Application of Deinterleaving Using Clustering for Radar Emitter Signals Based on PMOPSO

Due to the increased overlapping probability of radar emitter signals in a dense signal environment, traditional clusteringalgorithms can′t reveal the diversity structure of the signal feature set or meet the requirements of radar emitter signal sorting and recognition. Therefore, combined with the essence of deinterleaving for radar emitter signals, a multi-objective function is developed with clustering compactness and connectivity indicators. Inspired by the P system optimization theory, a P system-based multi-objective particle swarm optimization algorithm is proposed under the membrane frame. This algorithm incorporates particle swarm algorithm into each elementary membrane and executes particle multi-objective search strategy; through the dissolution rules between membranes, the non-dominated sorting and crowding distance mechanism are used in the skin membrane to improve convergence of the algorithm and keep the diversity and difference of the solution set. In the end, the proposed algorithm is used to obtain the Pareto optimal solution set of the symbolic entropy feature set of radar emitter signal. Simulation results show that the algorithm has better accuracy rate of deinterleaving and recognition for radar emitter signal. Moreover, the proposed algorithm is effective and feasible, and its performance is superior to traditional clustering methods.