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Extended Object Tracking Using Monte Carlo Methods.

Angelova, D and Mihaylova, L (2008) Extended Object Tracking Using Monte Carlo Methods. IEEE Transactions on Signal Processing, 56 (2). pp. 825-832. ISSN 1053-587X

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    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.

    Item Type: Article
    Journal or Publication Title: IEEE Transactions on Signal Processing
    Additional Information: "©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE." "This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder."
    Uncontrolled Keywords: sequential Monte Carlo methods ; extended targets ; Mixture Kalman filtering ; data augmentation ; DCS-publications-id ; art-877 ; DCS-publications-credits ; dsp ; DCS-publications-personnel-id ; 121
    Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
    Departments: Faculty of Science and Technology > School of Computing & Communications
    ID Code: 993
    Deposited By: Dr L Mihaylov
    Deposited On: 21 Jan 2008 14:02
    Refereed?: Yes
    Published?: Published
    Last Modified: 22 Oct 2017 02:02
    Identification Number:
    URI: http://eprints.lancs.ac.uk/id/eprint/993

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