Research on the Cooperative Passive Location of Moving Targets Based on Improved Particle Swarm Optimization

Abstract

Aiming at the cooperative passive location of moving targets by UAV swarm, this paper constructs a passive location and tracking algorithm for a moving target based on the A optimization criterion and the improved particle swarm optimization (PSO) algorithm. Firstly, the localization method of cluster cooperative passive localization is selected and the measurement model is constructed. Then, the problem of improving passive location accuracy is transformed into the problem of obtaining more target information. From the perspective of information theory, using the A criterion as the optimization target, the passive localization process for static targets is further deduced. The Recursive Neural Network (RNN) is used to predict the probability distribution of the target’s location in the next moment so as to improve the localization method and make it suitable for the localization of moving targets. The particle swarm algorithm is improved by using grouping and time period strategy, and the algorithm flow of moving target location is constructed. Finally, through the simulation verification and algorithm comparison, the advantages of the algorithm in this paper are presented.