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Nonlinear multiple model particle filters algorithm for tracking multiple targets

Sebbagh A., Tebbikh H.

Archives of Control Sciences

2011

Vol. 21, no. 1

37-60

The paper addresses multiple targets tracking problem encountered in number of situations in signal and image processing. In this paper, we present an efficient filtering algorithm to perform accurate estimation in jump Markov nonlinear systems, which we aim to contribute in solving the problem of multiple targets tracking using bearings-only measurements. The idea of this algorithm consists of the combination between the multiple model approach and particle filtering methods, which give a nonlinear multiple model particle filters algorithm. This algorithm is used to estimate the trajectories of multiple targets assumed to be nonlinear, from their noisy bearings.

IMM based UKF and IMM based EKF algorithms for tracking highly maneuverable target

Djouadi M. S., Sebbagh A., Berkani D.

Archives of Control Sciences

2005

Vol. 15, no. 1

19-51

This paper aims to contribute in solving the problem of model-based body motion estimation by using data coming from visual sensors. We consider the case of state estimation in jump Markov nonlinear systems. The Interacting Multiple Model (IMM) algorithm is specially designed to track accurately targets whose atate and/or measurement models changes during motion transition. However, when these models are nonlinear, the IMM algorithm must be modified in order to guarantee an accurate track. In this paper we propose to compare the results given by an IMM algorithm Extended Kalman filter based (IMM-EKF) versus those given by an IMM algorithm Unscented Kalman Filter based (IMM-UKF) in tracking target assumed to be highly maneuverable.