Passive target tracking with non Gaussian initial conditions

Abstract

In this paper the problem of target tracking using passive measurements is examined. The target is assumed to be travelling on a straight line with constant velocity. A new algorithm is proposed that takes into consideration the effects of the non-Gaussian initial conditions on the estimation of the position and velocity of the target. The new filter is based on the Lainiotis partitioning algorithm. Extensive simulation results show the superiority of the new filter over a tracker employing the extended Kalman filter and assuming Gaussian initial conditions.