements is used to receive signals to obtain 16 channels of radio frequency signals.

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

[0053] Step 2: Mix the 16 RF signals to obtain 16 analog intermediate frequency signals; perform AD sampling on the 16 analog intermediate frequency signals to obtain 16 digital intermediate frequency signals; perform digital down-conversion on the 16 digital intermediate frequency signals to obtain 16 digital baseband signals; wherein each signal includes 8192 acquisition points, corresponding to 8192 sampling moments.

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

[0055] Step 4: Obtain the covariance matrix of the 16-channel digital baseband signal at the current moment and perform eigenvalue decomposition to obtain a covariance matrix corresponding to the current moment, and then obtain the eigenvalue of the covariance matrix corresponding to the current moment by eigenvalue decomposition; the eigenvalue includes the maximum eigenvalue λ max and the minimum eigenvalue λ min ;

[0056] Specifically, the covariance matrix at the current moment is expressed as follows:

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

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

[0059] Then, the calculated covariance matrix is ​​subjected to eigenvalue decomposition using the following formula:

[0060]

[0061] Among them, V x =[υ1,υ2,...,υ N ,] is an orthogonal matrix composed of the eigenvalues ​​of the covariance matrix, Λ x is a diagonal matrix composed of the eigenvalues ​​of the covariance matrix. Solving it yields the maximum eigenvalue λ max and the minimum eigenvalue λ min .

[0062] Step 5: calculate the eigenvalues ​​of the covariance matrix of the digital baseband signals at the current moment according to the interference power estimate obtained in step 3 and the eigenvalues ​​of the covariance matrix of the digital baseband signals obtained in step 4 (i.e., the maximum eigenvalue λ max and the minimum eigenvalue λ min) determines the iteration step size of each digital baseband signal at the current moment; the iteration step size is the convergence factor, which controls the convergence accuracy and speed of the adaptive zeroing algorithm. The calculation formula of the iteration step size is as follows:

[0063]

[0064] Among them, μ(t) is the iteration step size at the current moment, R xx (t) is the covariance matrix at the current moment, is the estimated interference power, 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 impact on the convergence speed and stability of the LMS algorithm, the ratio of the maximum eigenvalue to the minimum eigenvalue of the covariance matrix is ​​introduced into the iterative step formula to measure the distribution discreteness of the eigenvalues. At the same time, considering the influence of the interference signal strength, the interference power estimation value is introduced to complete the real-time update of the iterative step, thereby effectively improving the convergence accuracy and speed of the LMS algorithm.

[0066] Step 6: Calculate the weight at the next moment according to the iteration step size at the current moment, and determine the zeroing error mean square value at the next moment according to the calculated weight at the next moment;

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

[0068] w(t+1)=w(t)-μ(t)x(t)f(t)

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

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

[0071] δ=E[|W H (+1)(+1)| 2 ]

[0072] Step 7, repeating steps 4 to 6 until the mean square value of the zeroing error of each digital baseband signal at the next moment reaches the preset cutoff threshold, and the weight when the preset cutoff threshold is reached is used as the optimal zeroing weight of each digital baseband signal; that is, 16 optimal zeroing weights are finally obtained in this embodiment;

[0073] Step 8: Determine the anti-interference output signal according to the optimal zeroing weight and the digital baseband signal corresponding to the optimal zeroing weight. Specifically, substitute the obtained optimal zeroing weight and the digital baseband signal of the corresponding receiving line into the following formula to obtain the anti-interference output signal:

[0074]

[0075] Among them, w i * (t) is the optimal zeroing weight of the i-th signal at the current moment, x(t) is the digital baseband signal at the current moment, x(t) = [x1(t), x2(t), …, x N (t)] Τ , the superscript H stands for conjugate transpose.

[0076] In this embodiment, MATLAB software is used to verify the estimated convergence accuracy and speed performance of the method of the present invention under different interference-to-noise ratio conditions. The results of 1000 Monte Carlo experiments are simulated and counted, the interference-to-noise ratio is 20dB~80dB, the number of snapshots is 8192, and the interference incident direction is ±60°. The results are as follows Figures 3 to 9 As shown in the figure, it can be seen that the method of the present invention can achieve spatial notch processing of three interferences, and the zeroing depth is better than 60dB. In particular, from Figure 5 It can be seen that the convergence speed can be achieved in only 53 iterations under the condition of 20dB interference-to-noise ratio. Compared with the 137 iterations of the traditional static weight stepping scheme, the convergence speed is increased by 61%, which is higher than the traditional weight stepping scheme based on matrix trace. Figure 8 It can be seen that under the condition of 80dB interference-to-noise ratio, this embodiment can achieve convergence through 68 iterations, which is 77% faster than the 302 iterations of the traditional static weight stepping scheme, and the number of iterations is significantly less than the weight stepping scheme based on matrix trace. Fig. 9 It can be seen that when the number of iterations is greater than 50, under different interference-to-noise ratio conditions, the convergence accuracy (steady-state error) performance of the method of the present invention is significantly better than that of the existing traditional algorithms.

