A Real-time Analysis Method and System for Multi-target Interference Effects in Electronic Warfare Simulation

Abstract

The present invention relates to the field of data analysis in electronic warfare simulation, and specifically to a real-time analysis method and system for multi-target interference effects in electronic warfare simulation. The method constructs a dynamic multipath channel model by obtaining the multipath propagation parameters of each interfering target in the target area; simulates the time-domain superposition process of the interfering signal waveform and the real signal waveform at the receiving end according to the dynamic multipath channel model, obtains the composite signal at the receiving end and extracts the signal characteristic parameters; obtains the dynamic interference effectiveness value of the interfering target through weighted fusion of the signal characteristic parameters; adjusts the transmission parameters according to the comparison relationship between the dynamic interference effectiveness value and the preset interference effectiveness threshold; when the dynamic interference effectiveness value of the interfering target is greater than or equal to the preset interference effectiveness threshold, the transmission parameters are output as interference strategy parameters. It solves problems such as signal inaccuracy and one-sided interference evaluation in complex electromagnetic environments, and significantly improves the accuracy and battlefield adaptability of interference effect analysis.

Description

Technical Field

[0001] The present invention relates to the field of data analysis in electronic warfare simulation, and in particular to a method and system for real-time analysis of multi-target interference effects in electronic warfare simulation. Background Art

[0002] With the rapid development of electronic warfare technology, the simulation analysis of multi-target interference effects has become an important means to evaluate the effectiveness of electronic countermeasure systems, and the real-time analysis of multi-target interference effects in complex battlefield environments is of great significance. In a complex electromagnetic environment, signal propagation is often accompanied by multi-path effects and is significantly affected. Interference signals and real signals are dynamically superimposed at the receiving end by various influencing factors, which may lead to problems such as signal distortion, false alarms, or missed detections, directly affecting the effectiveness of interference strategies.

[0003] However, the existing technologies still have obvious limitations in aspects such as multi-path modeling, interference effect quantification, and dynamic parameter optimization, and it is difficult to meet the real-time analysis requirements in a high-dynamic electromagnetic environment. Currently, the existing interference effect evaluation relies on a single index and lacks comprehensive analysis of multi-dimensional features, making it difficult to comprehensively reflect the actual impact of complex electromagnetic environments on interference strategies.

[0004] Therefore, there is an urgent need for a method for real-time analysis of multi-target interference effects in electronic warfare simulation to solve problems such as signal inaccuracy and one-sided evaluation in complex electromagnetic environments, and significantly improve the accuracy and adaptability of interference effect analysis. Summary of the Invention

[0005] (1) Technical Problems to be Solved

[0006] The purpose of the present invention is to provide a method and system for real-time analysis of multi-target interference effects in electronic warfare simulation to solve the problems that in a complex electromagnetic environment, signal propagation is often accompanied by multi-path effects and is significantly affected, and interference signals and real signals are dynamically superimposed at the receiving end by various influencing factors, which may lead to problems such as signal distortion, false alarms, or missed detections, directly affecting the effectiveness of interference strategies.

[0007] (2) Technical Solutions

[0008] To achieve the above object, on the one hand, the present invention provides a method for real-time analysis of multi-target interference effects in electronic warfare simulation, the method comprising:

[0009] S1. Obtain the multi-path propagation parameters corresponding to each interference target in the target area, the multi-path propagation parameters including time delay, path attenuation coefficient, and phase offset; construct a dynamic multi-path channel model according to the multi-path propagation parameters.

[0010] S2. Generate an interference signal corresponding to the interference target according to a preset interference strategy, and simulate the time-domain superposition process of the interference signal and the real signal at the receiving end according to the dynamic multipath channel model to obtain the synthesized signal at the receiving end.

[0011] S3. Perform multi-dimensional feature analysis on the synthesized signal at the receiving end to extract signal feature parameters, where the signal feature parameters include the signal-to-noise ratio degradation value, the phase distortion amount, and the target detection false alarm rate increment.

[0012] S4. Obtain the dynamic interference effectiveness value of the interference target by weighted fusion of the signal feature parameters; adjust the transmission parameters of the corresponding interference target according to the comparison relationship between the dynamic interference effectiveness value and the preset interference effectiveness threshold; until the dynamic interference effectiveness value of the interference target is greater than or equal to the preset interference effectiveness threshold, then output the transmission parameters as the interference strategy parameters.

[0013] Further, the method for generating an interference signal corresponding to the interference target according to a preset interference strategy and simulating the time-domain superposition process of the interference signal and the real signal at the receiving end according to the dynamic multipath channel model to obtain the synthesized signal at the receiving end includes:

[0014] According to the multipath propagation parameters in the dynamic multipath channel model, perform time delay compensation, attenuation coefficient weighting, and phase shift correction on each path component of the interference signal and the real signal respectively to obtain a set of multipath signal components; the set of multipath signal components includes interference signal multipath components and real signal multipath components.

[0015] Align the interference signal multipath components and the real signal multipath components at the sampling points in the time domain and perform point-by-point superposition to obtain the synthesized signal at the receiving end.

[0016] Real-time monitor the dynamic changes of the multipath propagation parameters in the current electromagnetic environment. If it is detected that the change amount of the number of paths, time delay, or phase shift amount exceeds the preset tolerance threshold, then update the dynamic multipath channel model.

[0017] Verify whether the synthesized signal at the receiving end meets the preset bandwidth constraint condition through spectrum analysis. If it exceeds the preset bandwidth constraint condition range, then adjust the weight allocation of the path attenuation coefficient in the dynamic multipath channel model until the spectrum of the synthesized signal at the receiving end converges within the preset bandwidth constraint condition range.

[0018] Further, the method for adjusting the weight allocation of the path attenuation coefficient in the dynamic multipath channel model includes:

[0019] Obtain the contribution degrees of each path component of the received synthesized signal to exceeding the preset bandwidth constraint condition; according to the contribution degrees, proportionally reduce the attenuation coefficient weights of the top N path components with the highest contribution degrees, and simultaneously increase the attenuation coefficient weights of the remaining path components while keeping the total attenuation coefficient weight constant; N is the smallest positive integer that satisfies the sum of the contribution degrees of the top N path components being greater than or equal to the preset contribution degree threshold.

[0020] Further, the method for obtaining the contribution degrees of each path component of the received synthesized signal to exceeding the preset bandwidth constraint condition includes:

[0021] According to the spectrum analysis result of the received synthesized signal, determine the out-of-limit frequency band range where the signal energy exceeds the preset bandwidth constraint condition.

