阅读说明：本技术 一种距离门拖引干扰的智能生成方法 (Intelligent generation method for distance gate dragging interference ) 是由 张天贤 魏雅琦 孔令讲 方学立 刘永坚 杨晓波 蔡光耀 王睿甲 于 2021-09-22 设计创作，主要内容包括：本发明公开一种距离门拖引干扰的智能生成方法,应用于电子对抗技术领域,针对传统的距离门拖引干扰方法忽略了多帧之间的信息互连,拖引方法不具灵活性,本发明提出了一种距离门拖引干扰的智能生成方法。本发明首先分析距离门拖引干扰过程建立距离门拖引干扰多帧优化模型；为了衡量距离门拖引干扰的效果,选择平均波门偏移距离作为目标函数；其次针对目标函数中间状态未知、涉及噪声的挑战,利用蒙特卡洛方法拟合目标函数；最后提出了基于改进粒子群算法的距离门拖引干扰优化方法。本发明提出的算法能够动态调整不同帧的拖引距离,最大化距离门拖引干扰的效果,在拖引成功率和拖引距离方面都优于传统方法。(The invention discloses an intelligent generation method of range gate dragging interference, which is applied to the technical field of electronic countermeasure and aims at the problems that the traditional range gate dragging interference method ignores information interconnection among multiple frames and the dragging method has no flexibility. Firstly, analyzing a range gate dragging interference process to establish a range gate dragging interference multi-frame optimization model; in order to measure the effect of the range gate dragging interference, the average wave gate offset distance is selected as a target function; secondly, aiming at the challenges that the intermediate state of the objective function is unknown and noise is involved, fitting the objective function by utilizing a Monte Carlo method; finally, a distance gate dragging interference optimization method based on the improved particle swarm optimization is provided. The algorithm provided by the invention can dynamically adjust the dragging distances of different frames, maximizes the effect of distance gate dragging interference, and is superior to the traditional method in the aspects of dragging success rate and dragging distance.)

1. An intelligent generation method of range gate dragging interference is characterized in that an application scene is as follows: in air-to-air wars, an jammer is tracked by a tracking radar of a fire control system, the method comprising the steps of:

s1, analyzing a distance gate towing interference process, and building a distance gate towing interference multi-frame design model; specifically, the method comprises the following steps: representing the dragging distance of the ith frame as the sum of dragging increments of the ith frame; echo signals received by the ith frame radarWill be affected by the i frame trailing delta;

s2, establishing an optimization problem considering both towing distance and towing success rate;

s3, solving the optimization problem in the step S2 based on the improved particle swarm optimization to obtain the dragging interference optimization method.

2. The method of claim 1, wherein the expression of step S1 for representing the pulling distance of the i-th frame as the sum of the pulling increments of the i-th frame is:

wherein R is_iRepresenting the towing distance, t, of the ith frame_iDelay time of ith frame, c represents light velocity, Δ t_iIncreased delay time for the ith frame compared to the (i-1) th frame,. DELTA.R_iIs the trailing delta for the ith frame.

3. The method of claim 2, wherein the ith frame radar is used for intelligently generating the range gate towing interferenceTo the received echo signalThe expression of (a) is:

wherein, w_i(t) represents noise generated inside the receiver.

4. The method of claim 2, wherein the optimization problem expression of step S2 is:

s.t.0≤△R_i≤△R_max

wherein, flag is a flag indicating whether the pulling interference is effective, flag is 1 indicating success, flag is 0 indicating failure, Δ R_NIndicates the increment of towing distance, Δ R, of the Nth frame_maxIndicating a single frame trailing maximum distance.

