Dynamic Modified Chaotic Particle Swarm Optimization for Radar Signal Sorting

Radar signal sorting is the core part of electronic support measures, which responsible for deinterleaving overlapping pulse sequences received by receiver from complex environment, separating different radiation source signals, and providing identification. Particle swarm optimization (PSO) a population-based global algorithm with great advantages in intelligent can adapt to electromagnetic environment variable signals high stream density. However, PSO-based method prone premature convergence cannot adaptively adjust particle parameters positions. In this paper, dynamic modified chaotic PSO (DMCPSO) proposed. Chaotic search used increase diversity later iteration avoid falling into local optimum. Adaptive adjustment related fitness value are adopted balance ability search. A new function proposed position dynamically corrected clustering analysis improve accuracy influence distribution feature parameters. The simulation results show that DMCPSO provides stable fast performance excellent indexes complex, variable, dense environment.