[0022] Perform independent spectrum analysis on the signal waveforms of each path component in the dynamic multipath channel model, and respectively extract the energy integral values of each path component within the out-of-limit frequency band range.

[0023] Calculate the energy integral ratio of the energy integral value of each path component to the total energy integral value of all path components within the out-of-limit frequency band range, and use the energy integral ratio as the contribution degree of the corresponding path component to exceeding the preset bandwidth constraint condition.

[0024] Further, the method for performing multi-dimensional feature analysis on the received synthesized signal to extract signal feature parameters includes:

[0025] Perform time-domain analysis on the received synthesized signal, calculate the difference between the average power and the baseline power of the synthesized signal to obtain the signal-to-noise ratio degradation value; perform phase demodulation on the received synthesized signal, and extract the root mean square value of the instantaneous phase deviation of the synthesized signal waveform to obtain the phase distortion amount; perform false alarm rate statistical analysis on the received synthesized signal, and calculate the increment of the current detection false alarm rate and the baseline false alarm rate to obtain the target detection false alarm rate increment.

[0026] Further, the method for adjusting the transmission parameters of the corresponding interference target according to the comparison relationship between the dynamic interference effectiveness value and the preset interference effectiveness threshold includes:

[0027] The transmission parameters include the interference signal frequency, transmission power, and beam pointing angle; if the dynamic interference effectiveness value of the current interference target is less than the preset interference effectiveness threshold, then according to the effectiveness difference between the dynamic interference effectiveness value and the preset interference effectiveness threshold, calculate the adjustment amounts of the interference signal frequency, transmission power, and beam pointing angle respectively.

[0028] The adjustment amount of the interference signal frequency is determined by a frequency offset compensation algorithm, and the compensation direction is to offset towards the center frequency of the interference target receiving frequency band; the adjustment amount of the transmission power is linearly amplified according to the proportional coefficient of the effectiveness difference, and the maximum transmission power does not exceed the preset safe transmission power threshold; the adjustment amount of the beam pointing angle dynamically optimizes the beam pointing angle weight according to the deviation value between the azimuth angle of the interference target and the current beam pointing angle.

[0029] Generate an updated interference signal through data analysis with the adjusted interference signal frequency, transmission power, and beam pointing angle.

[0030] Furthermore, the method of generating an updated interference signal through data analysis with the adjusted interference signal frequency, transmission power, and beam pointing angle includes:

[0031] Generate a baseband modulation signal according to the adjusted interference signal frequency, up-convert the baseband modulation signal to the adjusted interference signal frequency and generate a radio frequency modulation signal; use the adjusted transmission power as the amplitude gain coefficient to amplify the power of the radio frequency modulation signal, and perform clipping processing on the amplified radio frequency modulation signal; according to the adjusted beam pointing angle weight, perform multi-channel weighting processing on the clipped radio frequency modulation signal to obtain a multi-channel space signal set, and the multi-channel space signal set synthesizes a beam signal pointing to the azimuth of the interference target through an array antenna model.

[0032] If the bandwidth of the beam signal is greater than or equal to the preset bandwidth tolerance, readjust the compensation direction of the interference signal frequency or the beam pointing angle weight until an interference signal with a beam signal bandwidth less than the preset bandwidth tolerance is obtained.

[0033] Furthermore, the method of obtaining the dynamic interference effectiveness value of the interference target by weighted fusion of the signal characteristic parameters includes:

[0034] According to the current electromagnetic environment dynamic parameters, obtain the weight coefficients corresponding to the signal-to-noise ratio deterioration value, phase distortion amount, and false alarm rate increment from a pre-trained interference effect evaluation model.

[0035] Perform normalization processing on the signal-to-noise ratio deterioration value, phase distortion amount, and false alarm rate increment respectively according to the weight coefficients to obtain characteristic parameter values; linearly superimpose the characteristic parameter values according to the weight coefficients to obtain the dynamic interference effectiveness value.

[0036] Furthermore, the method of obtaining the weight coefficients corresponding to the signal-to-noise ratio deterioration value, phase distortion amount, and false alarm rate increment from a pre-trained interference effect evaluation model according to the current electromagnetic environment dynamic parameters includes:

[0037] Obtain the electromagnetic environment characteristic parameters of each historical interference scenario and the measured values of the corresponding interference effectiveness to construct a pre-trained interference effect evaluation model; the electromagnetic environment characteristic parameters include the number of signal propagation paths, the standard deviation of path delay, and the environmental noise power spectral density.

[0038] Collect the current electromagnetic environment dynamic parameters in real time and calculate the cosine similarity between the electromagnetic environment dynamic parameters and the electromagnetic environment characteristic parameters, and match the historical interference scenario with the highest similarity as the reference interference scenario; obtain the initial weight coefficient corresponding to the reference interference scenario from the pre-trained interference effect evaluation model.

[0039] Obtain the deviation value between the current electromagnetic environment dynamic parameters and the electromagnetic environment characteristic parameters of the reference interference scenario, and dynamically correct the initial weight coefficient by linear interpolation method to obtain the weight coefficient adapted to the current electromagnetic environment and use it as the weight coefficient corresponding to the signal-to-noise ratio deterioration value, phase distortion amount, and false alarm rate increment.

[0040] On the other hand, based on the same inventive concept, the present invention also provides a real-time analysis system for multi-target interference effects in electronic warfare simulation. The system includes: a data processing module, a synthesized signal analysis module, a signal characteristic parameter acquisition module, and an interference analysis management module, and the modules are communicatively connected in sequence;

[0041] The data processing module is used to obtain the multi-path propagation parameters corresponding to each interference target in the target area. The multi-path propagation parameters include delay, path attenuation coefficient, and phase offset; construct a dynamic multi-path channel model according to the multi-path propagation parameters.

[0042] The synthesized signal analysis module is used to generate an interference signal corresponding to the interference target according to a preset interference strategy, and simulate the time-domain superposition process of the interference signal and the real signal at the receiving end according to the dynamic multi-path channel model to obtain the synthesized signal at the receiving end.

