Communication anti-interference method based on conjugate space

Abstract

The invention discloses a communication anti-interference method based on conjugate space, which relates to the technical field of communication anti-interference, and comprises the following steps: constructing a received signal model; constructing a new received signal model according to the received signal model and the conjugate of the received signal model; constructing a correlation matrix of the new received signal model and conjugate transpose of the new received signal model, and constructing an autocorrelation matrix of the new received signal model according to mathematical expectation of the correlation matrix; constructing a new useful signal steering vector according to the receiving steering vector of the useful signal and the conjugate of the receiving steering vector; constructing an optimization problem based on a conjugate space by taking minimization of output power as an optimization criterion according to a weight vector of a beam former and conjugate transpose thereof, an autocorrelation matrix and a signal steering vector; and solving the optimization problem based on the conjugate space according to a Lagrange multiplier method. According to the method, the problem that a large signal is suppressed due to a small error factor is solved, and the method has very strong guiding significance for anti-interference engineering.

Description

Technical Field

[0001] This invention relates to the field of communication anti-interference technology, and in particular to a communication anti-interference method based on conjugate space. Background Technology

[0002] In the field of communications, due to the variable communication distance, communication signals may appear to have a higher noise floor than the actual noise level. At the same time, under point-to-point communication conditions, there is generally only one useful signal. In order to achieve better communication results and preserve the only useful signal, it is necessary to ensure a high anti-interference success rate for the single signal, which places high demands on the anti-interference success rate.

[0003] The MVDR algorithm and its derivatives represent a major breakthrough in communication anti-interference. However, in practical engineering applications, MVDR is subject to very demanding operating conditions and is particularly sensitive to error signals, including pointing errors, amplitude and phase errors, and antenna element position errors, which are unavoidable in practical applications. These factors greatly limit the engineering application of the MVDR algorithm.

[0004] Therefore, a communication anti-interference method based on conjugate space was developed to solve the above problems. Summary of the Invention

[0005] This invention proposes a communication anti-interference method based on conjugate space to solve the problem that even small error factors in existing technologies can cause large signals to be suppressed.

[0006] The present invention achieves the above objectives through the following technical solutions:

[0007] This invention provides a communication anti-interference method based on conjugate space, comprising:

[0008] A received signal model is constructed based on the reception of several antenna array elements;

[0009] A new received signal model is constructed based on the received signal model and its conjugate.

[0010] Construct the correlation matrix between the new received signal model and its conjugate transpose, and construct the autocorrelation matrix of the new received signal model based on the mathematical expectation of the correlation matrix;

[0011] A new signal steering vector is constructed based on the receiver steering vector of the useful signal and the conjugate of the receiver steering vector;

[0012] Based on the beamformer's weight vector and its conjugate transpose, autocorrelation matrix, and signal steering vector, an optimization problem based on conjugate space is constructed with minimizing output power as the optimization criterion.

[0013] Solve the above optimization problem based on conjugate space using the Lagrange multiplier method to obtain the optimal beamformer weight vector;

[0014] The received real-time signal is correlated with the weight vector of the optimal beamformer to output an anti-interference signal.

[0015] The beneficial effects of this invention are as follows:

[0016] This invention proposes a communication anti-interference method based on conjugate space, which solves the problem in existing MVDR and PI algorithms where even small error factors can cause large signals to be suppressed. This method has strong guiding significance for the engineering application of anti-interference. By introducing conjugate information constraints, this invention effectively introduces new constraints into the autocorrelation matrix, thereby weakening the suppression effect of redundant degrees of freedom on strong signals and improving the survivability of useful signals in anti-interference environments. Attached Figure Description

[0017] Figure 1 The graph shows the effect of signal power on anti-interference performance under interference-free and error-free conditions.

[0018] Figure 2 The graph shows the impact of signal power on anti-interference performance under the condition of no interference and the simultaneous presence of the three types of errors.

[0019] Figure 3 The graph shows the effect of signal power on anti-interference performance under one weak interference (ISR=0dB) and error-free conditions.

[0020] Figure 4 The graph shows the effect of signal power on anti-interference performance under the condition of one weak interference (ISR=0dB) and the simultaneous presence of three types of errors.

[0021] Figure 5 The graph shows the effect of signal power on anti-interference performance under two weak interferences (ISR=0dB) and error-free conditions.

[0022] Figure 6 The graph shows the effect of signal power on anti-interference performance under the condition of two weak interferences (ISR=0dB) and three types of errors existing simultaneously.

