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Extended Object Tracking Using Monte Carlo Methods.

Research output: Contribution to journalJournal article

Published

Journal publication date1/02/2008
JournalIEEE Transactions on Signal Processing
Journal number2
Volume56
Number of pages8
Pages825-832
Original languageEnglish

Abstract

Abstract—This paper addresses the problem of tracking extended objects, such as ships or a convoy of vehicles moving in urban environment. Two Monte Carlo techniques for extended object tracking are proposed: an Interacting Multiple Model Data Augmentation (IMM-DA) algorithm and a modified version of the Mixture Kalman Filter (MKF) of Chen and Liu [1], Mixture Kalman Filter modified (MKFm). The DA technique with finite mixtures estimates the object extent parameters, whereas an IMM filter estimates the kinematic states (position and speed) of the manoeuvring object. Next, the system model is formulated in a Partially Conditional Dynamic Linear (PCDL) form. This affords us to propose two latent indicator variables characterising, respectively, the motion mode and object size. Then a MKFm is developed with the PCDL model. The IMM-DA and the MKFm performance is compared with a combined IMM-Particle Filter (IMM-PF) algorithm with respect to accuracy and computational complexity. The most accurate parameter estimates are obtained by the DA algorithm, followed by the MKFm and PF.

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