[0043] The signal characteristic parameter acquisition module is used to perform multi-dimensional feature analysis on the synthesized signal at the receiving end to extract signal characteristic parameters. The signal characteristic parameters include signal-to-noise ratio deterioration value, phase distortion amount, and target detection false alarm rate increment;

[0044] The interference analysis management module is used to obtain the dynamic interference effectiveness value of the interference target by weighted fusion of the signal characteristic parameters; adjust the transmission parameters of the corresponding interference target according to the comparison relationship between the dynamic interference effectiveness value and the preset interference effectiveness threshold; until the dynamic interference effectiveness value of the interference target is greater than or equal to the preset interference effectiveness threshold, then output the transmission parameters as the interference strategy parameters.

[0045] (3) Beneficial effects

[0046] Compared with the prior art, the beneficial effects of the present invention are:

[0047] 1. By constructing a dynamic multipath channel model, simulating the time-domain superposition process of interference signals and real signals, combining multi-dimensional feature analysis to extract key signal feature parameters, and weighted fusion to obtain the dynamic interference effectiveness values of each interference target, the interference effect can be accurately evaluated, providing a reliable basis for adjusting the interference strategy.

[0048] 2. According to the comparison relationship between the dynamic interference effectiveness value and the preset interference effectiveness threshold, the transmission parameters can be adaptively adjusted, including the interference signal frequency, transmission power, and beam pointing angle, to ensure that the interference effect remains effective and adapts to the complex and changeable electromagnetic environment.

[0049] 3. Real-time monitor the dynamic changes of multipath propagation parameters in the electromagnetic environment, update the dynamic multipath channel model in a timely manner, and ensure that the synthesized signal at the receiving end meets the preset bandwidth constraint conditions by adjusting methods such as the weight distribution of path attenuation coefficients, improving the pertinence and effectiveness of the interference signal. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a flowchart of a method for real-time analysis of multi-target interference effects in electronic warfare simulation of the present invention.

[0051] Figure 2 It is a schematic diagram of the module composition of a system for real-time analysis of multi-target interference effects in electronic warfare simulation of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0052] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0053] It is necessary to elaborate on the application scenarios of the concept of the present invention. The present invention is a method and system for real-time analysis of multi-target interference effects in electronic warfare simulation, which is applied to solve problems such as signal inaccuracy and one-sided interference evaluation in complex electromagnetic environments, significantly improving the accuracy and battlefield adaptability of multi-target interference effect analysis, providing reliable decision-making support for electronic countermeasures, and improving combat effectiveness.

[0054] As Figure 1 shown, this embodiment provides a method for real-time analysis of multi-target interference effects in electronic warfare simulation, and the method includes:

[0055] S1. Obtain the multipath propagation parameters corresponding to each interfering target within the target area. The multipath propagation parameters include time delay, path attenuation coefficient, and phase offset. Construct a dynamic multipath channel model based on the multipath propagation parameters. In an electronic warfare environment, the signal propagation process is affected by various factors such as terrain, buildings, and the atmosphere, forming multiple propagation paths. The multipath propagation parameters are obtained by an electromagnetic field strength measurement device in combination with a geographic information system. The propagation paths include a direct path (with the minimum time delay and attenuation), a reflection path (caused by terrain and buildings, etc., having different time delay, attenuation characteristics, and phase offsets), and a scattering path, etc. The dynamic multipath channel model can be represented by a discrete time-varying channel transfer function, that is , where the attenuation coefficient represents the amplitude attenuation of the signal of the th path at time; represents that the signal of the th path arrives at the receiving end at time , describing the time delay characteristic of the signal; represents the phase change of the signal of the th path at time. The phase offset will affect the synthesis effect of the signal; it can reflect the change of the channel characteristics over time and is more in line with the characteristics of the actual electromagnetic environment.

[0056] S2. Generate an interference signal corresponding to the interfering target according to a preset interference strategy. Simulate the time-domain superposition process of the interference signal and the real signal at the receiving end based on the dynamic multipath channel model to obtain the synthesized signal at the receiving end. The interference strategies include noise interference (such as white noise, Gaussian noise), deception interference (such as false target echoes), or jamming interference (such as swept-frequency signals), etc. The parameters of the interference signal (such as center frequency, bandwidth, modulation method, etc.) are preset according to the working characteristics of the interfering target, aiming to disrupt or interfere with the normal operation of the enemy's communication system. Generate the real signal of the enemy's normal operation according to the working mode of the enemy's equipment (such as radar transmitted signal, communication data stream). Using the constructed dynamic multipath channel model, calculate the propagation of the interference signal and the real signal according to their respective multipath characteristics, and perform time-domain superposition at the receiving end to obtain the synthesized signal at the receiving end, which can be realized through discrete convolution and numerical addition. By considering the influence of multipath propagation, the synthesized signal obtained by simulation is closer to the actual situation. The receiving end refers to the signal receiving end of the enemy's electronic equipment (such as radar, communication system, etc.). The purpose is to simulate the synthesized signal received by the enemy's equipment in the actual battlefield environment, which contains the interference signal transmitted by our side and the real signal of the enemy itself (such as radar echo or communication signal).

[0057] S3. Perform multi-dimensional feature analysis on the synthesized signal at the receiving end to extract signal feature parameters, where the signal feature parameters include the signal-to-noise ratio deterioration value, the phase distortion amount, and the target detection false alarm rate increment. The signal-to-noise ratio deterioration value, the phase distortion amount, and the target detection false alarm rate increment comprehensively evaluate the interference effect from three dimensions of energy suppression, phase disruption, and false alarm generation respectively, and are the core basis for evaluating the interference effect: If the signal-to-noise ratio deterioration value of the interfering signal in the synthesized signal is high, that is, the energy significantly suppresses the real signal, the interference is effective; if the phase distortion of the synthesized signal is severe, the enemy may not be able to correctly demodulate the signal; if the synthesized signal causes a sharp increase in the false alarm rate, the enemy's target detection logic is successfully disrupted.

[0058] S4. Obtain the dynamic interference efficacy value of the interference target by weighted fusion of the signal feature parameters; according to the comparison relationship between the dynamic interference efficacy value and the preset interference efficacy threshold, adjust the transmission parameters of the corresponding interference target; until the dynamic interference efficacy value of the interference target is greater than or equal to the preset interference efficacy threshold, then output the transmission parameters as interference strategy parameters. During the weighted fusion process, normalization processing is performed on each signal feature parameter and converted into a value between 0 and 1. According to the comparison result between the dynamic interference efficacy value and the preset interference efficacy threshold, it is decided whether to adjust the transmission parameters of the interference target. The interference strategy parameters include the interference signal frequency, transmission power, and beam pointing angle of each interference target.

