False target interference method and system

Abstract

The invention provides a false target interference method and system, and belongs to the technical field of signal interference, and the method comprises the steps: calculating the frequency matching degree of the frequency component data of a true target and the working frequency range of an enemy detection system; acquiring a first distance value of the true target relative to the enemy detection system at a first preset moment, and acquiring a second distance value of the true target relative to the enemy detection system at a second preset moment based on a preset time interval; calculating the track identification degree of the true target in the enemy detection system based on the first distance value, the second distance value and the distance resolution; fusing the frequency matching degree and the track identification degree to obtain a comprehensive matching degree; and adjusting the initial false target based on the comprehensive matching degree to obtain a new false target, and performing interference based on the new false target. According to the false target interference method and system provided by the invention, the effectiveness of hiding the true target can be improved.

Description

Technical Field

[0001] This application belongs to the field of signal jamming technology, and more specifically, relates to a method and system for jamming false targets. Background Technology

[0002] In the field of modern electronic warfare, decoy jamming is a key means of protecting real targets and deceiving enemy detection systems, and its effectiveness directly affects the survivability of combat platforms. In traditional decoy jamming methods, the frequency characteristics and motion trajectories of decoys are mostly generated based on fixed parameters, lacking the ability to adapt to the dynamic characteristics of enemy detection systems.

[0003] Enemy detection systems often improve target identification accuracy by adjusting the operating frequency range and distance resolution. However, conventional decoys are difficult to match these changes in real time, resulting in low overlap between frequency characteristics and enemy detection frequency bands, easily distinguishable trajectories, unstable interference effects, and inability to effectively conceal real targets. Summary of the Invention

[0004] The purpose of this application is to provide a method and system for jamming false targets, so as to improve the effectiveness of concealing true targets.

[0005] A first aspect of this application provides a method for interfering with false targets, comprising: The frequency matching degree between the frequency component data of the true target and the operating frequency range of the enemy detection system is calculated. The frequency matching degree is the proportion of the frequency component data of the true target falling within the operating frequency range of the enemy detection system. The frequency component data of the true target is collected based on a preset acquisition period. The system acquires a first distance value of the true target relative to the enemy detection system at a first preset time, and acquires a second distance value of the true target relative to the enemy detection system at a second preset time based on a preset time interval; it calculates the trajectory recognition degree of the true target in the enemy detection system based on the first distance value, the second distance value, and the distance resolution; the distance resolution is the minimum distance interval that the enemy detection system can distinguish between two adjacent targets. The frequency matching degree and trajectory identification degree are fused to obtain the comprehensive matching degree; The initial false target is adjusted based on the comprehensive matching degree to obtain a new false target, and interference is performed based on the new false target. The initial false target is generated based on the frequency component data and motion trajectory data of the real target. The motion trajectory data is obtained based on the first distance value and the second distance value.

[0006] A second aspect of this application provides a false target jamming system, comprising: The first calculation module is used to calculate the frequency matching degree between the frequency component data of the real target and the operating frequency range of the enemy detection system. The frequency matching degree is the proportion of the frequency component data of the real target falling within the operating frequency range of the enemy detection system. The frequency component data of the real target is collected based on a preset acquisition period. The second calculation module is used to obtain the first distance value of the true target relative to the enemy detection system at a first preset time, and to obtain the second distance value of the true target relative to the enemy detection system at a second preset time based on a preset time interval; and to calculate the trajectory recognition degree of the true target in the enemy detection system based on the first distance value, the second distance value and the distance resolution; the distance resolution is the minimum distance interval that the enemy detection system can distinguish between two adjacent targets; The fusion module is used to fuse the frequency matching degree and trajectory identification degree to obtain the comprehensive matching degree; The adjustment module is used to adjust the initial false target based on the comprehensive matching degree to obtain a new false target, and to perform interference based on the new false target; the initial false target is generated based on the frequency component data and motion trajectory data of the real target, and the motion trajectory data is obtained based on the first distance value and the second distance value.

[0007] A third aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of the above-described decoy interference method.

[0008] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the aforementioned decoy target interference method.

[0009] The beneficial effects of the false target jamming method and system provided in this application are as follows: First, by calculating the frequency matching degree, the adaptability of the frequency characteristics of the real target to the enemy's detection frequency band is quantified, ensuring that the frequency parameters of the false target can accurately cover the enemy's detection range and reducing the jamming failure problem caused by frequency mismatch. Second, the trajectory identification degree is calculated based on the distance values ​​and distance resolution of adjacent time moments, so that the movement trajectory of the false target is highly consistent with that of the real target, reducing the probability of being identified by the enemy. Third, by fusing the frequency matching degree and trajectory identification degree to obtain the comprehensive matching degree, the dynamic adjustment of the frequency and trajectory of the false target is realized, solving the defects of single false target features and poor adaptability in conventional methods. Finally, the initial false target is generated based on the measured data of the real target, and the adjusted new false target can adapt to the enemy's detection characteristics in real time, greatly improving the realism of the false target, thereby effectively confusing the enemy's identification of real and false targets and improving the success rate of real target concealment. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 A schematic flowchart of a false target interference method provided in an embodiment of this application; Figure 2 This is a structural block diagram of a decoy jamming system provided in an embodiment of this application; Figure 3 This is a schematic block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0012] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0013] To make the objectives, technical solutions, and advantages of this application clearer, the following description will be provided in conjunction with the accompanying drawings and specific embodiments.

[0014] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a decoy target interference method provided in an embodiment of this application. The method can be executed by an electronic device and may include: S101: Calculate the frequency matching degree between the frequency component data of the true target and the operating frequency range of the enemy detection system. The frequency matching degree is the proportion of the frequency component data of the true target falling within the operating frequency range of the enemy detection system. The frequency component data of the true target is collected based on a preset acquisition cycle.

[0015] In this embodiment, the true target is a real combat target that needs to be concealed or protected (such as an aircraft or tank), and its reflected signal has specific frequency characteristics and trajectory. Frequency component data is a set of multiple frequency components and their corresponding characteristics obtained by decomposing the true target's reflected signal after processing, reflecting the signal spectrum characteristics of the true target. The enemy detection system is an electronic device (such as a radar system) used by the enemy to detect targets, acquiring target information by transmitting and receiving electromagnetic waves. Frequency matching degree is the proportion of the true target's frequency component data falling within the enemy detection system's operating frequency range to the total number of frequency components, used to represent the degree of compatibility between the true target's frequency characteristics and the enemy's detection frequency band.

