Particle Swarm Optimization based Tuned Interactive Multiple Model Extended Kalman Filter for Manoeuvring Target Tracking

R.K. Jatoth and T.K. Kumar (India)


Extended Kalman Filter, IMM-EKF, Tuning, LeastSquares, Estimation of Covariance Matrix, PSO


The Kalman filter is a well known adaptive filtering Algorithm, widely used for target tracking applications. When the system model and measurements are non linear, variation of Kalman filter like Extended Kalman filter (EKF) and Unscented Kalman filters (UKF) are used. If the target is manoeuvring, Interactive Multiple Models (IMM) are widely used. For obtaining reliable estimate of the target state, filter has to be tuned before the operation (off line).Tuning of the Kalman filter is the process of estimation of the noise covariance matrices from process data. In practical applications, due to unavailable measurements of the process noise and high dimensionality of the problem tuning of the filter is left for engineering intuition. In this paper, tuning of the IMM-EKF is investigated using Particle Swarm Optimization (PSO). The simulation results shows, the superiority of the PSO tuned IMM-EKF over the conventional IMM-EKF.

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