[0059] The method for generating an interference signal corresponding to an interference target according to a preset interference strategy and simulating the time-domain superposition process of the interference signal and the real signal at the receiving end according to the dynamic multipath channel model to obtain the synthesized signal at the receiving end includes:

[0060] According to the multipath propagation parameters in the dynamic multipath channel model, perform time delay compensation, attenuation coefficient weighting, and phase shift correction on each path component of the interference signal and the real signal respectively to obtain a set of multipath signal components; the set of multipath signal components includes interference signal multipath components and real signal multipath components; the time delay compensation is achieved by shifting the signal sampling points to correctly align each path signal in time; the attenuation coefficient weighting is achieved by multiplying the corresponding attenuation coefficient to simulate the energy loss of the signal during propagation; the phase shift correction is achieved by complex phase rotation to reflect the phase change in multipath propagation.

[0061] Align the multipath components of the interference signal and the multipath components of the real signal in the time domain according to the sampling points and superimpose them point by point to obtain the composite signal at the receiving end; the time-domain superposition process includes performing time-domain waveform alignment and point-by-point superposition on the path components of the interference signal and the real signal. Point-by-point superposition means adding the corresponding sampling values of all path components at each sampling moment to form the sampling value sequence of the composite signal. It is necessary to ensure that the sampling rate is high enough to accurately reflect the time-domain characteristics of the signal. The sampling rate is not less than 2 times the signal bandwidth, meeting the Nyquist sampling theorem.

[0062] Monitor the dynamic changes of the multipath propagation parameters in the current electromagnetic environment in real time. If the change amount of the number of paths, time delay or phase offset exceeds the preset tolerance threshold, update the dynamic multipath channel model; since the actual electromagnetic environment is dynamically changing, it is necessary to monitor the change situation of the multipath propagation parameters in real time. If the change amount of the number of paths, time delay or phase offset exceeds the preset tolerance threshold, it means that the current multipath channel model can no longer accurately reflect the actual propagation environment. Measure the parameters such as the number of paths, time delay and phase offset in the current electromagnetic environment regularly (such as every second or at a higher frequency) and compare them with the parameters in the multipath channel model. When the detected change amount exceeds the preset tolerance threshold (such as the change in the number of paths, the time delay change exceeds 0.1 μs or the phase change exceeds 10°), update the dynamic multipath channel model. This real-time update mechanism ensures that the dynamic multipath channel model is consistent with the actual environment and improves the accuracy of interference effect analysis.

[0063] Verify whether the composite signal at the receiving end meets the preset bandwidth constraint conditions through spectrum analysis. If it exceeds the preset bandwidth constraint condition range, adjust the weight distribution of the path attenuation coefficients in the dynamic multipath channel model until the spectrum of the composite signal at the receiving end converges to the preset bandwidth constraint condition range. In order to ensure that the interference signal does not interfere with other frequency bands, it is necessary to perform spectrum analysis on the composite signal at the receiving end to verify whether it meets the preset bandwidth constraint conditions. Spectrum analysis can be achieved through fast Fourier transform (FFT). If it is found that the signal bandwidth exceeds the constraint range (such as exceeding the target receiving frequency band or exceeding the allowed maximum bandwidth), the weight distribution of the path attenuation coefficients in the dynamic multipath channel model will be adjusted to optimize the spectrum characteristics of the composite signal at the receiving end.

[0064] The method for adjusting the weight distribution of the path attenuation coefficients in the dynamic multipath channel model includes:

[0065] Obtain the contribution degrees of the respective path components of the received-end synthesized signal to exceeding the preset bandwidth constraint condition; according to the contribution degrees, proportionally reduce the attenuation coefficient weights of the top N path components with the highest contribution degrees, and simultaneously increase the attenuation coefficient weights of the remaining path components while keeping the total attenuation coefficient weight constant; N is the smallest positive integer that satisfies the sum of the contribution degrees of the top N path components being greater than or equal to the preset contribution degree threshold.

[0066] According to the adjusted attenuation coefficient weights, regenerate the multipath signal component set, and perform time-domain superposition to obtain the updated received-end synthesized signal. Analyze the contribution degrees of the respective path components to exceeding the preset bandwidth constraint condition, which is achieved by separately performing spectral analysis on each path component and calculating the energy proportion within the over-limit frequency band. Then, proportionally reduce the attenuation coefficient weights of the top N path components with the highest contribution degrees, and at the same time increase the weights of other path components to keep the total attenuation unchanged. The adjustment ratio is determined according to the size of the contribution degree. The determination of N is based on selecting the fewest number of paths such that the sum of their contribution degrees reaches the preset contribution degree threshold. Using the adjusted path attenuation coefficient weights, perform time-delay compensation, attenuation coefficient weighting, and phase shift correction on the original interference signal waveform and the true signal waveform to obtain a new multipath signal component set. Align the new multipath signal component set at the sampling points in the time domain and perform point-by-point superposition. In this way, the signal intensity at each sampling point is the superposition result of the new interference signal and the true signal at that sampling point.

[0067] The method for obtaining the contribution degrees of the respective path components of the received-end synthesized signal to exceeding the preset bandwidth constraint condition includes:

[0068] According to the spectral analysis result of the received-end synthesized signal, determine the over-limit frequency band range where the signal energy exceeds the preset bandwidth constraint condition;

[0069] Perform independent spectral analysis on the signal waveforms of the respective path components in the dynamic multipath channel model, and separately extract the energy integral values of the respective path components within the over-limit frequency band range.

[0070] Calculate the energy integration ratio of the energy integration value of each path component to the total energy integration value of all path components within the over-limit frequency band range, and use the energy integration ratio as the contribution degree of the corresponding path component to exceeding the preset bandwidth constraint condition. Determine the over-limit frequency band range where the signal energy exceeds the preset bandwidth constraint condition by performing spectrum analysis on the received-end composite signal. For example, if the preset bandwidth constraint condition is 100 MHz, but the composite signal has obvious energy within the range of 120 MHz, the over-limit frequency band is 100 - 120 MHz. Perform independent spectrum analysis on the signal waveforms of each path component in the dynamic multipath channel model, and calculate the energy integration value of each path component within the over-limit frequency band. The energy integration value is obtained by performing Fourier transform on the signal waveform of each path component to obtain its spectrum distribution, and then performing integration operation within the over-limit frequency band range. Divide the energy integration value of each path component by the total energy integration value of all path components within the over-limit frequency band to obtain the energy integration ratio and use it as the contribution degree of the corresponding path component. The larger the ratio, the higher the contribution degree of the path component to spectrum over-limit. By accurately quantifying the influence degree of each path on bandwidth over-limit, it provides a scientific basis for subsequent weight adjustment.