5. The method according to claim 2, wherein the step S3 specifically includes:

s31, initializing a population; determining a population scale M, initializing the position and the speed of each particle, calculating the fitness of each particle, and determining an individual optimal value and a global optimal value when the iteration number is 0;

s32, updating the population; updating the position and the speed of each particle in the population;

s33, B is carried out on each particle in the population₀The fitness is updated by secondary resampling;

s34, selecting the N particles with best performance in the population, and resampling each particle b_iThen, the fitness value is updated

S35, updating the individual optimal value and the global optimal value of the particles in the population;

and S36, outputting the optimal solution.

Technical Field

The invention belongs to the technical field of electronic countermeasure, and particularly relates to a distance gate dragging interference technology.

Background

With the development of information war, the position of radar electronic countermeasure technology in modern war is increasingly prominent. The range gate dragging interference is an important interference pattern for tracking an automatic range tracking system of the radar, and means that an interference machine acts on the radar by false range information to cause target parameter information detected by the radar to be inaccurate, so that the positioning and tracking of the radar to a target are further misled. The interference power utilization rate is high, the risk of being resisted is low, and the like, so that the interference power utilization rate becomes a hot point of research in recent years.

The document' analysis modeling and evaluation of distance wave gate dragging scheme [ J ]. systematic engineering and electronic technology, 2006,028(008): 1158-. The documents "Spectrum analysis of spatial range gate pull-off (RGPO) signals, 201523 and Signal Processing and Communications Applications reference (SIU),2015, pp.1026-1029" derive time and frequency domain expressions for linear and parabolic range gate-pulled interference signals, providing the basis and necessary parameter information for better performing deceptive interference. However, the above work mainly focuses on the generation of interference signals in a single frame and the expression of time domain and frequency domain, neglects the information interconnection among multiple frames, and does not deeply dig the relation between frames. It can be easily understood that if the towing speed is too fast, especially in the initial stage of towing, the radar tracking system of the enemy cannot respond to the change in time, which may cause the failure of towing interference; if the towing speed is too slow, it is also easily locked by the enemy, and the towing disturbance will also fail. Therefore, the mutual cooperation and linkage between frames can affect the effect of dragging interference. In order to improve interference efficiency, it is necessary to design and optimize a range gate towed interference scheme.

Disclosure of Invention

In order to solve the technical problem, the invention provides an intelligent generation method of the distance gate dragging interference, which considers the problem of information interconnection among multiple frames and maximizes the distance gate dragging interference effect by reasonably configuring the dragging distance of each frame.

The technical scheme adopted by the invention is as follows: an intelligent generation method of range gate dragging interference is provided, and the application scene is as follows: in air-to-air wars, an jammer is tracked by a tracking radar of a fire control system, the method comprising the steps of:

s1, analyzing a distance gate towing interference process, and building a distance gate towing interference multi-frame design model; specifically, the method comprises the following steps: representing the dragging distance of the ith frame as the sum of dragging increments of the ith frame; echo signals received by the ith frame radarWill be affected by the i frame trailing delta;

s2, establishing an optimization problem considering both towing distance and towing success rate;

s3, solving the optimization problem in the step S2 based on the improved particle swarm optimization to obtain the dragging interference optimization method.

The expression for expressing the dragging distance of the ith frame as the sum of the dragging increments of the ith frame in step S1 is:

wherein R is_iRepresenting the towing distance, t, of the ith frame_iDelay time of ith frame, c represents light velocity, Δ t_iIncreased delay time for the ith frame compared to the (i-1) th frame,. DELTA.R_iIs the trailing delta for the ith frame.

Echo signal received by ith frame radarExpression (2)Comprises the following steps:

wherein, w_i(t) represents noise generated inside the receiver.

The optimization problem expression described in step S2 is:

s.t.0≤△R_i≤△R_max

wherein, flag is a flag indicating whether the pulling interference is effective, flag is 1 indicating success, flag is 0 indicating failure, Δ R_NIndicates the increment of towing distance, Δ R, of the Nth frame_maxIndicating a single frame trailing maximum distance.

Step S3 specifically includes:

s31, initializing a population; determining the population scale M, initializing the position and the speed of each particle, calculating the fitness of each particle, and determining the individual optimal value and the global optimal value when the iteration number is 0.

