Optimization of radar reconnaissance constellation based on fuzzy relative entropy

Abstract

The optimization of radar reconnaissance constellation is of great significance for reconnaissance timeliness. In order to solve the problems of insufficient selection pressure and poor diversity of the evolutionary algorithm based on Pareto domination in solving the many-objective optimization problem of radar reconnaissance constellation, an imporved particle swarm optimization based on fuzzy relative entropy (IFREM_PSO) is proposed. A new inertial weight strategy is proposed to enhance the convergence speed and accuracy of the algorithm, improve the external archive maintenance strategy, and enhance the convergence and diversity of the algorithm. The optimization model of radar imaging reconnaissance constellation oriented to area reconnaissance is constructed. The IFREM_PSO is used to optimize the model, and the multi-objective particle swarm optimization algorithm (MOPSO) is used as the control algorithm. Experiments show that the IFREM_PSO has better results in terms of convergence, diversity and convergence speed, and can effectively solve the problem of radar reconnaissance constellation optimization.