Target Feature Extraction Using Generalized Particle Swarm Optimization-Based CLEAN for airdefense Radar
Recently, Particle Swarm Optimization (PSO) has been developed as a high-performance optimizer that is very easy to implement. Evolutionary optimization methods such as Genetic Algorithm (GA) or Evolutionary Programming (EP) are very similar to PSO, but requires more computational time than PSO. In this paper, The authors investigated the performance of generalized PSO-based CLEAN and generalized EP-based CLEAN for the simultaneous extraction of scattering centers and Complex Natural Resonance (CNR) frequencies which are used for the radar target recognition in air defense radar. The simulation results using artificially created data show that the calculation time of the former is lesser than that of the latter.