TY - JOUR

T1 - Extended Object Tracking Using Monte Carlo Methods.

AU - Angelova, D

AU - Mihaylova, L

N1 - "©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."

PY - 2008/2/1

Y1 - 2008/2/1

N2 - 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.

AB - 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.

KW - sequential Monte Carlo methods

KW - extended targets

KW - Mixture Kalman filtering

KW - data augmentation

KW - DCS-publications-id

KW - art-877

KW - DCS-publications-credits

KW - dsp

KW - DCS-publications-personnel-id

KW - 121

UR - http://www.scopus.com/inward/record.url?scp=39649088439&partnerID=8YFLogxK

U2 - 10.1109/TSP.2007.907851

DO - 10.1109/TSP.2007.907851

M3 - Journal article

VL - 56

SP - 825

EP - 832

JO - IEEE Transactions on Signal Processing

JF - IEEE Transactions on Signal Processing

SN - 1053-587X

IS - 2

ER -