S32, updating the population; updating the position and the speed of each particle in the population;

s33, B is carried out on each particle in the population₀The fitness is updated by secondary resampling;

s34, selecting the N particles with best performance in the population, and resampling each particle b_iThen, the fitness value is updated

S35, updating the individual optimal value and the global optimal value of the particles in the population;

and S36, outputting the optimal solution.

The invention has the beneficial effects that: the invention realizes better interference effect by considering the coordination between frames; firstly, analyzing a range gate dragging interference process to establish a range gate dragging interference multi-frame optimization model; in order to measure the effect of the range gate dragging interference, the average wave gate offset distance is selected as a target function; secondly, aiming at the challenges that the intermediate state of the objective function is unknown and noise is involved, fitting the objective function by utilizing a Monte Carlo method; finally, a distance gate dragging interference optimization method based on an improved particle swarm algorithm is provided; the method has the advantages that the radar signal processing flow is considered, the effect of the distance gate dragging interference is maximized through the matching between frames, and the method is superior to the traditional method in the aspects of dragging success rate and dragging distance; the invention can be applied to the fields of electronic countermeasure and the like.

Drawings

FIG. 1 is a schematic diagram of a scenario in which a single jammer is tracked by a tracking radar in an embodiment of the present invention.

Fig. 2 is a diagram of a range gate towed interference process used in an embodiment of the present invention.

Fig. 3 is a simplified block diagram of an embodiment of the present invention employing a radar system.

FIG. 4 is a drawing increment comparison diagram of the method of the present invention and the traditional constant speed drawing and uniform acceleration drawing.

Fig. 5 is a drawing distance comparison diagram of the method of the invention and the traditional drawing method of uniform speed drawing and uniform acceleration drawing.

Fig. 6 is a comparison diagram of the CDF of the constant speed towing and the constant acceleration towing method according to the embodiment of the present invention.

Detailed Description

The invention mainly adopts a computer simulation experiment method for verification, and all the steps and conclusions are verified to be correct on Matlab 2014.

The specific implementation process of the invention is as follows:

step 1: establishing a scene graph tracked by a tracking radar of a fire control system by an interference machine in air-to-air wars, and initializing system parameters, wherein the scene graph mainly comprises the following steps: initial positions of the radar and the jammer, a movement mode of the jammer and the like;

FIG. 1 shows a scene graph of a single jammer tracked by a tracking radar, in this embodiment, the radar is located at the origin of a rectangular coordinate system, and the jammer is interferedThe machine does uniform linear motion far away from the radar in a two-dimensional plane. The state vector of the jammer can be expressed asThe initial state is X (1) ([ 50km 50m/s 55km 350 m/s)]'. Following the equation of motion of

X(k+1)＝F(k)X(k)+Γ(k)v(k) (1)

Wherein x is_k、y_k、F (k) represents a state transition matrix as shown in formula (2), and f (k) represents the x-direction position, x-direction velocity, y-direction position, and y-direction velocity at time k, respectively; t is revisit interval time, and T is 1 s; Γ (k) represents the process noise distribution matrix, as shown in equation (3); v (k) represents the process noise component.

Step 2: analyzing a distance gate dragging interference process, and building a distance gate dragging interference multi-frame design model;

as shown in fig. 2, range gate towed interference contains three phases: capture, drag, close. 0-T₁For the acquisition phase, T₁-T₂For towing stage, T₂-T₃A stage of closing the interference machine; as shown in FIG. 2T₁<T₂<T₃. In the dragging period, the delay time of the forwarded pulse is gradually increased every time the jammer intercepts a radar irradiation signal, so that the range gate gradually leaves the target echo along with the movement of the interference signal until the range gate deviates from the target echo by a preset distance. Due to the fact thatHere, the towing process is a discrete process.