[0071] The method for extracting signal feature parameters by performing multi-dimensional feature analysis on the received-end composite signal includes:

[0072] Perform time-domain analysis on the received-end composite signal, and calculate the difference between the average power and the baseline power of the composite signal to obtain the signal-to-noise ratio degradation value; perform phase demodulation on the received-end composite signal, and extract the root mean square value of the instantaneous phase deviation of the composite signal waveform to obtain the phase distortion amount; perform false alarm rate statistical analysis on the received-end composite signal, and calculate the increment between the current detection false alarm rate and the baseline false alarm rate to obtain the target detection false alarm rate increment. The average power can be obtained by averaging the squared values of the signal sampling points. Compare the calculated average power with the pre-measured baseline power, and calculate the difference to obtain the signal-to-noise ratio degradation value. The baseline power can be obtained by measuring or theoretically calculating an interference-free signal under the same conditions. Extract the instantaneous phase deviation of the composite signal waveform through phase demodulation, and the root mean square value of the instantaneous phase deviation can quantify the phase distortion degree of the signal waveform. Phase demodulation can be achieved through Hilbert transform or quadrature demodulation, obtain the instantaneous phase sequence of the signal, compare the actual phase sequence with the ideal phase sequence (the phase sequence without interference), and calculate the root mean square value of the instantaneous phase deviation to obtain the phase distortion amount. By simulating the interference target detection process (such as the CFAR detection algorithm), statistically analyze the detection results under a given false alarm probability, and calculate the increment between the current detection false alarm rate and the baseline false alarm rate (the false alarm rate without interference) to obtain the interference target detection false alarm rate increment.

[0073] The method of adjusting the transmission parameters of the corresponding interference target according to the comparison relationship between the dynamic interference effectiveness value and the preset interference effectiveness threshold includes:

[0074] The transmission parameters include the interference signal frequency, transmission power, and beam pointing angle; if the dynamic interference effectiveness value of the current interference target is less than the preset interference effectiveness threshold, then according to the effectiveness difference between the dynamic interference effectiveness value and the preset interference effectiveness threshold, the adjustment amounts of the interference signal frequency, transmission power, and beam pointing angle are calculated respectively.

[0075] The adjustment amount of the interference signal frequency is determined by the frequency offset compensation algorithm, and the compensation direction is to offset towards the center frequency of the interference target receiving frequency band; the adjustment amount of the transmission power is linearly amplified according to the proportional coefficient of the effectiveness difference, and the maximum transmission power does not exceed the preset safe transmission power threshold; the adjustment amount of the beam pointing angle dynamically optimizes the beam pointing angle weight according to the deviation value between the interference target azimuth angle and the current beam pointing angle.

[0076] The adjusted interference signal frequency, transmission power, and beam pointing angle are used to generate an updated interference signal through data analysis. The adjustment amount of the interference signal frequency is determined by the frequency offset compensation algorithm, and the compensation direction is to offset towards the center frequency of the interference target receiving frequency band to ensure that the interference signal can more effectively cover the receiving frequency band of the interference target. The adjustment amount of the transmission power is linearly amplified according to the proportional coefficient of the effectiveness difference, and the adjusted transmission power shall not exceed the preset safe transmission power threshold to prevent equipment damage or excessive energy consumption. The proportional coefficient is a numerical factor used to quantify the relationship between the effectiveness difference and the adjustment amount of the transmission power, reflecting the sensitivity of the change in the effectiveness difference to the adjustment of the transmission power. Through the proportional coefficient, the effectiveness difference can be converted into the actual adjustment amount of the transmission power, realizing precise control of the transmission power, making the adjustment of the transmission power match the change in the effectiveness difference, and thus optimizing the interference effect. The adjustment amount of the beam pointing angle is obtained by dynamically optimizing the beam pointing angle weight according to the deviation value between the interference target azimuth angle and the current beam pointing angle. By adjusting the beam pointing angle, the interference signal can more accurately point to the interference target and improve the interference effect.

[0077] The method of generating an updated interference signal by using the adjusted interference signal frequency, transmission power, and beam pointing angle through data analysis includes:

[0078] Generate a baseband modulation signal according to the adjusted interference signal frequency, up-convert the baseband modulation signal to the adjusted interference signal frequency and generate a radio frequency modulation signal; use the adjusted transmit power as the amplitude gain coefficient, amplify the power of the radio frequency modulation signal, and perform clipping processing on the amplified radio frequency modulation signal; according to the adjusted beam pointing angle weights, perform multi-channel weighting processing on the clipped radio frequency modulation signal to obtain a multi-channel spatial signal set, and the multi-channel spatial signal set synthesizes a beam signal pointing to the azimuth of the interference target through an array antenna model.

[0079] If the bandwidth of the beam signal is greater than or equal to the preset bandwidth tolerance, readjust the compensation direction of the interference signal frequency or the beam pointing angle weights until an interference signal with a beam signal bandwidth less than the preset bandwidth tolerance is obtained. Generate a baseband modulation signal according to the interference type, where the interference type includes noise interference, spoofing interference, etc., which can be implemented through digital signal processing techniques such as direct digital synthesis (DDS) or software-based waveform generation. Up-convert the baseband modulation signal to the adjusted interference signal frequency to generate a radio frequency modulation signal. The up-conversion process is usually achieved through a mixer, multiplying the baseband signal by the local oscillator signal to generate a radio frequency signal with a frequency of , where is the local oscillator frequency, and is the baseband signal frequency. Power amplification is achieved through a variable gain amplifier, and the gain value is set according to the adjusted transmit power. Perform clipping processing on the amplified radio frequency modulation signal to prevent the signal amplitude from being too large, resulting in nonlinear distortion or damaging the transmitting device. The clipper ensures that the signal is transmitted within a safe amplitude range by limiting the maximum amplitude of the signal. According to the adjusted beam pointing angle weights, perform multi-channel weighting processing on the clipped radio frequency modulation signal to obtain a multi-channel spatial signal set. The signal of each channel is adjusted in amplitude and phase according to its corresponding beam pointing angle weight to form a beam pointing to a specific direction. Synthesize the signals after multi-channel weighting processing through an array antenna model to form a beam signal pointing to the azimuth of the interference target. The array antenna model forms a beam pointing to a specific direction by adjusting the amplitude and phase of each antenna element. The weight calculation can adopt classical beamforming algorithms such as the delay-and-sum method or the minimum variance distortionless response (MVDR) algorithm. The bandwidth of the beam signal being less than the preset bandwidth tolerance is to prevent the interference signal from occupying too much spectrum resources, affecting other legitimate communications or causing spectrum conflicts. By adjusting the compensation direction of the interference signal frequency, the center frequency of the signal can be changed, thereby affecting the signal bandwidth. By adjusting the beam pointing angle weights, the pointing and shape of the beam can be changed, and thus the distribution of the signal in the spectrum can be affected.