[0016] In this embodiment, data acquisition is initiated based on a preset acquisition period (the preset acquisition period can be determined according to the changing trend of the current operating frequency range of the enemy detection system), and a spectrum monitoring device (such as a spectrum analyzer) is used to continuously monitor the signal reflected by the real target to obtain the raw time domain data of the signal.

[0017] The collected time-domain signal is subjected to Fourier transform and converted into a frequency-domain signal. Then, the frequency component data of the true target is extracted by feature extraction algorithm (such as peak detection), that is, each main frequency component and its corresponding feature information, forming a frequency component dataset.

[0018] Intercept detection signals (such as radar signals) emitted by enemy detection systems using signal reconnaissance equipment (such as ultra-wideband reconnaissance receivers). Perform spectrum analysis (such as Fast Fourier Transform, FFT) on the intercepted signals to determine the current operating frequency range of the enemy detection system [f]. min ,f max Finally, the true target frequency components fall within [f]. min ,f max The frequency matching degree is the proportion of the number of frequencies within a certain range to the total number of frequency components. The formula for calculating the frequency matching degree is:

[0019] in, Indicates frequency matching degree, Represents the Dirac function (when the i-th frequency component f) i Falling within the operating frequency range of the enemy's detection system [f min ,f max When f is within ], the function value is 1; when f i When the frequency is outside this range, the function value is 0. The Dirac function is used to count the number of frequency components that fall within the effective frequency range. This represents the lower limit of the operating frequency range of the enemy detection system. This indicates the upper limit of the operating frequency range of the enemy detection system. This represents the total number of frequency components in the true target frequency component dataset.

[0020] The formula for calculating frequency matching degree reflects the degree of overlap between the frequency characteristics of the true target and the enemy's detection frequency band by statistically analyzing the proportion of frequency components of the true target falling within the enemy's operating frequency range to the total number of frequency components.

[0021] S102: Obtain the first distance value of the true target relative to the enemy detection system at a first preset time, and obtain the second distance value of the true target relative to the enemy detection system at a second preset time based on a preset time interval; calculate the trajectory recognition degree of the true target in the enemy detection system based on the first distance value, the second distance value and the distance resolution; the distance resolution is the minimum distance interval that the enemy detection system can distinguish between two adjacent targets.

[0022] In this embodiment, the distance value is the straight-line distance measurement of the real target relative to the enemy detection system at a preset time, calculated through radar echo delay or positioning system, such as the first distance value r1 = 5000m and the second distance value r2 = 5100m. Range resolution is the minimum distance interval that the enemy detection system can distinguish between two adjacent targets, determined by the system signal bandwidth. Trajectory recognition is an indicator reflecting the ease with which the real target's trajectory is identified by the enemy detection system.

[0023] In this embodiment, a first preset time (e.g., t1) is set. The straight-line distance of the true target relative to the enemy detection system at that time is measured using a high-precision positioning or detection device (e.g., an inertial measurement unit (IMU) combined with a satellite positioning system, a radar echo ranging system, etc.), and recorded as the first distance value r1. A second preset time t2 is determined based on a preset time interval: t2 = t1 + preset time interval. At the second preset time t2, the same measurement device and method are used to obtain the straight-line distance of the true target relative to the enemy detection system, and recorded as the second distance value r2.

[0024] By intercepting detection signals (such as radar echo signals) emitted by enemy detection systems using signal reconnaissance equipment, the pulse width and bandwidth parameters of the signals are extracted. The range resolution is then calculated using the formula for range resolution. The formula for calculating distance resolution is:

[0025] Where c is the speed of light and B is the bandwidth of the enemy's detection signal.

[0026] Calculate the absolute value of the difference between the first distance value and the second distance value: This value reflects the change in position of the true target within a preset time interval. Based on the distance resolution and the aforementioned change in position, the trajectory recognition degree I is calculated. r The calculation formula is:

[0027] Trajectory Recognition I r It reflects the degree to which the trajectory of a real target can be identified in the enemy's detection system. The smaller the value, the more difficult it is for the enemy to clearly distinguish the trajectory, and the easier it is for the trajectory of a false target to be confused with that of a real target.

[0028] S103: The frequency matching degree and trajectory identification degree are fused to obtain the comprehensive matching degree.

[0029] In one embodiment of this application, the frequency matching degree and trajectory identification degree are fused to obtain a comprehensive matching degree, including: The weights corresponding to frequency matching degree are determined based on the operating frequency range of the enemy detection system, and the weights corresponding to trajectory identification degree are determined based on the range resolution of the enemy detection system. The comprehensive matching degree is obtained by weighted fusion based on frequency matching degree, trajectory identification degree and their corresponding weights.

[0030] In this embodiment, the weight corresponding to the frequency matching degree is dynamically determined based on the operating frequency range of the enemy detection system: if the operating frequency range of the enemy detection system is narrow (e.g., only covering a few frequency components of the true target), the weight of the frequency matching degree is increased (e.g., the weight coefficient). The weight of frequency matching should be set to 0.6-0.7 to prioritize the compatibility of the false target's frequency characteristics with the enemy's detection frequency band. If the enemy's operating frequency range is wide (covering most frequency components of the true target), the weight of frequency matching should be reduced (e.g., ...). Take 0.3-0.4).

[0031] The weight corresponding to the trajectory identification degree is determined based on the range resolution of the enemy's detection system: if the enemy's range resolution is high (i.e., If the value is relatively small (and has a strong ability to distinguish close-range targets), then the weight of trajectory recognition is increased (e.g., the weighting coefficient). Use a value of 0.6-0.7 to enhance the deceptiveness of false target trajectories; if the enemy's range resolution is low ( If the value is relatively large, then reduce the weight of trajectory recognition (e.g., (Take a value of 0.3-0.4). Ensure the weighting coefficients meet the following requirements. + =1, and the specific value of the weight can be determined through orthogonal experiments or simulation tests (as mentioned in the relevant embodiments of claim 1).

[0032] Weighting coefficients , Each with frequency matching degree P f Inverse indicators of trajectory recognition (such as 1 / I) r , where I r Multiply the frequency fit value and the trajectory fit value by the trajectory identification value to obtain the weighted frequency fit value and trajectory fit value.

[0033] The formula for summing the two weighted values ​​is as follows:

[0034] Where M is the overall matching degree, and the value range is [0,1]. The larger the value, the better the overall compatibility between the false target and the enemy detection system.