[0023] Figure 7 The graph shows the effect of signal power on anti-interference performance under three weak interferences (ISR=0dB) and error-free conditions.

[0024] Figure 8 The graph shows the impact of signal power on anti-interference performance under the condition that three weak interferences (ISR=0dB) and three types of errors exist simultaneously.

[0025] Figure 9 The graph shows the effect of signal power on anti-interference performance under one strong interference (ISR=100dB) and error-free conditions.

[0026] Figure 10 The graph shows the impact of signal power on anti-interference performance under the condition of one strong interference (ISR=100dB) and the simultaneous presence of three types of errors.

[0027] Figure 11 The graph shows the effect of signal power on anti-interference performance under two strong interferences (ISR=100dB) and error-free conditions.

[0028] Figure 12 The graph shows the effect of signal power on anti-interference performance under the condition of two strong interferences (ISR=100dB) and three types of errors existing simultaneously.

[0029] Figure 13 The graph shows the effect of signal power on anti-interference performance under three strong interferences (ISR=100dB) and error-free conditions.

[0030] Figure 14 The graph shows the effect of signal power on anti-interference performance under the condition that three strong interferences (ISR=100dB) and three types of errors exist simultaneously.

[0031] Figure 15 The graph shows the effect of interference power on anti-interference performance under the condition of one interference, weak signal (SNR=-19dB), and no error.

[0032] Figure 16 The graph shows the impact of interference power on anti-interference performance when there is one interference, a weak signal (SNR=-19dB), and three types of errors present simultaneously.

[0033] Figure 17 The graph shows the effect of interference power on anti-interference performance under two interference signals, weak signal (SNR=-19dB), and no error conditions.

[0034] Figure 18 The graph shows the impact of interference power on anti-interference performance when two interferences, a weak signal (SNR=-19dB), and three types of errors coexist.

[0035] Figure 19 The graph shows the effect of interference power on anti-interference performance under three interferences, weak signals (SNR=-19dB), and error-free conditions.

[0036] Figure 20 The graph shows the impact of interference power on anti-interference performance when three types of interference, weak signals (SNR=-19dB) and three types of errors coexist.

[0037] Figure 21 The graph shows the effect of interference power on anti-interference performance under the condition of one interference signal, strong signal (SNR=11dB), and no error.

[0038] Figure 22 The graph shows the impact of interference power on anti-interference performance when there is one interference, a strong signal (SNR=11dB), and three types of errors present simultaneously.

[0039] Figure 23 The graph shows the effect of interference power on anti-interference performance under two interference signals, strong signal (SNR=11dB), and no error conditions.

[0040] Figure 24 The graph shows the impact of interference power on anti-interference performance when two interferences, a strong signal (SNR=11dB), and three types of errors coexist.

[0041] Figure 25 The graph shows the effect of interference power on anti-interference performance under three interferences, strong signal (SNR=11dB), and error-free conditions.

[0042] Figure 26 The graph shows the impact of interference power on anti-interference performance when three interferences, a strong signal (SNR=11dB), and three types of errors coexist. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0044] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0045] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0046] This invention provides a communication anti-interference method based on conjugate space, comprising:

[0047] A received signal model is constructed based on the reception of several antenna array elements;

[0048] A new received signal model is constructed based on the received signal model and its conjugate.

[0049] Construct the correlation matrix between the new received signal model and its conjugate transpose, and construct the autocorrelation matrix of the new received signal model based on the mathematical expectation of the correlation matrix;

[0050] A new signal steering vector is constructed based on the receiver steering vector of the useful signal and the conjugate of the receiver steering vector;

[0051] Based on the beamformer's weight vector and its conjugate transpose, autocorrelation matrix, and signal steering vector, an optimization problem based on conjugate space is constructed with minimizing output power as the optimization criterion.

[0052] Solve the above optimization problem based on conjugate space using the Lagrange multiplier method to obtain the optimal beamformer weight vector;

[0053] The received real-time signal is correlated with the weight vector of the optimal beamformer to output an anti-interference signal.

[0054] In one embodiment, in Under the condition of receiving signals from individual antenna elements, the received signals include A useful signal The interference signal has a received signal model as follows:

[0055]

[0056] t represents the current time. for The vector signal in row 1 and column 1 represents The sum of signals received by each array element; for The vector signal in row 1 and column 1 represents The sum of useful signals received by each array element; for The vector signal in row 1 and column 1 represents The sum of interference signals received by each array element; for The vector signal in row 1 and column 1 represents The sum of noise signals received by each array element; For the kth useful signal, Let be the receiving steering vector for the k-th useful signal. Let be the incident azimuth angle of the k-th useful signal, with a value range of . , Let be the incident elevation angle of the k-th useful signal, with a value range of . ; For the m-th interference signal, Let be the receiving steering vector for the i-th interference signal. Let be the incident azimuth angle of the m-th useful signal, with a value range of . , Let be the incident elevation angle of the m-th interference signal, with a value range of . .