[0080] The method of obtaining the dynamic interference effectiveness value of the interference target by weighted fusion of the signal feature parameters includes:

[0081] According to the dynamic parameters of the current electromagnetic environment, obtain the weight coefficients corresponding to the signal-to-noise ratio degradation value, phase distortion amount, and false alarm rate increment from the pre-trained interference effect evaluation model.

[0082] After normalizing the signal-to-noise ratio degradation value, phase distortion amount, and false alarm rate increment respectively according to the weight coefficients, obtain the feature parameter values; linearly superimpose the feature parameter values according to the weight coefficients to obtain the dynamic interference effectiveness value. Obtain the weight coefficients corresponding to the signal-to-noise ratio degradation value, phase distortion amount, and false alarm rate increment from the pre-trained interference effect evaluation model. After obtaining the weight coefficients, normalize each feature parameter to convert parameters with different dimensions and value ranges into a unified standard scale (values between 0 and 1). Normalization can use methods such as min-max normalization or Z-score standardization. Linearly combine the normalized feature parameters according to the weight coefficients to calculate the dynamic interference effectiveness value, that is, dynamic interference effectiveness value = weight coefficient 1 × normalized signal-to-noise ratio degradation value + weight coefficient 2 × normalized phase distortion amount + weight coefficient 3 × normalized false alarm rate increment. Among them, weight coefficients 1, 2, and 3 are the weight coefficients of the three feature parameters, and the sum of weight coefficients 1, 2, and 3 is 1.

[0083] The method of obtaining the weight coefficients corresponding to the signal-to-noise ratio degradation value, phase distortion amount, and false alarm rate increment from the pre-trained interference effect evaluation model according to the dynamic parameters of the current electromagnetic environment includes:

[0084] Obtain the electromagnetic environment feature parameters and the corresponding measured interference effectiveness values of each historical interference scenario to construct a pre-trained interference effect evaluation model; the electromagnetic environment feature parameters include the number of signal propagation paths, the standard deviation of path delay, and the environmental noise power spectral density.

[0085] Real-time collect the dynamic parameters of the current electromagnetic environment and calculate the cosine similarity between the dynamic parameters of the electromagnetic environment and the electromagnetic environment feature parameters, and match the historical interference scenario with the highest similarity as the reference interference scenario; obtain the initial weight coefficients corresponding to the reference interference scenario from the pre-trained interference effect evaluation model.

[0086] Obtain the deviation value between the dynamic parameters of the current electromagnetic environment and the electromagnetic environment characteristic parameters of the reference interference scenario, and dynamically correct the initial weight coefficient through linear interpolation to obtain the weight coefficient adapted to the current electromagnetic environment, which is used as the weight coefficient corresponding to the signal-to-noise ratio degradation value, phase distortion amount, and false alarm rate increment. Historical data can be obtained through experimental tests or actual combat records, and includes interference effect data for different scenarios and different target types. The pre-trained interference effect evaluation model is trained using machine learning algorithms (such as neural networks, support vector machines, etc.) to learn the mapping relationship between electromagnetic environment characteristic parameters and interference effectiveness. The dynamic parameters of the current electromagnetic environment reflect the electromagnetic propagation characteristics and background interference conditions of the current environment, and directly affect the interference effect. Calculate the cosine similarity between the dynamic parameters of the current electromagnetic environment and the electromagnetic environment characteristic parameters of the historical scenario. The closer the cosine similarity value is to 1, the more similar the two environments are. Further calculate and obtain the deviation value between the dynamic parameters of the current electromagnetic environment and the electromagnetic environment characteristic parameters of the reference interference scenario. The deviation value reflects the degree of difference between the current electromagnetic environment and the reference interference scenario, and dynamically correct the initial weight coefficient through linear interpolation. Linear interpolation is a method of estimation based on known data points, which can adjust the initial weight coefficient according to the size of the deviation value to make it more suitable for the current electromagnetic environment.

[0087] Based on the same inventive concept, as Figure 2 shown, this embodiment also provides a real-time analysis system for multi-target interference effects in electronic warfare simulation. The system includes: a data processing module, a synthesized signal analysis module, a signal characteristic parameter acquisition module, and an interference analysis management module, and the modules are sequentially connected for communication;

[0088] The data processing module is used to obtain the multipath propagation parameters corresponding to each interference target in the target area. The multipath propagation parameters include time delay, path attenuation coefficient, and phase offset; construct a dynamic multipath channel model according to the multipath propagation parameters;

[0089] The synthesized signal analysis module is used to generate an interference signal corresponding to the interference target according to a preset interference strategy, and simulate the time-domain superposition process of the interference signal and the real signal at the receiving end according to the dynamic multipath channel model to obtain the synthesized signal at the receiving end;

[0090] The signal characteristic parameter acquisition module is used to perform multi-dimensional feature analysis on the synthesized signal at the receiving end to extract signal characteristic parameters. The signal characteristic parameters include signal-to-noise ratio degradation value, phase distortion amount, and target detection false alarm rate increment;

[0091] An interference analysis management module is configured to obtain a dynamic interference effectiveness value of an interference target through weighted fusion of the signal feature parameters; adjust transmission parameters of a corresponding interference target according to a comparison relationship between the dynamic interference effectiveness value and a preset interference effectiveness threshold; and when the dynamic interference effectiveness value of the interference target is greater than or equal to the preset interference effectiveness threshold, output the transmission parameters as interference strategy parameters.