[0035] S104: Adjust the initial false target based on the comprehensive matching degree to obtain a new false target, and perform interference based on the new false target; the initial false target is generated based on the frequency component data and motion trajectory data of the real target, and the motion trajectory data is obtained based on the first distance value and the second distance value.

[0036] In this embodiment, the initial false target is a virtual target signal initially generated based on the frequency component data and motion trajectory data of the real target. The motion trajectory data is a set of parameters describing the real target's path in space, including information such as position, velocity, and acceleration at different times. In this embodiment, a trajectory model can be obtained by fitting a first distance value at a first preset time and a second distance value at a second preset time, combined with a time interval, thereby extracting the motion trajectory data. The new false target is an optimized false target signal generated by dynamically adjusting the frequency characteristics or motion trajectory of the initial false target based on a comprehensive matching degree. Its parameters are specifically optimized based on the comparison results of the comprehensive matching degree and a threshold to improve the interference effect on the enemy's detection system.

[0037] In this embodiment, based on the frequency component data of the real target, the spectral characteristics of the real target are replicated to generate the frequency parameters (including center frequency, frequency component distribution, amplitude value, etc.) of the initial false target. Based on the first distance value r1, the second distance value r2, and the corresponding preset time, a motion trajectory model (e.g., the function relationship between position and time) of the real target is established through curve fitting (e.g., least squares method), and dynamic parameters such as velocity and acceleration of the trajectory are extracted to generate the motion trajectory data of the initial false target (including initial position, direction of motion, velocity change law, etc.). The above frequency parameters and motion trajectory data are fused to form the complete feature parameters of the initial false target.

[0038] If the overall matching degree is high (the real target is easily detected), it is necessary to strengthen the consistency of features between the false target and the real target, so that the frequency matching degree and trajectory identification degree of the false target are close to those of the real target, making it difficult for the enemy to distinguish between the real and false targets (for example, increasing the overlap between the frequency of the false target and the enemy's frequency band, adjusting the trajectory of the false target so that the distance change between it and the real target is close to the enemy's resolution threshold); if the overall matching degree is low (the real target is difficult to detect), it is necessary to make the false target simulate the "low detectability features" of the real target, such as reducing the matching degree between the frequency of the false target and the enemy's frequency band, or making the trajectory change of the false target close to the enemy's distance resolution, further increasing the difficulty of the enemy's identification.

[0039] By adjusting the frequency and trajectory characteristics of the new false targets, the overall matching degree characteristics of the true targets can be made as close as possible to those of the true targets, so that the enemy's detection system will mistake the false targets for the true targets, or be unable to lock onto the true targets due to the high similarity of the characteristics of the true and false targets.

[0040] After generating a new false target, the characteristics (frequency, trajectory) of the false target are radiated or reflected back to the enemy's detection system through jamming equipment (such as radar decoys, infrared flares, electronic pods, etc.), thus interfering with the enemy's detection, tracking, or identification processes. If the frequency matching degree of the false target and the real target is close, the enemy may misjudge the frequency signal of the false target as the real target; if the trajectory identification degree of the false target is consistent with that of the real target, the enemy may not be able to distinguish the movement trajectory of the real and false targets, resulting in tracking failure or misjudgment of the number of targets (such as identifying one real target and multiple false targets as multiple groups of real targets).

[0041] As can be seen from the above, this embodiment measures the compatibility between the frequency characteristics of the real target and the enemy's detection frequency band by calculating the frequency matching degree, ensuring that the frequency parameters of the false target can accurately cover the enemy's detection range and reducing the interference failure problem caused by frequency mismatch. Secondly, the trajectory identification degree is calculated based on the distance values ​​and distance resolution of adjacent time moments, so that the trajectory of the false target is highly consistent with that of the real target, reducing the probability of being identified by the enemy. Furthermore, by fusing the frequency matching degree and trajectory identification degree to obtain the comprehensive matching degree, the frequency and trajectory of the false target are dynamically adjusted, which solves the defects of single false target features and poor adaptability in conventional methods. Finally, the initial false target is generated based on the measured data of the real target, and the adjusted new false target can adapt to the enemy's detection characteristics in real time, greatly improving the realism of the false target, thereby effectively confusing the enemy's identification of real and false targets and improving the success rate of real target concealment.

[0042] In one embodiment of this application, the process of acquiring the frequency component data of the true target includes: Obtain the time-domain signal reflected by the real target, and perform a Fourier transform on the time-domain signal to obtain the frequency-domain signal corresponding to the real target reflection signal; Feature extraction is performed on the frequency domain signal to obtain the frequency component data of the true target.

[0043] In this embodiment, the time-domain signal is a signal form described by time as a variable, reflecting the signal's variation over time and representing the raw data form of the acquired signal. The frequency-domain signal is a signal form described by frequency as a variable, visually presenting the various frequency components contained in the signal and their corresponding amplitude, phase, and other information.

[0044] In this embodiment, a signal receiving device (such as an antenna array with a receiver) is deployed to collect enemy detection signals (such as radar electromagnetic waves) reflected by the real target, and record the original time-domain data of the signal changes over time (i.e., the fluctuation curve of voltage or power over time). The collection time needs to cover the typical motion state of the real target to ensure signal integrity.

[0045] The Fourier transform algorithm is used to mathematically transform the acquired time-domain signal, converting it from the time domain to the frequency domain to obtain the frequency-domain signal. This transformation allows for the decomposition of the signal's frequency components and their corresponding amplitude and phase information.

[0046] Peak detection is used to process the frequency domain signal, and the main frequency components with amplitudes exceeding a preset threshold are selected. The specific values ​​of each frequency and their corresponding amplitudes are recorded to form the frequency component data of the true target, providing a basis for subsequent simulation of the frequency characteristics of the false target.

[0047] As can be seen from the above, this embodiment achieves a comprehensive analysis of the spectral characteristics of the real target signal by acquiring the time-domain signal reflected by the real target and converting it into a frequency-domain signal through Fourier transform, thus avoiding the problem of incomplete frequency feature extraction in traditional methods. Secondly, by screening the main frequency components and corresponding features through feature extraction algorithms, the accuracy and relevance of the frequency component data are ensured, enabling the extracted frequency features to truly reflect the signal essence of the real target. The frequency component data provides a direct basis for the simulation of the frequency characteristics of the initial false target, improves the similarity of the frequency characteristics between the false target and the real target, reduces the probability of being identified by the enemy detection system, and enhances the realism and effectiveness of the interference signal.

