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—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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IEEE Transactions on Signal Processing
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?? sequential monte carlo methodsextended targetsmixture kalman filteringdata augmentationdcs-publications-idart-877dcs-publications-creditsdspdcs-publications-personnel-id121signal processingelectrical and electronic engineeringqa75 electronic computers. co ??
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21 Jan 2008 14:02
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13 Jan 2024 00:08