The implementation of track deception jamming by multiple UAVs on enemy network radar has high requirements for cooperation between the UAVs. The complex and changeable air combat environment will cause UAVs to deviate from the preset track, and then affect the homology test effect of false track points. In order to plan the UAV flight track with high interference rate, this paper first constructs the track deception jamming model, and then establishes an evaluation function model based on the analysis of the UAV energy consumption, probability of being detected by enemy radar and ineffective interference rate. Finally, the standard particle swarm optimization algorithm is improved based on Logistic chaos mapping, Levy flight and greedy strategy and then the improved simplified particle swarm optimization algorithm is obtained. The simulation results show that the improved simplified particle swarm optimization algorithm has faster search speed and higher search accuracy, and the UAV flight track planning based on the evaluation function in this paper can obtain greater effective interference rate.
Aiming at the problem of complex target data allocation of anti-radiation UAV, with the support of anti-radiation UAV target data, a combat target allocation algorithm is proposed to solve the optimal task allocation scheme of anti-radiation UAV. Firstly, the model of combat target allocation is established, and the problem of target allocation strategy is transformed into the problem of maximizing the mathematical expectation of cluster combat effectiveness. Secondly, the combat target allocation algorithm is proposed and the global optimality of the proposed algorithm is deduced. Finally, the simulation results show that the algorithm in this paper is more efficient and can obtain the global optimal solution compared with the classical traversing method and genetic algorithm. The dynamic combat target allocation algorithm proposed in this paper can greatly improve the combat effectiveness of anti-radiation UAV.
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