Particle Filter Optimization for Adaptive Radar Data Processing

Adaptive data processing in modern radar systems is standard approach. The classic kalman filter, extended kalman filter and many other applications are usually used for state estimate and radar target tracking. Signal and data processing technology improvement allows the more effective target tracking in real time. In this paper, we approach the structure and key features of the optimal particle filtering (PF). The optimization criteria for PF are proposed and analyzed. The dependence of mean square error on the number of particles during track prediction by particle filter is discussed. Using this dependence, we were able to determine the minimum required number of particles to correctly predict the trajectory of the target depending on its dynamics (trajectory complexity). The results of off-line real radar data processing are analyzed.