Abstract: The particle swarm optimization (PSO) is a high performance optimization algorithm. It is easy to implementwith strong robustness, and nearly always can be used to solve complicated linear and nonlinear optimization problems.High-resolution range profile (HRRP) and broadband depolarization coefficient were extracted as target feature vector in thispaper. A classifier based on the correlation matching algorithm was designed. PSO algorithm was introduced to optimize theclassifier in order to solve the problem of large workload. Simulation results show that PSO can greatly improve the capabilityof classifier and is more efficient.