[0057] In one embodiment, For modulated signals, It is a broadband Gaussian interference signal.

[0058] In one embodiment, the new received signal model is:

[0059]

[0060] in, for . conjugate.

[0061] In one embodiment, The autocorrelation matrix is:

[0062]

[0063] in, for transpose, for The conjugate transpose of . for The conjugate transpose of . This represents the statistical average of a random variable, i.e., the mathematical expectation.

[0064] In one embodiment, the signal steering vector is:

[0065] ;

[0066] The receiving steering vector for the useful signal. To receive the conjugate of the guide vector, The incident azimuth angle of the useful signal. The incident elevation angle of the useful signal.

[0067] In one embodiment, the receiving vector of the useful signal is directed. Replace with Therefore, the algorithm can be extended to the corresponding PI algorithm.

[0068] In one embodiment, the optimization problem based on conjugate space is:

[0069] ;

[0070] This is the weight vector for the beamformer. This is the conjugate transpose of the weight vector of the beamformer.

[0071] In one embodiment, according to the Lagrange multiplier method, the optimal solution to the above optimization problem is:

[0072] .

[0073] In one embodiment, the received real-time signal is combined with the weight vector of the optimal beamformer to output an anti-interference signal, including:

[0074] Pick The former Wei, that is

[0075] ;

[0076] for The i-th element;

[0077] The final output anti-interference signal is:

[0078]

[0079] for The former The conjugate transpose of dimensionality For received Dimensional signal.

[0080] The present invention adopts The scheme of using the receiver steering vector of the useful signal as the Conj-MVDR algorithm is adopted in this invention. The scheme using the receiver steering vector of the useful signal is the Conj-PI algorithm. The following simulation experiments compare the anti-interference performance of existing MVDR algorithms, PI algorithms, and the Conj-MVDR and Conj-PI algorithms of this invention:

[0081] (I) Simulation Conditions

[0082] 1) The signal is a BPSK spread spectrum signal with a bandwidth of 20MHz and a spread factor of 8192; 2) The interference signal is also a Gaussian signal with a bandwidth of 20MHz; 3) The noise is Gaussian white noise; 4) The communication frequency is 1268MHz; 5) The antenna is a 4-element rectangular array antenna with an element spacing of half a wavelength.

[0083] The main error signals in the simulation process include: signal direction error, channel amplitude and phase error, and array element position error; the anti-interference performance of each algorithm is examined under the condition that these three types of errors exist simultaneously.

[0084] During simulation, the signal arrival error, azimuth error, and elevation error are all ±10 degrees, with a random increment of +10 degrees or -10 degrees for each simulation; the channel amplitude error is 1 / 10 of the received amplitude, and the phase error is ±5 degrees, also randomized for each simulation. ±5 degrees; the element position error is 1 / 10 of half the wavelength, and is randomized for each simulation. .

[0085] Specifically, the simulations cover the following scenarios: 1) The impact of signal power on anti-interference performance in a no-interference scenario; 2) The impact of signal power on anti-interference performance in a weak-interference scenario; 3) The impact of signal power on anti-interference performance in a strong-interference scenario; 4) The impact of interference power on anti-interference performance in a weak-signal (low signal-to-noise ratio) scenario; 5) The impact of interference power on anti-interference performance in a strong-signal (high signal-to-noise ratio) scenario; and 6) The impact of the number of interference sources on anti-interference performance.

[0086] (II) Evaluation Criteria

[0087] This paper defines the standard signal as the despread signal-to-noise ratio output by the receiver under optimal matching reception conditions, obtained by passing the spread spectrum signal only through a multi-antenna noise system (without interference). Simultaneously, a normal anti-interference system is defined, and the despread signal-to-interference-plus-noise ratio of the output signal after anti-interference is achieved through an anti-interference receiver. The signal-to-noise ratio loss of the system is then defined as...

[0088] .

