Adaptive filter based on Monte Carlo method to improve the target tracking in radar systems

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Abstract The state estimation and tracking of random target is an attractive research problem in radar system. The information received in the radar receiver was reflected by the target, that it is received with many white and Gaussian noise due to the characteristics of the transmission channel and the radar environment. After detection and location scenarios, the radar system must track the target in real time. We aim to improve the state estimation process for too random target at the given instant in order to converge to the true target state and smooth their true path for a long time, it simplifies the process of real-time tracking. In this framework, we propose a new approach based on the numerical methods presented by MONTE CARLO (MC) counterpart the method conventionally used named Extended KALMAN Filter (EKF), we showed that the first are more successful. Keywords: Radar, Monte Carlo, Extended KALMAN Filter, Tracking, PF, Random target.

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