[0048] In one embodiment of this application, adjusting the initial false target based on the comprehensive matching degree to obtain a new false target includes: In response to the overall matching degree being less than the first matching degree threshold, the frequency offset of the initial false target is determined based on the deviation between the overall matching degree and the first matching degree threshold. The center frequency of the initial false target is adjusted based on the frequency offset and the center frequency of the true target to obtain a new false target. In response to the overall matching degree being greater than the second matching degree threshold, the trajectory correction parameter is determined based on the difference between the overall matching degree and the second matching degree threshold. The motion trajectory data of the initial false target is adjusted by the trajectory correction parameter to obtain a new false target. In response to a comprehensive matching degree greater than or equal to the first matching degree threshold and less than or equal to the second matching degree threshold, an adjustment ratio coefficient is calculated. The formula for calculating the adjustment ratio coefficient is as follows:

[0049] in, This indicates the adjustment ratio coefficient. represents the comprehensive matching degree, represents the first matching degree threshold, represents the second matching degree threshold; Determine the frequency adjustment weight and trajectory adjustment weight of the initial false target based on the adjustment ratio coefficient; Adjust the frequency of the initial false target based on the frequency adjustment weight, and adjust the motion trajectory data of the initial false target based on the trajectory adjustment weight to obtain a new false target.

[0050] In this embodiment, when the comprehensive matching degree is lower than the first matching degree threshold (M < M1), it indicates that the overall adaptability of the false target to the enemy detection system is poor, and the problem lies in insufficient frequency feature adaptation. The comprehensive matching degree is weighted and fused by the frequency matching degree and the trajectory recognition degree. At this time, the low frequency matching degree (the proportion of the true target frequency component falling within the enemy's working frequency range) is the main factor. The enemy detection system identifies targets through a specific working frequency range. If the frequency characteristics of the false target have a low overlap with this range (poor frequency matching degree), it will directly cause the false target signal to be difficult to be captured by the enemy detection system, and the interference signal will be invalidated. At this time, adjusting the frequency parameter first (such as optimizing the center frequency through the frequency offset) can quickly increase the overlap degree between the false target frequency and the enemy detection frequency band, and solve the problem that the signal cannot be detected.

[0051] When the comprehensive matching degree is lower than the first matching degree threshold (M < M1), calculate the deviation value , based on the preset frequency adjustment coefficient k f Determine the frequency offset . Then the new false target f 新 = f 真 + , where f 真 is the center frequency of the true target.

[0052] In this embodiment, when the comprehensive matching degree is higher than the second matching degree threshold (M > M2), the overall adaptability of the false target is already good. At this time, the frequency matching degree usually reaches a relatively high level (that is, the frequency characteristics of the false target have a high overlap with the enemy detection frequency band), and the problem is reflected in that the trajectory characteristics are easy to be recognized. The enemy detection system distinguishes target trajectories through range resolution. If the false target trajectory is significantly different from the true target (high trajectory recognition degree), even if the frequency characteristics are adapted, it will still be detected by the enemy through trajectory analysis. At this time, focusing on optimizing the trajectory characteristics (such as smoothing the trajectory through trajectory correction parameters) can reduce the distinguishability between the false target trajectory and the true target, reduce the probability of being detected by the enemy recognition algorithm, further improve the concealment and effectiveness of the interference, and avoid the problem of frequency adaptation but trajectory exposure.

[0053] When the comprehensive matching degree is higher than the second matching degree threshold (M > M2), calculate the difference Trajectory correction parameters (such as smoothing coefficients) are generated based on the difference. The correction parameters are then used to smooth the motion trajectory data (such as position and velocity) of the initial false target.

[0054] In this embodiment, when the overall matching degree is between the first matching degree threshold and the second matching degree threshold (M1≤M≤M2), the adjustment ratio coefficient k is calculated based on the formula for calculating the adjustment ratio coefficient. The formula for calculating the adjustment ratio coefficient is as follows: Dynamically allocate frequency adjustment weights (w) f =1-k) and trajectory adjustment weights (w) t =k): The closer M is to M1, the higher the frequency weight; the closer it is to M2, the higher the trajectory weight. After adjusting the frequency parameters and trajectory data according to their weights, they are fused to generate a new false target.

[0055] In this embodiment, dual-dimensional collaborative optimization is achieved through dynamic weight allocation, avoiding the limitations of single adjustment, and enabling the false target to adapt to the enemy's detection characteristics in both frequency and trajectory, thus covering the interference requirements of intermediate states.

[0056] As can be seen from the above, this embodiment adopts a differentiated adjustment strategy based on the comparison of comprehensive matching degree and threshold, targeting different adaptation states. When the comprehensive matching degree is lower than the first threshold, the frequency parameter is adjusted first to solve the problem of insufficient adaptation; when it is higher than the second threshold, the trajectory features are optimized to reduce the probability of being identified; when it is between the two thresholds, the weights are dynamically allocated through a proportional coefficient to achieve balanced optimization of frequency and trajectory. This precise and hierarchical adjustment logic avoids the limitations of a single fixed parameter, enabling the false target to adapt to the enemy's detection characteristics in real time, greatly improving realism, effectively confusing the enemy's identification, and enhancing the concealment effect and interference stability of the real target.

[0057] In one embodiment of this application, before calculating the frequency matching degree between the frequency component data of the true target and the operating frequency range of the enemy detection system, the method further includes: Acquire real-time operating parameters of the enemy detection system, including operating frequency jump patterns, pulse repetition period, beam scanning period, and dynamic range of range resolution; An enemy detection system feature library is established based on real-time operating parameters. Time-series analysis is performed on the parameters in the feature library to predict the operating frequency range and range resolution change trend of the enemy detection system in the current time period. The preset acquisition period for collecting frequency component data of the true target is determined based on the operating frequency range of the enemy detection system within the current time period; the preset time interval is determined based on the change trend of the distance resolution of the enemy detection system within the current time period.