Suppose at time T₁-T₂The total frame number of the internal dragging is N, and the delay time of the ith frame is t_iThen the dragging distance R of the ith frame_i

Wherein c is 3 × 10⁸m/s, representing the speed of light,. DELTA.t_iThe delay time added for the ith frame over the ith-1 frame. Delta R_iFor the trailing increment of the ith frame, at_iThe corresponding distance. It can be seen that the drag distance of the ith frame can be expressed as the sum of the i frame drag increments. In a polar coordinate system, the position r of the ith frame real target is recorded_i，r_iThe angle between the positive direction of x and the positive direction of x is theta_iThen the horizontal and vertical coordinate x of the true target can be obtained_iAnd y_i. Similarly, the abscissa and ordinate of the decoy generated by RGPO can be obtainedAnd

as shown in FIG. 3, assume that the radar-transmitted signal is a chirp signal s_t(t), then the echo signal received by the i-th frame radarThe mathematical expression is

Wherein the content of the first and second substances,is the distance of a false target interfering with the manufacture, equal to the distance r of a true target_iPlus a towing distance R_i；w_i(t) representsNoise generated inside the receiver is mainly thermal noise and is white noise of zero-mean complex Gaussian; t represents a time variable. It follows that each Δ R of the towing phase_kWill affect the interfering signal and produce different interference effects.

After receiving the signal, the radar side mainly detects the target through the steps of pulse compression, moving target detection, constant false alarm detection and the like to obtain information such as the distance, the speed, the direction and the like of the target; and then, carrying out correlation processing on the trace points after signal processing, and estimating and stably tracking the motion parameters of the next frame of the target. Ideally, the measurement value of the true and False target of each frame can be obtained after CFAR (Constant False-Alarm Rate). Without loss of generality, a radar party performs target tracking by adopting a Kalman filter based on probability data interconnection. The probability data interconnection filter firstly calculates the probability that the current frame different measurements come from the target according to all the measurement values in the wave gate, and then carries out weighting estimation on the state of the target.

And step 3: selecting the average wave gate offset distance as a target function, and establishing a range gate dragging interference method optimization problem by combining with an interference machine dragging distance constraint condition;

the RGPO dragging stage has the task of keeping the distance from the wave gate away from a real target, and the larger the offset distance of the wave gate is, the better the interference effect is, so that the own party can be protected better. However, for the jammer, according to the radar signal processing flow described in step 2, it is unknown whether the radar can be successfully attracted to track the false target. Therefore, the mathematical expectation is taken here, and the average gate offset distance is taken as an optimization target to establish an optimization model. Therefore, the optimization problem of the above-mentioned distance gate dragging interference method is modeled as

Wherein E (-) represents the mathematical expectation, r_NRepresenting the true target position of the nth frame of the towing phase,representing the centre of the wave gate, Δ R, of the Nth frame_maxThe maximum dragging distance of a single frame.

Due to the influence of factors which cannot be accurately obtained in the received signal processing flow by the radar, the interference party cannot specifically obtain the real target position and the predicted position of the wave gate center. The dragging distance R of the ith frame can be known by the flow analysis of the step 2_iThe larger the distance between the true and false targets is, the larger the distance between the center of the wave gate and the true target is. The problem of maximizing the offset distance of the wave gate can be indirectly converted into the problem of maximizing the dragging distance, and the process of solving the real target position and the predicted position of the wave gate center is avoided. A Monte Carlo method of a reward mechanism combined with dragging interference success is considered to fit an objective function, and dragging distance and dragging success rate are considered to a certain extent, so that a more effective range gate dragging interference method is obtained.

Thus, in this embodiment, the optimization problem is converted into the following expression:

wherein, the flag is a flag indicating whether the dragging interference is effective, and the flag is 1 to indicate success and 0 to indicate failure; delta R_NRepresenting the towing distance increment for the nth frame.

And 4, step 4: and solving the optimization problem to obtain the optimization method of the range gate dragging interference.