[0089] (III) Anti-interference performance in interference-free scenarios

[0090] like Figure 1 and Figure 2 The following conclusions can be drawn:

[0091] Under interference-free and error-free conditions, the loss of the MVDR algorithm in signal processing is close to 0, which can be considered as lossless; the PI algorithm will eliminate the part of the useful signal that is higher than the noise floor, thereby deteriorating the output signal-to-noise ratio of the system; and when the SNR is greater than 0dB, the deterioration of the signal-to-noise ratio by PI is basically linear with the change of the input signal-to-noise ratio, that is, the useful signal is suppressed to a level comparable to the noise floor.

[0092] Under interference-free but error-prone conditions, in the high signal-to-noise ratio (SNR) region, both the MVDR and PI algorithms exhibit an approximately linear SNR suppression phenomenon on the useful signal, and the SNR loss caused by MVDR is about 15 dB smaller than that caused by PI; in the low SNR region, there is no additional loss.

[0093] In the low signal-to-noise ratio (SNR) region, the Conj-MVDR algorithm performs comparably to MVDR; in the high SNR region, the Conj-MVDR algorithm can overcome the influence of error factors and hardly causes any loss to the system's SNR.

[0094] In the low signal-to-noise ratio (SNR) region, the Conj-PI algorithm performs comparably to the PI algorithm; in the high SNR region, the Conj-PI algorithm can overcome the influence of error factors and hardly causes any loss to the SNR of the system.

[0095] In the low signal-to-noise ratio (SNR) region, the SNR loss caused by MVDR (Conj-MVDR) is about 6 dB less than that caused by PI (Conj-PI). This is because, under the 4-antenna condition, MVDR (Conj-MVDR) obtains a pointing gain of about 6 dB through the pointing signal.

[0096] (iv) The impact of signal power on anti-interference performance

[0097] This section examines the impact of signal power on anti-interference performance under two scenarios: weak interference and strong interference.

[0098] (4.1): Anti-interference performance in weak interference scenarios; at this time, the interference-to-signal ratio (ISR) is limited to 0dB, and the impact of the number of interferences and signal strength on anti-interference performance is simulated. Since the simulation is a 4-antenna scenario, scenarios with 1 interference, 2 interferences, and 3 interferences are considered.

[0099] like Figure 3-8 As shown, the following conclusions can be drawn:

[0100] In scenarios with 1 or 2 interferences, MVDR will suffer additional linear signal-to-noise ratio loss due to the introduction of error factors, and the loss under the 1 interference condition is significantly greater than that under the 2 interference condition. Under the 3 interference condition, the MVDR algorithm will also suffer a loss in output signal-to-noise ratio due to the introduction of error factors, but the loss value is no longer linearly related to the input signal-to-noise ratio, and is much smaller than the loss under the 1 or 2 interference conditions. The loss is about 0.5dB in the low signal-to-noise ratio region and about 6dB in the high signal-to-noise ratio region.

[0101] For the PI algorithm, under both interference 1 and interference 2 scenarios, regardless of the presence or absence of error factors, the PI algorithm will cause additional linear signal-to-noise ratio loss, and the loss under interference 1 condition is significantly greater than the loss under interference 2 condition; under interference 3 conditions, the PI algorithm will also cause a loss in output signal-to-noise ratio due to the introduction of error factors, but the loss value is no longer linearly related to the input signal-to-noise ratio, and is much smaller than the loss under interference 1 and interference 2 conditions, with a loss of about 6dB in the low signal-to-noise ratio region and about 10dB in the high signal-to-noise ratio region;

[0102] The analysis results from the first two points show that MVDR and PI algorithms can significantly suppress strong signals under conditions of redundancy and degree of freedom, and the greater the redundancy, the more obvious the suppression effect. Under the condition of 3 interferences, the suppression effect on strong signals is significantly weakened due to the lack of additional redundancy. As a result, under the condition of 3 interferences, the signal-to-noise ratio loss caused by MVDR and PI algorithms is significantly less than that in the scenarios with no interference, 1 interference, and 2 interferences.

[0103] The Conj-MVDR and Conj-PI algorithms based on conjugate space can still overcome the shortcomings of the MVDR and PI algorithms and obtain the same conclusions as in the interference-free scenario. That is, in the low signal-to-noise ratio region, their anti-interference performance is comparable to that of the MVDR and PI algorithms, respectively. In the high signal-to-noise ratio region, both algorithms can overcome the weaknesses of the MVDR and PI algorithms and hardly cause any signal-to-noise ratio loss.

[0104] (4.2) Anti-interference performance under strong interference scenarios

[0105] At this point, with the interference-to-signal ratio (ISR) limited to 100dB, the effects of the number of interferences and signal strength on the anti-interference performance are simulated.