[0058] In this embodiment, real-time operating parameters are core technical parameters of the enemy detection system acquired in real time through reconnaissance methods. These include the operating frequency jump pattern (frequency change pattern over time), pulse repetition period (time interval between transmitted pulses), beam scanning period (time for one complete antenna beam scan), and dynamic range of range resolution (range of fluctuation in range resolution), reflecting the real-time status of the enemy detection system. The enemy detection system feature database is a database storing historical and real-time operating parameters of the enemy detection system. It is used to accumulate and analyze the patterns of enemy detection characteristics, providing a data foundation for parameter prediction. The preset acquisition period is the time interval used to acquire true target frequency component data. It is set based on the changing trend of the enemy's operating frequency range to ensure timely capture of dynamic changes in frequency characteristics. The preset time interval is the time interval for acquiring distance values ​​at adjacent moments. It is set based on the changing trend of the enemy's range resolution to ensure that the trajectory data accurately reflects the target's motion state.

[0059] In this embodiment, real-time operating parameters of the enemy detection system can be intercepted using signal reconnaissance equipment, including the operating frequency jump pattern, pulse repetition period, beam scanning period, and dynamic range of range resolution, comprehensively capturing the dynamic characteristics of the enemy detection. Secondly, an enemy detection system feature library is established based on these real-time parameters. Time-series analysis algorithms (such as time series prediction models) are used to model the parameter change trends in the feature library, predicting the fluctuation range of the enemy's operating frequency range and the magnitude of range resolution changes within the current time period. Finally, key acquisition parameters are dynamically adjusted based on the prediction results: a preset acquisition period for the true target frequency component data is determined based on the changing trend of the operating frequency range (shortening the period if the frequency jumps quickly), and a preset time interval for acquiring distance values ​​is determined based on the changing trend of range resolution (reducing the interval if the resolution is high), ensuring that the acquired data accurately reflects the enemy's detection characteristics and providing high-quality input for subsequent calculations of frequency matching and trajectory identification.

[0060] In this embodiment, signal reconnaissance equipment (such as electronic warfare receivers and spectrum analyzers) can be deployed to intercept and analyze electromagnetic signals emitted by enemy detection systems (such as radar), and extract real-time operating parameters, including: The operating frequency jump pattern is recorded by analyzing the frequency jump sequence of the enemy detection signal over time (such as jump intervals and the set of jump frequency points). Pulse repetition period is the time interval between pulses transmitted by the enemy's detection signal (e.g., the average interval of 10 consecutive pulses). Beam scanning period is the time it takes for the enemy antenna beam to complete one omnidirectional scan by analyzing changes in signal strength. The dynamic range of distance resolution is calculated and recorded based on the signal bandwidth, representing the fluctuation range of distance resolution.

[0061] Based on the aforementioned real-time parameters, an enemy detection system feature library is constructed, storing parameter sequences by timestamp (e.g., recording frequency, resolution, and other parameters every 100ms). Temporal analysis algorithms (such as ARIMA models and LSTM neural networks) are used to perform trend analysis on the parameters in the feature library, uncovering the periodicity and correlation of parameter changes: predicting the possible range of the enemy's operating frequency within the current time period (e.g., the next 5 minutes) [f] pred-min ,f pred-max Predict the trend of changes in distance resolution (such as whether the resolution gradually increases or decreases) and the stable range.

[0062] The preset acquisition period for true target frequency component data is determined based on the predicted operating frequency range: if the enemy frequency jumps frequently (e.g., jump interval < 1s), the acquisition period is set to 0.5s (to ensure capture of each jump); if the frequency is stable (jump interval > 10s), the period is set to 5s to reduce data redundancy. The preset time interval is determined based on the predicted range resolution change trend: if the enemy range resolution increases (e.g., ... If the resolution is reduced from 50m to 10m, the time interval for obtaining distance values ​​will be shortened from 2s to 0.5s (to ensure that the difference between adjacent distances can be distinguished by the resolution); if the resolution is reduced, the interval will be extended to 5s.

[0063] As can be seen from the above, this embodiment acquires the real-time operating parameters of the enemy detection system and establishes a feature library. Through time-series analysis, it predicts the changing trends of the operating frequency range and range resolution, and dynamically determines the preset acquisition period for the frequency component data of the true target and the preset time interval for the range values. This process ensures that the acquired data can accurately adapt to the dynamic changes in the enemy detection characteristics, providing a high-quality data foundation for the accurate calculation of subsequent frequency matching degree and trajectory identification degree. It avoids acquisition lag or redundancy problems caused by fixed parameters, and improves the targeting and effectiveness of false target interference from the source.

[0064] In one embodiment of this application, determining a preset time interval based on the trend of range resolution change of the enemy detection system within the current time period includes: Based on historical change data of range resolution in the enemy detection system feature database, calculate the rate of change of range resolution per unit time. When the rate of change of distance resolution is greater than or equal to a preset rate of change threshold, the preset time interval is set to the first interval value; when the rate of change of distance resolution is less than the preset rate of change threshold, the preset time interval is set to the second interval value; wherein, the first interval value is less than the second interval value.

[0065] In this embodiment, distance resolution recording data within a preset time period (such as the last 5 minutes) can be retrieved from the enemy detection system feature library. This data is stored in order according to timestamps and includes the specific distance resolution values ​​and corresponding time points at each moment.

[0066] Calculate the rate of change of distance resolution per unit time based on historical data: Select distance resolution values ​​at adjacent time points. and Calculate the time difference .

[0067] Through formula Calculate the rate of change for a single segment, and take the average of the rates of change for multiple segments as the rate of change k of the distance resolution for the current time period. avg .

[0068] A preset rate of change threshold (e.g., k0 = 5 m / s) will be used to calculate k. avg Compared with the threshold, if k avg For distances ≥ k0 (where the distance resolution changes rapidly, such as dropping quickly from 30m to 10m), set the preset time interval to the first interval value (e.g., 0.5s) to ensure timely capture of trajectory detail changes; if k avg <k0 (distance resolution changes gradually, such as stabilizing at around 20m), set the preset time interval to the second interval value (such as 2s) to reduce redundant acquisition while ensuring the validity of trajectory data.

[0069] As can be seen from the above, this embodiment dynamically adjusts the preset time interval based on the rate of change of distance resolution. Small intervals are used when the resolution changes rapidly to ensure accurate trajectory data, while large intervals are used when the resolution is relatively flat to reduce redundancy. This ensures both the timeliness and accuracy of trajectory feature capture and reduces the data acquisition load, thereby improving the adaptability and efficiency of false target trajectory simulation.