[0092] It should be noted that regarding the system in the above embodiments, the specific manners in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0093] Finally, it should be noted that although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art may still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A real-time analysis method for multi-target interference effects in electronic warfare simulation, characterized in that The method includes: Obtaining multipath propagation parameters corresponding to each interfering target within a target area, where the multipath propagation parameters include time delay, path attenuation coefficient, and phase offset; constructing a dynamic multipath channel model based on the multipath propagation parameters; Generating an interference signal corresponding to the interfering target according to a preset interference strategy, simulating the time-domain superposition process of the interference signal and the real signal at the receiving end based on the dynamic multipath channel model, and obtaining a synthesized signal at the receiving end; Performing multi-dimensional feature analysis on the synthesized signal at the receiving end to extract signal feature parameters, where the signal feature parameters include signal-to-noise ratio degradation value, phase distortion amount, and target detection false alarm rate increment; Obtaining a dynamic interference effectiveness value of the interfering target by weighted fusion of the signal feature parameters; adjusting the transmission parameters of the corresponding interfering target according to the comparison relationship between the dynamic interference effectiveness value and a preset interference effectiveness threshold; until the dynamic interference effectiveness value of the interfering target is greater than or equal to the preset interference effectiveness threshold, the transmission parameters are output as interference strategy parameters; The method for adjusting the transmission parameters of the corresponding interfering target according to the comparison relationship between the dynamic interference effectiveness value and the preset interference effectiveness threshold includes: The transmission parameters include interference signal frequency, transmission power, and beam pointing angle; if the dynamic interference effectiveness value of the current interfering target is less than the preset interference effectiveness threshold, the adjustment amounts of the interference signal frequency, transmission power, and beam pointing angle are respectively calculated according to the effectiveness difference between the dynamic interference effectiveness value and the preset interference effectiveness threshold; The adjustment amount of the interference signal frequency is determined by a frequency offset compensation algorithm, and the compensation direction is to offset towards the center frequency of the receiving frequency band of the interfering target; the adjustment amount of the transmission power is linearly amplified according to the proportional coefficient of the effectiveness difference, and the maximum transmission power does not exceed a preset safe transmission power threshold; the adjustment amount of the beam pointing angle dynamically optimizes the beam pointing angle weight according to the deviation value between the azimuth angle of the interfering target and the current beam pointing angle; the proportional coefficient is a numerical factor used to quantify the relationship between the effectiveness difference and the adjustment amount of the transmission power, reflecting the sensitivity of the change in the effectiveness difference to the adjustment of the transmission power; Generating an updated interference signal through data analysis of the adjusted interference signal frequency, transmission power, and beam pointing angle; The method for generating an updated interference signal through data analysis of the adjusted interference signal frequency, transmission power, and beam pointing angle includes: Generating a baseband modulation signal according to the adjusted interference signal frequency, up-converting the baseband modulation signal to the adjusted interference signal frequency to generate a radio frequency modulation signal; using the adjusted transmission power as an amplitude gain coefficient, amplifying the radio frequency modulation signal, and performing clipping processing on the amplified radio frequency modulation signal; performing multi-channel weighting processing on the clipped radio frequency modulation signal according to the adjusted beam pointing angle weight to obtain a multi-channel spatial signal set, and synthesizing a beam signal pointing to the azimuth of the interfering target from the multi-channel spatial signal set through an array antenna model; If the bandwidth of the beam signal is greater than or equal to the preset bandwidth tolerance, readjust the compensation direction of the interference signal frequency or the beam pointing angle weight until an interference signal with a beam signal bandwidth less than the preset bandwidth tolerance is obtained. Readjust the compensation direction of the interference signal frequency or the beam pointing angle weight until an interference signal with a beam signal bandwidth less than the preset bandwidth tolerance is obtained.

2. The real-time analysis method for multi-target interference effect in electronic warfare simulation according to claim 1, characterized in that The method for generating an interference signal corresponding to an interference target according to a preset interference strategy, and simulating the time-domain superposition process of the interference signal and the real signal at the receiving end according to the dynamic multipath channel model to obtain the composite signal at the receiving end includes: According to the multipath propagation parameters in the dynamic multipath channel model, perform time-delay compensation, attenuation coefficient weighting, and phase offset correction on each path component of the interference signal and the real signal respectively to obtain a set of multipath signal components; the set of multipath signal components includes interference signal multipath components and real signal multipath components; Align the interference signal multipath components and the real signal multipath components in the time domain according to the sampling points and perform point-by-point superposition to obtain the composite signal at the receiving end; Monitor the dynamic changes of the multipath propagation parameters in the current electromagnetic environment in real time. If the change amount of the number of paths, time delay, or phase offset amount is detected to exceed the preset tolerance threshold, update the dynamic multipath channel model; Verify whether the composite signal at the receiving end meets the preset bandwidth constraint condition through spectrum analysis. If it exceeds the preset bandwidth constraint condition range, adjust the weight distribution of the path attenuation coefficient in the dynamic multipath channel model until the spectrum of the composite signal at the receiving end converges within the preset bandwidth constraint condition range.

3. The real-time analysis method for multi-target interference effect in electronic warfare simulation according to claim 2, characterized in that The method for adjusting the weight distribution of the path attenuation coefficient in the dynamic multipath channel model includes: Obtain the contribution degree of each path component of the composite signal at the receiving end to exceeding the preset bandwidth constraint condition; according to the contribution degree, proportionally reduce the attenuation coefficient weights of the top N path components with the highest contribution degree, and simultaneously increase the attenuation coefficient weights of the remaining path components and keep the total attenuation coefficient weight constant; N is the smallest positive integer that satisfies the sum of the contribution degrees of the top N path components being greater than or equal to the preset contribution degree threshold.

4. A real-time analysis method for multi-target interference effects in electronic warfare simulation according to claim 3, characterized in that, The method for obtaining the contribution degree of each path component of the composite signal at the receiving end to exceeding the preset bandwidth constraint condition includes: According to the spectrum analysis result of the composite signal at the receiving end, determine the over-limit frequency band range where the signal energy exceeds the preset bandwidth constraint condition; Perform independent spectrum analysis on the signal waveforms of each path component in the dynamic multipath channel model, and respectively extract the energy integral values of each path component within the over-limit frequency band range; the energy integral value is obtained by performing Fourier transform on the signal waveform of each path component to obtain its spectrum distribution, and then performing integral operation within the over-limit frequency band range; Calculate the energy integral ratio of the energy integral value of each path component to the total energy integral value of all path components within the over-limit frequency band range, and use the energy integral ratio as the contribution degree of the corresponding path component to exceeding the preset bandwidth constraint condition.