[0077] In summary, the multi-array spatial domain anti-interference method proposed in the present invention can effectively improve the algorithm convergence accuracy and speed by introducing time-frequency domain interference power estimation, and has strong robustness to the interference-to-noise ratio, meeting the needs of fast adaptive zeroing processing under the condition of dynamic changes in interference power.

[0078] Example 2

[0079] This embodiment discloses a multi-element spatial domain anti-interference system, including an array antenna, a digital processing module, an adaptive weight adjustment module and a digital combiner;

[0080] The array antenna is used to receive radio frequency signals;

[0081] The digital processing module is used to perform mixing, AD conversion, and digital down-conversion processing on the radio frequency signal received by the array antenna to obtain a digital baseband signal;

[0082] The adaptive weight adjustment module is used to obtain the covariance matrix of the digital baseband signal, decompose the covariance matrix to obtain eigenvalues; determine the iteration step size of the baseband signal according to the eigenvalues, and then the optimal zeroing weight of the baseband signal;

[0083] The digital combining module is used to calculate the anti-interference output signal according to the optimal zeroing weight of each digital baseband signal and each input signal.

[0084] Example 3

[0085] This embodiment provides a multi-element spatial domain anti-interference device, including a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the multi-element spatial domain anti-interference method disclosed in Embodiment 1 is implemented.

[0086] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods may also be implemented in other ways. The device embodiments described above are merely schematic, for example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions and operations of the devices, methods and computer program products according to the multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram may represent a module, a program segment or a part of a code, and the module, program segment or a part of a code includes one or more executable instructions for implementing a specified logical function.

[0087] The above are only various implementations of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A multi-element spatial domain anti-interference method, characterized in that: The following steps are included Step 1: Receive N signals using an array antenna to obtain N radio frequency signals; Step 2: preprocessing the N-channel RF signals to obtain N-channel 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 an interference power estimation value; Step 4: Obtain the covariance matrix of each digital baseband signal at the current moment and perform eigenvalue decomposition to obtain the eigenvalues ​​of the covariance matrix of each digital baseband signal at the current moment, wherein the eigenvalues ​​include the maximum eigenvalue λ max and the minimum eigenvalue λ min ; Step 5, determining the iteration step length of each digital baseband signal at the current moment according to the eigenvalue of the covariance matrix of each digital baseband signal at the current moment obtained according to the interference power estimate obtained in step 3 and step 4; Step 6: Calculate the weight of each digital baseband signal at the next moment according to the iteration step of each digital baseband signal at the current moment; determine the zeroing error mean square value of each digital baseband signal at the next moment according to the calculated weight of each digital baseband signal at the next moment; Step 7, repeating steps 4 to 6 until the mean square value of the zeroing error of each digital baseband signal at the next moment reaches a preset cutoff threshold, and taking the weight when the preset cutoff threshold is reached as the optimal zeroing weight of each digital baseband signal, and obtaining a total of N optimal zeroing weights; Step 8: Determine the anti-interference output signal according to the optimal zeroing weight and the digital baseband signal corresponding to the optimal zeroing weight.

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

3. The multi-element spatial domain anti-interference method according to claim 1, characterized in that: The iteration step size at the current moment described in step 5 is determined by the following formula: Among them, μ(t) is the iteration step size at the current moment, R xx (t) is the covariance matrix at the current moment, is the estimated interference power, λ max is the maximum eigenvalue, λ min is the minimum eigenvalue.

4. The multi-element spatial domain anti-interference method according to claim 1, characterized in that: The weight at the next moment described in step 6 is determined by the following formula: w(t+1)=w(t)-μ(t)x(t)f(t) Among them, w(t+1) is the weight at the next moment, w(t) is the weight at the current moment, the initial value of w(1) is 1, μ(t) is the iteration step at the current moment, f(t) is the error between the array output signal and the expected signal at time t, and f(t) = x(t) H w(t)-d * (t), d * (t) is assigned a value of 0.

5. The multi-element spatial domain anti-interference method according to claim 1, characterized in that: The cutoff threshold in step 7 is 1×10 -4 .

6. The multi-element spatial domain anti-interference method according to claim 1, characterized in that: The array antenna described in step 1 is a phased array antenna with 6 elements arranged in a hexagonal shape, and the coverage range of the array antenna in the elevation direction and the azimuth direction is -60° to 60°.

7. A multi-element spatial domain anti-interference system, characterized in that: Implementing the multi-element spatial domain anti-interference method as described in any one of claims 1 to 6, comprising an array antenna, a digital processing module, an adaptive weight adjustment module and a digital combiner; 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 radio frequency signal received by the array antenna to obtain a digital baseband signal; 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; Determine the iteration step size at the current moment according to the characteristic value; determine the optimal zeroing weight at the next moment according to the iteration step size at the current moment; The digital combining module is used to calculate the anti-interference output signal according to the obtained optimal zeroing weights of the digital baseband signals of each channel and the input signals of each channel.

8. A multi-element spatial domain anti-interference device, characterized in that: The method comprises a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the multi-element spatial domain anti-interference method according to any one of claims 1 to 6 is implemented.

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