CN-122018527-A - Multi-machine formation cooperative spoofing interference optimization method based on genetic algorithm

CN122018527ACN 122018527 ACN122018527 ACN 122018527ACN-122018527-A

Abstract

The invention discloses a multi-machine formation cooperative spoofing interference optimization method based on a genetic algorithm, and belongs to the technical field of electronic countermeasure. Aiming at the problems that false targets generated by multi-aircraft spoofing interference are discrete and are easy to identify and filter by a radar network in the prior art, a system model comprising aircraft formation, radar networking and a flight area is firstly established, then a collaborative interference evaluation model is established, space distances between false target pairs generated by different aircrafts on different radars are calculated and compared with radar resolution threshold values to quantify formation interference effectiveness, finally the evaluation model is used as an objective function, iterative optimization is carried out on the space positions of the multiple aircrafts by utilizing a genetic algorithm, and an aircraft formation scheme capable of enabling the false targets to coincide with each other to the greatest extent in space is obtained by searching. The method can automatically generate the optimized airplane formation position, and remarkably improves the cooperative deception jamming efficiency of the multi-airplane formation on the radar network.

Inventors

CHEN CHUAN
XU HAIXIN
XU RUI
WANG XIAOJUN
Hui Peiyu
HU WEI
JIA YONG
YIN LU
YANG LI

Assignees

成都理工大学

Dates

Publication Date: 20260512
Application Date: 20260413

Claims (10)

1. The multi-machine formation cooperative spoofing interference optimization method based on the genetic algorithm is characterized by comprising the following steps of: Step S1, establishing a system model and defining parameters: Definition includes Forming a queue of aircraft, wherein the position coordinate set of the queue is 1,2,..., ; Definition includes Radar networking of partial radar, its position coordinate set is = 1,2,..., ; Define the flight area Ω, Ω= ; Wherein, the Represent the first An abscissa of the aircraft; Represent the first An ordinate of the aircraft; representing an aircraft index; Representing a radar index; representing plane abscissa variables; representing a plane ordinate variable; Representing a lower abscissa of the flight area; representing the upper bound of the abscissa of the flight area; representing a lower ordinate bound of the flight area; Representing the upper bound of the ordinate of the flight area; s2, constructing a cooperative interference assessment model: For each aircraft And each radar Generating a group of false targets according to the relative position relation: ; Wherein, the Representation for aircraft And radar The generated false target set; Representing a first false object; Representing a fourth false object; Calculating the difference between different airplanes , ) Aiming at different radars , ) The distance between the generated false object pairs; Wherein, the Represent the first Erecting an airplane; Represent the first Erecting an airplane; Represent the first A partial radar; Represent the first A partial radar; The subscripts a, b refer to the subscripts 1,2,..., Two aircraft a and b inside; The subscript c, d denotes that it belongs to 1,2,..., Two radars c and d inside; When the distance is less than or equal to the radar resolution threshold When the cooperative interference is judged to be effective at one time; Constructing an objective function C by using the effective cooperative interference total number of all radar pairwise combinations and all aircraft pairwise combinations as an index for evaluating formation interference effectiveness; step S3, formation position optimization is carried out based on a genetic algorithm: Taking the objective function C as a fitness function, and The coordinate code of the aircraft is a chromosome, and the population is initialized in the flight area omega; and iteratively optimizing the population through the operations of selection, crossing and mutation of a genetic algorithm until the termination condition is met, and outputting an aircraft coordinate set with the maximum objective function C as an optimal collaborative interference formation scheme.
2. The genetic algorithm-based multi-aircraft formation co-spoofing interference optimization method of claim 1, wherein in step S2, the algorithm is performed for each aircraft And each radar Generating a set of false targets The method of (1) is as follows: In an aircraft And radar On the line of the aircraft Along the left and right sides of the radar Directional airplane Is equally spaced in the unit vector direction of (a) Generating K false targets in a distribution way; Wherein, the Representing the separation distance between false targets, and K representing the number of false targets.
3. The genetic algorithm-based multi-machine formation co-spoofing interference optimization method of claim 1, wherein the false target The first of (3) Coordinates of the individual false objects: ; Wherein, the Representing an aircraft And radar The generated false target set; representation for aircraft And radar Generated first A false target coordinate; Represent the first The position of the aircraft; Represent the first The direction coefficients of the false targets; Representing the spacing of adjacent false objects; Is radar Directional airplane Is a unit vector of (a).
4. The genetic algorithm-based multi-machine formation co-spoofing interference optimization method of claim 1, wherein in step S2, the expression of the objective function C is: = ; Wherein, the Representing an indicator function when its argument Not greater than Taking 1 if not, taking 0 if not; Wherein, the method comprises the steps of, Representing the separation between decoys when the distance between two targets is less than or equal to the radar resolution threshold When the radar networking is judged to be a cooperative target; Wherein, the Represent the first A partial radar index; Represent the first A partial radar index; Represent the first The index of the aircraft is set up, Represent the first Indexing the aircraft; Represent the first A false target index is used to indicate the number of false target indexes, Represent the first A false target index; Representing an aircraft And radar Generated first A false target; Representing an aircraft And radar Generated first And a false target.
5. The genetic algorithm-based multi-machine formation co-spoofing interference optimization method of claim 1, wherein in step S3, the step of The coordinates of each aircraft are encoded into a chromosome, specifically, the two-dimensional coordinates of each aircraft are encoded Respectively converting the two binary strings into binary strings, and sequentially splicing the binary strings of all the airplanes to form a chromosome.
6. The method for optimizing multi-machine formation co-spoofing interference based on the genetic algorithm of claim 1, wherein in step S3, the parameters of the genetic algorithm include population size popsize, maximum number of iterations, crossover probability and mutation probability.
7. The genetic algorithm-based multi-machine formation co-spoofing interference optimizing method of claim 1, wherein said step S3 includes the sub-steps of: S31, coding, namely two-dimensional coordinates of each aircraft Respectively converting the two binary strings into binary strings, and sequentially splicing the binary strings of all the airplanes to form a chromosome; S32, initializing, namely randomly generating an initial population with a preset scale popsize in a flight area omega; S33, evaluating, namely decoding each chromosome to obtain corresponding aircraft coordinates, and calling an objective function C constructed in the step S2 to calculate the fitness value of the current formation scheme; s34, performing selection operation according to the fitness value, and performing crossover and mutation operation on the selected individuals to generate a new generation population; And S35, iterating, namely repeating the steps S33 and S34 until the maximum iteration times are reached, and outputting an airplane coordinate set corresponding to the chromosome with the maximum fitness value as an optimal collaborative interference formation scheme.
8. The method for optimizing multi-aircraft formation co-spoofing interference based on genetic algorithm as recited in claim 7, wherein in said step S31, the two-dimensional coordinates of each aircraft are converted into binary strings by linearly normalizing the coordinates to an interval of [0,1], multiplying by ] ) And rounding to obtain a quantized integer, and converting the quantized integer into a bit binary string, wherein bits are preset coding bits.
9. The genetic algorithm-based multi-machine formation co-spoofing interference optimization method of claim 7, wherein in step S34, the selecting operation employs a tournament selection method, the crossing operation employs a single point crossing, and the crossing probability is The mutation operation adopts bit-by-bit mutation, and the mutation probability is that 。
10. The genetic algorithm-based multi-aircraft formation co-spoofing interference optimization method of any one of claims 1 to 9, further comprising a step S4 of generating a co-interference control instruction according to the optimal co-interference formation scheme and transmitting the control instruction to each aircraft in the formation, wherein the control instruction is used for controlling the on-board jammer to radiate a spoofing interference signal according to the false target parameter generated in step S2.