Since the optimization problem of the formula (10) is a multi-frame joint optimization problem, the objective function is difficult to directly express by using an analytical expression and the whole process is influenced by noise. Therefore, the method of the invention provides the optimization problem to be solved based on the improved particle swarm optimization.

The particle swarm PSO optimization algorithm is an intelligent algorithm which is proposed by being inspired by a swarm theory and a social model, and particles of the PSO optimization algorithm represent a potential solution of an n-dimensional optimization problem. The position of each particle is a pending optimization method, namely a pending distance gate pull interference method. Taking the ith particle as an example, its position can be expressed as

Due to its simple and fast convergence characteristics, PSO is widely used to solve the problem of high optimization dimensionality. However, when the optimization problem is affected by noise, its performance may drop significantly, leading to inaccurate and uncertain information such as measurement errors. In this case, the true objective function value of the solution is also disturbed by noise. Resampling is one method to mitigate the effects of noise. It is clear that the more re-estimation of a solution, the more accurate the estimated value of the corresponding objective function and the higher the computational cost. The computational budget is defined by the total number of evaluations of the objective function. Consider that in situations where computational budget is limited, the evaluation is distributed among solutions using PSO-ERN. The method first performs B on the target values of all solutions in the cluster₀sub-Equal Resampling (ER) and then immediately distributing the additional re-evaluation b between top-N solutions_iGeneral case B₀Is less than b_i. This allows for the mass of the particles, which reduces unnecessary resampling and computation costs. Then, PSO operation is operated as usual, and when the iteration times are met, a towed interference optimization method gbest is output_tmax. Since the intermediate state is unknown, the objective function is difficult to represent with analytical expressions, and the Monte Carlo method can be adopted to fit the function. The improved PSO algorithm is referred to herein as MC-PSO-ERN, and the complete algorithm flow is shown in Table 1.

TABLE 1 MC-PSO-ERN Algorithm

The fitness in this embodiment is the value of the objective function,the result of the objective function is brought in for the solution. This value is now resampled by resamplingAnd newly calculating to obtain a new fitness value.

Optimal solution gbest of final output_tmaxI.e. the global optimum.

The solution of the invention is further verified below according to a Matlab simulation example.

The corresponding values of the radar, interference and particle swarm algorithm related parameters are shown in table 2.

TABLE 2 System parameters

Parameter(s)	(symbol)	Numerical value
			Initial position of jammer		(50km,55km)
Initial speed of jammer		(50m/s,350m/s)
			Number of interference frames		30
False alarm rate		10-6
			Revisit time interval	T	1s
Distance dimension measurement noise variance		100
			Direction dimension measurement noise variance		0.12
Maximum distance of single frame dragging	△Rmax	150m
			Maximum value of inertial weight	ωmax	0.9
Minimum value of inertial weight	ωmin	0.5
			Top N		4
Calculating amount	B0	1000
			Additional quota	bi	2000
Maximum number of iterations of particle swarm	tmax	80
			Particle swarm size	M	60
Learning factor	c1And c2	2，2

FIG. 4 is a drawing increment comparison diagram of the method of the present invention and the traditional constant speed drawing and uniform acceleration drawing. Fig. 5 is a drawing distance comparison diagram of the method of the invention and the traditional drawing method of uniform speed drawing and uniform acceleration drawing. As can be seen from fig. 4 and 5, the method provided by the present invention can realize the mutual matching between frames by adjusting the towing distance of different detection frame numbers, and the flexibility of uniform speed towing and uniform acceleration towing is low, and only the towing interference can be realized according to the given parameters, thereby embodying the effectiveness of the present invention.

Fig. 6 is a comparison graph of CDF (cumulative distribution function) of the uniform speed towing and uniform acceleration towing methods according to the embodiment of the present invention. As can be seen from fig. 6, the method proposed by the present invention has advantages over some conventional towing schemes in terms of both towing distance and towing success rate.

It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.