[0070] In one embodiment of this application, after adjusting the initial false target based on the comprehensive matching degree to obtain a new false target, and after interfering with the new false target, the method further includes: Acquire target detection data of the enemy detection system before and after jamming. The target detection data includes the number of detected targets, frequency characteristic data of each target, trajectory parameters and target identification results. The interference effectiveness index is calculated based on target detection data. The interference effectiveness index includes the false target identification confusion rate, the real target concealment success rate, and the multi-target discrimination interference rate. Among them, the false target identification confusion rate is the ratio of the number of false targets misidentified as real targets by the enemy detection system to the total number of false targets; the real target concealment success rate is the ratio of the number of times the enemy detection system fails to correctly identify real targets after interference to the total number of detections; and the multi-target discrimination interference rate is the ratio of the number of target pairs that the enemy detection system cannot distinguish between real and false targets to the total number of real and false target pairs. When any of the interference effectiveness indicators fails to reach the preset interference effect threshold, the feedback adjustment direction is determined based on the interference effectiveness indicator that fails to meet the threshold. The overall matching degree is updated based on the feedback adjustment direction to obtain a new overall matching degree. The initial false target is then adjusted based on the new overall matching degree to obtain a new false target.

[0071] In this embodiment, target detection data refers to the original target-related information output by the enemy detection system before and after jamming, including the number of detected targets, frequency characteristic data of each target (such as frequency component distribution), trajectory parameters (such as position and velocity), and target identification results (such as whether it is a real target or a false target). This data forms the basis for evaluating the jamming effect. The jamming effectiveness index represents a set of parameters for assessing the jamming effect, including the false target identification confusion rate, the real target concealment success rate, and the multi-target discrimination jamming rate. These indicators reflect the actual effectiveness of false target jamming from different dimensions. The false target identification confusion rate is the ratio of the number of false targets misidentified as real targets by the enemy detection system to the total number of false targets. A higher ratio indicates stronger deception by the false targets. The real target concealment success rate is the ratio of the number of times the enemy detection system fails to correctly identify real targets after jamming to the total number of detections. A higher ratio indicates better protection of real targets. The multi-target discrimination jamming rate is the ratio of the number of target pairs that the enemy detection system cannot distinguish between real and false targets to the total number of real and false target pairs. A higher ratio indicates a higher degree of confusion between real and false targets and a better jamming effect. The feedback adjustment direction is the direction of pseudo-target optimization (such as frequency adjustment, trajectory correction, etc.) determined based on the type of non-compliant indicator when the interference effectiveness indicator fails to meet the standard.

[0072] In this embodiment, before the new false target interference is implemented, the original detection data of the enemy detection system on the real target is collected by the signal receiving device, and the number of detected targets, the frequency characteristics of the real target (such as center frequency and frequency component distribution), trajectory parameters (such as distance and speed at different times) and target identification results (determined to be real targets) are recorded.

[0073] After the new decoy interference is implemented, target detection data of the enemy's detection system will be continuously collected in the same way, with a focus on recording the detection information of the newly added decoys (such as the number of decoys, the frequency characteristics of the decoys and trajectory parameters) and the enemy's identification results of all targets (the judgment of distinguishing between real and decoy targets).

[0074] False target identification confusion rate: The number of false targets that the enemy detection system misidentifies as real targets is counted and compared with the total number of false targets. The formula is: Confusion rate = Number of false targets misidentified as real targets / Total number of false targets.

[0075] True Target Concealment Success Rate: The number of times the enemy's detection system failed to correctly identify a true target after interference (such as misjudging a true target as a false target or failing to identify it), is compared with the total number of detections. The formula is: Concealment Success Rate = Number of times a true target was not correctly identified / Total number of detections.

[0076] Multi-target discrimination interference rate: The number of target pairs that the enemy detection system cannot distinguish between real and fake (e.g., real and fake targets are judged to be of the same type) is compared with the total number of real and fake target pairs. The formula is: Discrimination interference rate = Number of target pairs that cannot be distinguished / Total number of real and fake target pairs.

[0077] In this embodiment, preset thresholds are set for each effectiveness indicator (e.g., confusion rate ≥80%, concealment success rate ≥90%, interference resolution rate ≥70%), and the calculation results are compared with the thresholds: If the false target identification confusion rate does not meet the standard, the feedback adjustment direction is determined to improve the fidelity of the false target frequency features and update the weight of the frequency matching degree in the comprehensive matching degree. If the real target concealment success rate does not meet the standard, the adjustment direction is to optimize the similarity between the false target trajectory and the real target and update the calculation parameters of the trajectory recognition degree.

[0078] As can be seen from the above, this embodiment obtains target detection data before and after interference from the enemy's detection system, calculates effectiveness indicators such as the confusion rate of false target identification and the success rate of real target concealment, and accurately evaluates the interference effect. When the indicators fail to meet the standards, the overall matching degree is updated based on feedback to perform secondary optimization of the false target. This effectively solves the problem of fixed parameter interference being prone to failure, ensuring that the false target can continuously adapt to changes in enemy detection characteristics, and improving the deception effect of the false target and the concealment effect of the real target.

[0079] Corresponding to the false target interference method in the above embodiments, Figure 2 This is a structural block diagram of a decoy jamming system provided in one embodiment of this application. For ease of explanation, only the parts relevant to the embodiment of this application are shown. References Figure 2 The false target interference system 20 includes: a first calculation module 21, a second calculation module 22, a fusion module 23, and an adjustment module 24.

[0080] The first calculation module 21 is used to calculate the frequency matching degree between the frequency component data of the real target and the operating frequency range of the enemy detection system. The frequency matching degree is the proportion of the frequency component data of the real target falling within the operating frequency range of the enemy detection system. The frequency component data of the real target is collected based on a preset acquisition cycle. The second calculation module 22 is used to obtain the first distance value of the true target relative to the enemy detection system at a first preset time, and to obtain the second distance value of the true target relative to the enemy detection system at a second preset time based on a preset time interval; and to calculate the trajectory recognition degree of the true target in the enemy detection system based on the first distance value, the second distance value and the distance resolution; the distance resolution is the minimum distance interval that the enemy detection system can distinguish between two adjacent targets. The fusion module 23 is used to fuse the frequency matching degree and the trajectory identification degree to obtain the comprehensive matching degree; The adjustment module 24 is used to adjust the initial false target based on the comprehensive matching degree to obtain a new false target, and to perform interference based on the new false target; the initial false target is generated based on the frequency component data and motion trajectory data of the real target, and the motion trajectory data is obtained based on the first distance value and the second distance value.