Description

Multi-machine formation cooperative spoofing interference optimization method based on genetic algorithm Technical Field The invention relates to the technical field of radar electronic countermeasure and intelligent optimization intersection, in particular to a multi-machine formation cooperative spoofing interference optimization method based on a genetic algorithm. Background In the field of radar electronic countermeasure, multi-machine cooperative spoofing is an important means for improving survival and sudden defense capabilities of aircraft formation. The existing typical multi-aircraft cooperative radar interference technology generally depends on that a plurality of aircrafts fly according to a pre-planned fixed formation (such as a line shape, a diamond shape and the like), and each aircrafts independently generates and radiates a spoofing interference signal according to the relative relation between the aircrafts and the radars, so that independent false target point tracks are formed on a display of each radar. However, this conventional approach has two prominent technical drawbacks: First, interference effect cooperativity is poor, and is easy to be recognized by an advanced radar network. Because each aircraft works independently, false targets generated by each aircraft are distributed discretely and unassociated in space and geographic positions. Modern radar networking systems commonly employ multi-site data fusion and intersection verification techniques, which can easily determine that these false points from different spatial locations cannot intersect on the same real target track, thereby identifying it as interference and filtering it. The root cause is that the prior art lacks a quantized cooperative interference effectiveness evaluation model taking a deception radar network as a core target, and can not guide an airplane formation to generate false targets which can lead multiple radars to generate consistent space misjudgment. Second, the formation strategy is stiff and cannot dynamically adapt to the battlefield environment. The preset fixed formation cannot be dynamically adjusted according to battlefield elements (such as radar station positions, number, resolution characteristics and own flight constraint areas) which change in real time, so that the adaptability and robustness of an interference strategy are insufficient. The core difficulty faced by the current technology is how to automatically plan the optimal multi-aircraft space position formation in real time under the constraint of a given battlefield environment (radar network and flight airspace), so that false target groups generated by all aircraft in the formation in a cooperative way can be maximally overlapped in space under the multi-view observation of radar networking, and thus the false target groups are misjudged as a large number or a plurality of real moving targets. Therefore, how to effectively deception the radar network, especially how to misjudge false targets generated by the cooperation of the aircraft clusters by multiple radars as real targets, is a technical problem to be solved in the current radar interference field. Disclosure of Invention In view of the above, the present invention aims to provide a genetic algorithm-based multi-machine formation co-spoofing interference optimization method, which is capable of implementing efficient searching for an optimal formation scheme in a complex constraint space by constructing a model capable of precisely quantitatively evaluating formation co-spoofing interference effectiveness. In order to achieve the above purpose, the present invention provides the following technical solutions: the invention provides a multi-machine formation cooperative spoofing interference optimization method based on a genetic algorithm, which comprises the following steps: Step S1, establishing a system model and defining parameters: Definition includes Forming a queue of aircraft, wherein the position coordinate set of the queue is1,2,...,; Definition includesRadar networking of partial radar, its position coordinate set is=1,2,...,; Define the flight area Ω, Ω=; Wherein, the Represent the firstAn abscissa of the aircraft; Represent the first An ordinate of the aircraft; representing an aircraft index; Representing a radar index; representing plane abscissa variables; representing a plane ordinate variable; Representing a lower abscissa of the flight area; representing the upper bound of the abscissa of the flight area; representing a lower ordinate bound of the flight area; Representing the upper bound of the ordinate of the flight area; s2, constructing a cooperative interference assessment model: For each aircraft And each radarGenerating a group of false targets according to the relative position relation: ; Wherein, the Representation for aircraftAnd radarThe generated false target set; Representing a first false object; Representing a fourth false object