[0106] like Figure 9-14 As shown, the following conclusions can be drawn:

[0107] For the MVDR and PI algorithms, under conditions of one and two interferences, the signal-to-noise ratio (SNR) loss first increases and then decreases in the high SNR region; under conditions of three interferences, the SNR loss caused by both algorithms decreases as the SNR increases, and hardly causes any loss in the high SNR region.

[0108] In environments with strong interference, the Conj-MVDR and Conj-PI algorithms can effectively overcome the shortcomings of the MVDR and PI algorithms, and their anti-interference loss decreases as the signal-to-noise ratio increases. Furthermore, under the three conditions of strong interference, the signal-to-noise ratio loss trends of the Conj-MVDR, Conj-PI, MVDR, and PI algorithms are consistent, indicating that under extreme conditions (i.e., conditions without degree-of-freedom redundancy), the anti-interference performance of these algorithms is comparable.

[0109] (v) The impact of interference power on anti-interference performance

[0110] This section simulates the impact of interference power on anti-interference performance under two scenarios: weak signal and strong signal.

[0111] (5.1) Anti-interference performance in weak signal scenarios

[0112] The useful signal is a BPSK signal spread by 8192 times. Under single-antenna conditions, the Eb / n0 before spreading is 20dB, and the chip signal-to-noise ratio after spreading is Ec / n0 = -19dB, that is, the signal is below the noise floor.

[0113] Under the above assumptions, the effects of the number and intensity of interference on the anti-interference performance are further simulated.

[0114] like Figure 15-20 The following conclusions can be drawn:

[0115] In weak signal scenarios, the Conj-MVDR and Conj-PI algorithms perform comparably to the MVDR and PI algorithms, respectively, and the signal-to-noise ratio loss gradually increases with the increase of interference power, reaching a stable value in the strong interference region.

[0116] When there is no error, the Conj-MVDR and MVDR algorithms lose approximately 2 dB of signal-to-noise ratio (SNR) with 1 interference, approximately 4 dB with 2 interferences, and approximately 10 dB with 3 interferences in the strong interference region. For the Conj-PI and PI algorithms, the SNR loses approximately 7 dB with 1 interference, approximately 8 dB with 2 interferences, and approximately 10 dB with 3 interferences in the strong interference region.

[0117] When errors are present, for the Conj-MVDR and MVDR algorithms, in the strong interference region, the signal-to-noise ratio loss is about 3dB with 1 interference, about 7dB with 2 interferences, and about 12dB with 3 interferences; for the Conj-PI and PI algorithms, in the strong interference region, the signal-to-noise ratio loss is about 8dB with 1 interference, about 9dB with 2 interferences, and about 12dB with 3 interferences.

[0118] Comparing the conditions with and without error, the error factor causes additional signal-to-noise ratio (SNR) loss. For Conj-MVDR and MVDR algorithms, in the strong interference region, the SNR loss is approximately 1 dB with 1 interference, approximately 3 dB with 2 interferences, and approximately 2 dB with 3 interferences. For Conj-PI and PI algorithms, in the strong interference region, the SNR loss is approximately 1 dB with 1 interference, approximately 1 dB with 2 interferences, and approximately 2 dB with 3 interferences.

[0119] (5.2) Anti-interference performance in strong signal scenarios

[0120] Strong signals require analog signals to be above the noise floor, which is suitable for non-spread spectrum communication systems. Under single-antenna conditions, Eb / n0 = 50dB before spreading and Ec / n0 = 11dB after spreading, meaning the signal is above the noise floor.

[0121] Under the above assumptions, the effects of the number and intensity of interference on the anti-interference performance are further simulated.

[0122] like Figure 21-26 As shown, the following conclusions can be drawn:

[0123] When the signal-to-noise ratio (SNR) is very high (11dB in this case), the SNR losses of Conj-MVDR, Conj-PI, MVDR, and PI algorithms tend to stabilize and are basically unaffected by interference power. Overall, the SNR loss of Conj-MVDR is smaller than that of MVDR, but the difference is not significant and is within 1dB. The SNR loss of Conj-PI is smaller than that of the PI algorithm, about 6dB smaller with 1 interference, about 3dB smaller with 2 interferences, and the loss values ​​are comparable with 3 interferences.