[0081] In one embodiment of this application, the false target jamming system 20 further includes: a data acquisition module. Specifically used for: Obtain the time-domain signal reflected by the real target, and perform a Fourier transform on the time-domain signal to obtain the frequency-domain signal corresponding to the real target reflection signal; Feature extraction is performed on the frequency domain signal to obtain the frequency component data of the true target.

[0082] In one embodiment of this application, the fusion module 23 is specifically used for: The weights corresponding to frequency matching degree are determined based on the operating frequency range of the enemy detection system, and the weights corresponding to trajectory identification degree are determined based on the range resolution of the enemy detection system. The comprehensive matching degree is obtained by weighted fusion based on frequency matching degree, trajectory identification degree and their corresponding weights.

[0083] In one embodiment of this application, the adjustment module 24 is specifically used for: In response to the overall matching degree being less than the first matching degree threshold, the frequency offset of the initial false target is determined based on the deviation between the overall matching degree and the first matching degree threshold. The center frequency of the initial false target is adjusted based on the frequency offset and the center frequency of the true target to obtain a new false target. In response to the overall matching degree being greater than the second matching degree threshold, the trajectory correction parameter is determined based on the difference between the overall matching degree and the second matching degree threshold. The motion trajectory data of the initial false target is adjusted by the trajectory correction parameter to obtain a new false target. In response to a comprehensive matching degree greater than or equal to the first matching degree threshold and less than or equal to the second matching degree threshold, an adjustment ratio coefficient is calculated. The formula for calculating the adjustment ratio coefficient is as follows:

[0084] in, This indicates the adjustment ratio coefficient. Indicates the overall matching degree. This represents the first matching threshold. This represents the second matching threshold; The frequency adjustment weight and trajectory adjustment weight of the initial false target are determined based on the adjustment ratio coefficient; The frequency of the initial false target is adjusted based on the frequency adjustment weight, and the motion trajectory data of the initial false target is adjusted based on the trajectory adjustment weight to obtain a new false target.

[0085] In one embodiment of this application, the false target jamming system 20 further includes: a data acquisition module. Specifically used for: Acquire real-time operating parameters of the enemy detection system, including operating frequency jump patterns, pulse repetition period, beam scanning period, and dynamic range of range resolution; An enemy detection system feature library is established based on real-time operating parameters. Time-series analysis is performed on the parameters in the feature library to predict the operating frequency range and range resolution change trend of the enemy detection system in the current time period. The preset acquisition period for collecting frequency component data of the true target is determined based on the operating frequency range of the enemy detection system within the current time period; the preset time interval is determined based on the change trend of the distance resolution of the enemy detection system within the current time period.

[0086] In one embodiment of this application, the acquisition module is further used for: Based on historical change data of range resolution in the enemy detection system feature database, calculate the rate of change of range resolution per unit time. When the rate of change of distance resolution is greater than or equal to a preset rate of change threshold, the preset time interval is set to the first interval value; when the rate of change of distance resolution is less than the preset rate of change threshold, the preset time interval is set to the second interval value; wherein, the first interval value is less than the second interval value.

[0087] In one embodiment of this application, the adjustment module is further configured to: Acquire target detection data of the enemy detection system before and after jamming. The target detection data includes the number of detected targets, frequency characteristic data of each target, trajectory parameters and target identification results. The interference effectiveness index is calculated based on target detection data. The interference effectiveness index includes the false target identification confusion rate, the real target concealment success rate, and the multi-target discrimination interference rate. Among them, the false target identification confusion rate is the ratio of the number of false targets misidentified as real targets by the enemy detection system to the total number of false targets; the real target concealment success rate is the ratio of the number of times the enemy detection system fails to correctly identify real targets after interference to the total number of detections; and the multi-target discrimination interference rate is the ratio of the number of target pairs that the enemy detection system cannot distinguish between real and false targets to the total number of real and false target pairs. When any of the interference effectiveness indicators fails to reach the preset interference effect threshold, the feedback adjustment direction is determined based on the interference effectiveness indicator that fails to meet the threshold. The overall matching degree is updated based on the feedback adjustment direction to obtain a new overall matching degree. The initial false target is then adjusted based on the new overall matching degree to obtain a new false target.

[0088] See Figure 3 , Figure 3 This is a schematic block diagram of an electronic device provided according to an embodiment of this application. Figure 3 The electronic device 300 in this embodiment may include one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memories 304 store computer programs, including program instructions. The processors 301 execute the program instructions stored in the memories 304. Specifically, the processors 301 are configured to invoke the program instructions to perform the functions of the modules in the aforementioned system embodiments, for example... Figure 2 The functions of the first calculation module 21, the second calculation module 22, the fusion module 23, and the adjustment module 24 are shown.

[0089] It should be understood that, in the embodiments of this application, the processor 301 may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0090] Input device 302 may include a touchpad, a fingerprint sensor (for collecting the user's fingerprint information and fingerprint orientation information), a microphone, etc., and output device 303 may include a display (LCD, etc.), a speaker, etc.

[0091] The memory 304 may include read-only memory and random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include non-volatile random access memory. For example, the memory 304 may also store information such as frequency component data of the true target, a first matching degree threshold, and a second matching degree threshold.

[0092] In specific implementations, the processor 301, input device 302, and output device 303 described in the embodiments of this application can execute the implementation method described in the false target interference method provided in the embodiments of this application, or they can execute the implementation method of the electronic device described in the embodiments of this application, which will not be repeated here.

[0093] In another embodiment of this application, a computer-readable storage medium is provided. This computer-readable storage medium stores a computer program, which includes program instructions. When executed by a processor, the program instructions implement all or part of the processes in the methods described above. Alternatively, the computer program can instruct related hardware to implement these processes. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include any entity or system capable of carrying computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0094] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the foregoing embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the electronic device. Furthermore, the computer-readable storage medium can include both internal and external storage units of the electronic device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0095] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0096] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the electronic devices and units described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0097] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connections shown or discussed may be indirect coupling or communication connections through some interfaces or units, or they may be electrical, mechanical, or other forms of connection.

[0098] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of this application, depending on actual needs.

