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Extended CPHD Filter for Combining Multi-Target Tracking with Sensor Alignment

Li D., Yi D.

Przegląd Elektrotechniczny

2013

R. 89, nr 1b

41--44

An extended CPHD (Cardinalised Probability Hypothesis Density) filter for combining multi-target tracking with sensor alignment is proposed. The augmented state is established by appending the sensor biases to the single-target state. The cardinality distribution of the targets and the intensity of the augmented state are propagated by employing Gaussian mixtures. The target states and the sensor biases are jointly estimated. Simulation results show that the proposed filter successfully achieves the sensor alignment and outperforms the standard CPHD filter.

Zaproponowano rozszerzony filtr CPHD do połączenia śledzenia wielu celów z wyrównaniem czujników. Wyrażenia dotyczące pojedynczego celu rozszerzono przez dodanie offsetu czujnika. Moc (kardynalność) rozkładu celów i intensywność rozszerzonego wyrażenia są zrealizowane przez zastosowanie przekształceń Gaussa. Przeprowadzono jednoczesna estymację wyrażeń celu i offsetu czujnika. Wyniki symulacji wykazują, że proponowane rozwiązanie satysfakcjonująco dokonuje wyrównania czujników i wyjściowych parametrów standardowego filtru CPHD.

GA-JPDA and fuzzy data association algorithms coupled with IMM-PF estimator for highly maneuvering multiple-target tracking

Djouadi M. S., Morsly Y.

Archives of Control Sciences

2007

Vol. 17, no. 3

311-327

In this paper, we present filtering algorithm to perform accurate estimation in jump Markov nonlinear systems, in case of multi-target tracking. With this paper, we aim to contribute in solving the problem of model-based body motion estimation by using data coming from visual sensors. The Interacting Multiple Model (IMM) algorithm is specially designed to track accurately targets whose state and/or measurement (assumed to be linear) models change during motion transition. However, when these models are nonlinear, the IMM algorithm must be modified in order to guarantee an accurate tracking. In order to deal with this problem, the IMM algorithm was combined with the Unscented Kalman Filter (UKF). Even if the later algorithm proved its efficacy in nonlinear model case, it presents a serious drawback in the case of non Gaussian noise. To deal with this problem we propose to substitute the UKF with the Particle Filter (PF). To overcome the problem of data association, we propose the use the JPDA approach. To reduce the computational burden of this technique, we choose firstly the most likely feasible events by applying a Genetic Algorithm; finally the derived algorithm from the combination of the IMM-PF algorithm and the GA-JPDA approach is noted GA-JPDA-IMM-PF. To insure a more reduction of the computation complexity of the latter data association approach, we propose a fuzzy data association approach which we combine with the IMM-PF estimator, the derived algorithm is noted fuzzy IMM-PF. Finally the two algorithms are compared according to the target loss rate inferred by each of them.