[0124] In interference scenarios of type 1 and type 2, the signal-to-noise ratio (SNR) loss caused by Conj-MVDR and MVDR algorithms is significantly less than that caused by Conj-PI and PI algorithms. In interference scenario of type 3, in weak interference areas, the SNR loss caused by Conj-MVDR and MVDR algorithms is significantly less than that caused by Conj-PI and PI algorithms. As the interference power increases, the performance of the four algorithms tends to be consistent, that is, under extreme conditions (no degree of freedom redundancy and very strong interference power), the anti-interference algorithm performance of these algorithms is comparable.

[0125] This paper aims to address the problem that MVDR and PI algorithms suppress useful signal power under large signal conditions, leading to signal reception failure after anti-interference. Several algorithms, including MVDR, PI, Conj-MVDR, and Conj-PI, are introduced, and their anti-interference performance in different scenarios is simulated. Simulation results show that both MVDR and PI algorithms suffer from large signal suppression, especially when the system has many error factors. Conj-MVDR and Conj-PI algorithms effectively solve the problem of large signal suppression.

[0126] The communication anti-interference method based on conjugate space proposed in this invention solves the problem of large signal suppression in existing methods.

[0127] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A communication anti-interference method based on conjugate space, characterized in that, include: A received signal model is constructed based on the reception of several antenna array elements; A new received signal model is constructed based on the received signal model and its conjugate. Construct the correlation matrix between the new received signal model and its conjugate transpose, and construct the autocorrelation matrix of the new received signal model based on the mathematical expectation of the correlation matrix; A new useful signal steering vector is constructed based on the useful signal's receiving steering vector and its conjugate. Based on the beamformer's weight vector and its conjugate transpose, autocorrelation matrix, and signal steering vector, an optimization problem based on conjugate space is constructed with minimizing output power as the optimization criterion. Solve the above optimization problem based on conjugate space using the Lagrange multiplier method to obtain the optimal beamformer weight vector; The received real-time signal is correlated with the weight vector of the optimal beamformer to output an anti-interference signal.

2. The communication anti-interference method based on conjugate space according to claim 1, characterized in that, Under the condition of receiving signals with N antenna elements, the received signal includes K useful signals and M interference signals. The received signal model is as follows: ； t represents the current time. It is an N-row, 1-column vector signal, representing the sum of signals received by the N array elements; It is an N-row, 1-column vector signal, representing the sum of the useful signals received by the N array elements; The vector signal is N rows and 1 column, representing the sum of interference signals received by the N array elements; The vector signal is N rows and 1 column, representing the sum of noise signals received by the N array elements; For the kth useful signal, Let be the receiving steering vector for the k-th useful signal. Let be the incident azimuth angle of the k-th useful signal, with a value range of . , Let be the incident elevation angle of the k-th useful signal, with a value range of . ; For the m-th interference signal, Let be the receiving steering vector for the i-th interference signal. Let be the incident azimuth angle of the m-th useful signal, with a value range of . , Let be the incident elevation angle of the m-th interference signal, with a value range of . .

3. The communication anti-interference method based on conjugate space according to claim 2, characterized in that, For modulated signals, It is a broadband Gaussian interference signal.

4. The communication anti-interference method based on conjugate space according to claim 2, characterized in that, The new received signal model is: ； in, for . conjugate.

5. The communication anti-interference method based on conjugate space according to claim 4, characterized in that, The autocorrelation matrix is: ； in, for transpose, for The conjugate transpose of . for The conjugate transpose of . This represents the statistical average of a random variable, i.e., the mathematical expectation.

6. The communication anti-interference method based on conjugate space according to claim 5, characterized in that, The signal steering vector is: ； The receiving steering vector for the useful signal. To receive the conjugate vector of the guide vector, The incident azimuth angle of the useful signal. The incident elevation angle of the useful signal.

7. The communication anti-interference method based on conjugate space according to claim 6, characterized in that, Guide the receiving vector of the useful signal Replace with .

8. The communication anti-interference method based on conjugate space according to claim 6, characterized in that, The optimization problem based on conjugate space is: ； w is the weight vector of the beamformer. This is the conjugate transpose of the weight vector of the beamformer.

9. A communication anti-interference method based on conjugate space according to claim 8, characterized in that, According to the Lagrange multiplier method, the optimal solution to the above optimization problem is: 。 10. A communication anti-interference method based on conjugate space according to claim 9, characterized in that, The received real-time signal is correlated with the weight vector of the optimal beamformer to output an anti-interference signal, including: The final output weighted vector is taken as follows The first N dimensions, i.e.: ； for The i-th element; The final output anti-interference signal is: ； for The conjugate transpose of the first N dimensions The received N-dimensional signal.

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