[0099] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0100] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for interfering with false targets, characterized in that, include: Calculate the frequency matching degree between the frequency component data of the true target and the operating frequency range of the enemy detection system, wherein the frequency matching degree is the proportion of the frequency component data of the true target falling within the operating frequency range of the enemy detection system; The frequency component data of the true target are collected based on a preset acquisition period; Obtain the first distance value of the true target relative to the enemy detection system at a first preset time, and obtain the second distance value of the true target relative to the enemy detection system at a second preset time based on a preset time interval; The trajectory identification degree of the true target in the enemy detection system is calculated based on the first distance value, the second distance value, and the distance resolution; The distance resolution is the minimum distance interval at which the enemy detection system can distinguish two adjacent targets; The frequency matching degree and the trajectory identification degree are fused to obtain the comprehensive matching degree; The initial false target is adjusted based on the comprehensive matching degree to obtain a new false target, and interference is performed based on the new false target; The initial false target is generated based on the frequency component data and motion trajectory data of the real target, and the motion trajectory data is obtained based on the first distance value and the second distance value.

2. The false target interference method as described in claim 1, characterized in that, The process of acquiring the frequency component data of the true target includes: The time-domain signal reflected by the real target is obtained, and the time-domain signal is subjected to Fourier transform to obtain the frequency-domain signal corresponding to the real target reflection signal; Feature extraction is performed on the frequency domain signal to obtain the frequency component data of the true target.

3. The false target interference method as described in claim 1, characterized in that, The process of fusing the frequency matching degree and the trajectory identification degree to obtain a comprehensive matching degree includes: The weight corresponding to the frequency matching degree is determined based on the operating frequency range of the enemy detection system, and the weight corresponding to the trajectory identification degree is determined based on the distance resolution of the enemy detection system. The comprehensive matching degree is obtained by weighted fusion based on the frequency matching degree, the trajectory identification degree and their corresponding weights.

4. The false target interference method as described in claim 1, characterized in that, The initial false targets are adjusted based on the comprehensive matching degree to obtain new false targets, including: In response to the overall matching degree being less than a first matching degree threshold, the frequency offset of the initial false target is determined based on the deviation between the overall matching degree and the first matching degree threshold. The center frequency of the initial false target is adjusted based on the frequency offset and the center frequency of the true target to obtain a new false target. In response to the overall matching degree being greater than the second matching degree threshold, a trajectory correction parameter is determined based on the difference between the overall matching degree and the second matching degree threshold. The motion trajectory data of the initial false target is adjusted using the trajectory correction parameter to obtain a new false target. In response to the overall matching degree being greater than or equal to a first matching degree threshold and less than or equal to a second matching degree threshold, an adjustment ratio coefficient is calculated, and the formula for calculating the adjustment ratio coefficient is as follows: in, This indicates the adjustment ratio coefficient. Indicates the overall matching degree. This represents the first matching threshold. This represents the second matching threshold; The frequency adjustment weight and trajectory adjustment weight of the initial false target are determined based on the adjustment ratio coefficient. The frequency of the initial false target is adjusted based on the frequency adjustment weight, and the motion trajectory data of the initial false target is adjusted based on the trajectory adjustment weight to obtain a new false target.

5. The false target interference method as described in claim 1, characterized in that, Before calculating the frequency matching degree between the frequency component data of the true target and the operating frequency range of the enemy detection system, the following steps are also included: Acquire real-time operating parameters of the enemy detection system, including operating frequency jump patterns, pulse repetition period, beam scanning period, and dynamic range of range resolution; Based on the real-time operating parameters, an enemy detection system feature library is established. The parameters in the feature library are analyzed over time to predict the operating frequency range and range resolution change trend of the enemy detection system in the current time period. The preset acquisition period for collecting frequency component data of the true target is determined based on the operating frequency range of the enemy detection system within the current time period; the preset time interval is determined based on the change trend of the distance resolution of the enemy detection system within the current time period.

6. The false target interference method as described in claim 5, characterized in that, The determination of the preset time interval based on the trend of distance resolution change of the enemy detection system within the current time period includes: Based on the historical change data of range resolution in the enemy detection system feature library, calculate the rate of change of range resolution per unit time; When the rate of change of distance resolution is greater than or equal to a preset rate of change threshold, the preset time interval is set to the first interval value; when the rate of change of distance resolution is less than the preset rate of change threshold, the preset time interval is set to the second interval value; wherein, the first interval value is less than the second interval value.

7. The false target interference method as described in claim 1, characterized in that, After adjusting the initial false target based on the comprehensive matching degree to obtain a new false target, and then applying interference based on the new false target, the process further includes: Acquire target detection data of the enemy detection system before and after interference. The target detection data includes the number of detected targets, frequency characteristic data of each target, trajectory parameters, and target identification results. The interference effectiveness index is calculated based on the target detection data. The interference effectiveness index includes the false target identification confusion rate, the true target concealment success rate, and the multi-target discrimination interference rate. Among them, the false target identification confusion rate is the ratio of the number of false targets misidentified as true targets by the enemy detection system to the total number of false targets; the true target concealment success rate is the ratio of the number of times the enemy detection system fails to correctly identify true targets after interference to the total number of detections; and the multi-target discrimination interference rate is the ratio of the number of target pairs that the enemy detection system cannot distinguish between true and false targets to the total number of true and false target pairs. When any of the interference effectiveness indicators fails to reach the preset interference effect threshold, the feedback adjustment direction is determined based on the interference effectiveness indicator that fails to meet the threshold. The overall matching degree is updated based on the feedback adjustment direction to obtain a new overall matching degree, and the initial false target is adjusted based on the new overall matching degree to obtain a new false target.

8. A decoy jamming system, characterized in that, include: The first calculation module is used to calculate the frequency matching degree between the frequency component data of the true target and the operating frequency range of the enemy detection system. The frequency matching degree is the proportion of the frequency component data of the true target falling within the operating frequency range of the enemy detection system. The frequency component data of the true target are collected based on a preset acquisition period; The second calculation module is used to obtain the first distance value of the real target relative to the enemy detection system at a first preset time, and to obtain the second distance value of the real target relative to the enemy detection system at a second preset time based on a preset time interval. The trajectory identification degree of the true target in the enemy detection system is calculated based on the first distance value, the second distance value, and the distance resolution; The distance resolution is the minimum distance interval at which the enemy detection system can distinguish two adjacent targets; The fusion module is used to fuse the frequency matching degree and the trajectory identification degree to obtain a comprehensive matching degree; An adjustment module is used to adjust the initial false target based on the comprehensive matching degree to obtain a new false target, and to perform interference based on the new false target; The initial false target is generated based on the frequency component data and motion trajectory data of the real target, and the motion trajectory data is obtained based on the first distance value and the second distance value.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

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