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Proceedings Paper

Comparison between smoothing and auxiliary particle filter in tracking a maneuverable target in a multiple sensor network
Author(s): Hazem Kamel; Wael Badawy
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Paper Abstract

Tracking a maneuvering target weakens the performance of predictive-model-based Bayesian state estimators (Kalman Filter). Therefore, the particle is used to track maneuverable targets instead of Kalman filter and its extensions. The particle filter proved more efficiency compared to Kalman filter and its extensions, e.g. Extended Kalman Filter (EKF) and Interacting Multiple Model (IMM). Unfortunately, due to the highly uncertainty and incompleteness of the information in a highly-maneuverable target-tracking problem, the advantage of the particle filter is weakened. Both auxiliary and smoothing particle filter were proposed to overcome this problem. In this paper, we compare the performance of both auxiliary and smoothing particle filter in tracking a highly maneuverable target. We applied both algorithms to track a maneuverable target in a multiple-sensors network. Monte Carlo simulation showed that the smoothing particle filter has a better performance when compared to auxiliary particle filter in tracking a maneuvering target.

Paper Details

Date Published: 19 May 2005
PDF: 8 pages
Proc. SPIE 5810, Acquisition, Tracking, and Pointing XIX, (19 May 2005); doi: 10.1117/12.603168
Show Author Affiliations
Hazem Kamel, Univ. of Calgary (Canada)
Wael Badawy, Univ. of Calgary (Canada)


Published in SPIE Proceedings Vol. 5810:
Acquisition, Tracking, and Pointing XIX
Michael K. Masten; Larry A. Stockum, Editor(s)

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