5. A real-time analysis method for multi-target interference effects in electronic warfare simulation according to claim 1, characterized in that The method for performing multi-dimensional feature analysis on the composite signal at the receiving end to extract signal feature parameters includes: Perform time-domain analysis on the synthesized signal at the receiving end, calculate the difference between the average power and the baseline power of the synthesized signal to obtain the signal-to-noise ratio degradation value; perform phase demodulation on the synthesized signal at the receiving end, and extract the root mean square value of the instantaneous phase deviation of the synthesized signal waveform to obtain the phase distortion amount; perform false alarm rate statistical analysis on the synthesized signal at the receiving end, and calculate the increment of the current detection false alarm rate and the baseline false alarm rate to obtain the target detection false alarm rate increment.

6. The real-time analysis method for multi-target interference effect in electronic warfare simulation according to claim 1, wherein The method for obtaining the dynamic interference effectiveness value of the interference target by weighted fusion of the signal characteristic parameters includes: According to the current electromagnetic environment dynamic parameters, obtain the weight coefficients corresponding to the signal-to-noise ratio degradation value, the phase distortion amount, and the false alarm rate increment from the pre-trained interference effect evaluation model; Perform normalization processing on the signal-to-noise ratio degradation value, the phase distortion amount, and the false alarm rate increment respectively according to the weight coefficients to obtain characteristic parameter values; linearly superimpose the characteristic parameter values according to the weight coefficients to obtain the dynamic interference effectiveness value.

7. A real-time analysis method for multi-target interference effects in electronic warfare simulation according to claim 6, characterized in that, The method for obtaining the weight coefficients corresponding to the signal-to-noise ratio degradation value, the phase distortion amount, and the false alarm rate increment from the pre-trained interference effect evaluation model according to the current electromagnetic environment dynamic parameters includes: Obtain the electromagnetic environment characteristic parameters and the corresponding measured interference effectiveness values of each historical interference scenario to construct a pre-trained interference effect evaluation model; the electromagnetic environment characteristic parameters include the number of signal propagation paths, the standard deviation of path delay, and the environmental noise power spectral density; Collect the current electromagnetic environment dynamic parameters in real time and calculate the cosine similarity between the electromagnetic environment dynamic parameters and the electromagnetic environment characteristic parameters, and match the historical interference scenario with the highest similarity as the reference interference scenario; obtain the initial weight coefficients corresponding to the reference interference scenario from the pre-trained interference effect evaluation model; Obtain the deviation value between the current electromagnetic environment dynamic parameters and the electromagnetic environment characteristic parameters of the reference interference scenario, and dynamically correct the initial weight coefficients by linear interpolation method to obtain the weight coefficients adapted to the current electromagnetic environment and use them as the weight coefficients corresponding to the signal-to-noise ratio degradation value, the phase distortion amount, and the false alarm rate increment.

8. A real-time analysis system for multi-target interference effects in electronic warfare simulation, characterized in that The system includes: a data processing module, a synthesized signal analysis module, a signal characteristic parameter acquisition module, and an interference analysis management module, and the modules are communicatively connected in sequence; The data processing module is used to obtain the multipath propagation parameters corresponding to each interference target in the target area, and the multipath propagation parameters include delay, path attenuation coefficient, and phase offset; construct a dynamic multipath channel model according to the multipath propagation parameters; The synthesized signal analysis module is used to generate an interference signal corresponding to the interference target according to a preset interference strategy, simulate the time-domain superposition process of the interference signal and the real signal at the receiving end according to the dynamic multipath channel model, and obtain the synthesized signal at the receiving end; The signal characteristic parameter acquisition module is used to perform multi-dimensional feature analysis on the synthesized signal at the receiving end to extract signal characteristic parameters, and the signal characteristic parameters include signal-to-noise ratio degradation value, phase distortion amount, and target detection false alarm rate increment; The interference analysis and management module is used to obtain the dynamic interference effectiveness value of the interference target through weighted fusion of the signal feature parameters; according to the comparison relationship between the dynamic interference effectiveness value and the preset interference effectiveness threshold, adjust the transmission parameters of the corresponding interference target; until the dynamic interference effectiveness value of the interference target is greater than or equal to the preset interference effectiveness threshold, then output the transmission parameters as interference strategy parameters; The method for adjusting the transmission parameters of the corresponding interference target according to the comparison relationship between the dynamic interference effectiveness value and the preset interference effectiveness threshold includes: The transmission parameters include the interference signal frequency, transmission power, and beam pointing angle; if the dynamic interference effectiveness value of the current interference target is less than the preset interference effectiveness threshold, then according to the effectiveness difference between the dynamic interference effectiveness value and the preset interference effectiveness threshold, calculate the adjustment amounts of the interference signal frequency, transmission power, and beam pointing angle respectively; The adjustment amount of the interference signal frequency is determined by the frequency offset compensation algorithm, and the compensation direction is to offset towards the center frequency of the interference target receiving frequency band; the adjustment amount of the transmission power is linearly amplified according to the proportional coefficient of the effectiveness difference, and the maximum transmission power does not exceed the preset safe transmission power threshold; the adjustment amount of the beam pointing angle dynamically optimizes the beam pointing angle weight according to the deviation value between the interference target azimuth angle and the current beam pointing angle; the proportional coefficient is a numerical factor used to quantify the relationship between the effectiveness difference and the transmission power adjustment amount, reflecting the sensitivity of the effectiveness difference change to the transmission power adjustment; Generate an updated interference signal through data analysis with the adjusted interference signal frequency, transmission power, and beam pointing angle; The method for generating an updated interference signal through data analysis with the adjusted interference signal frequency, transmission power, and beam pointing angle includes: Generate a baseband modulation signal according to the adjusted interference signal frequency, up-convert the baseband modulation signal to the adjusted interference signal frequency and generate a radio frequency modulation signal; use the adjusted transmission power as the amplitude gain coefficient to amplify the power of the radio frequency modulation signal, and perform a clipping process on the amplified radio frequency modulation signal; according to the adjusted beam pointing angle weight, perform multi-channel weighting processing on the clipped radio frequency modulation signal to obtain a multi-channel space signal set, and the multi-channel space signal set is synthesized through an array antenna model into a beam signal pointing to the azimuth of the interference target; If the bandwidth of the beam signal is greater than or equal to the preset bandwidth tolerance, readjust the compensation direction of the interference signal frequency or the beam pointing angle weight until an interference signal with a beam signal bandwidth less than the preset bandwidth tolerance is obtained. Readjust the compensation direction of the interference signal frequency or the beam pointing angle weight until an interference signal with a beam signal bandwidth less than the preset bandwidth tolerance is obtained.

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