Comparison between smoothing and auxiliary particle filter in tracking a maneuverable target in a multiple sensor network

Hazem Kamel; Wael Badawy

doi:10.1117/12.603168

19 May 2005 Comparison between smoothing and auxiliary particle filter in tracking a maneuverable target in a multiple sensor network

Hazem Kamel, Wael Badawy

Author Affiliations +

Proceedings Volume 5810, Acquisition, Tracking, and Pointing XIX; (2005) https://doi.org/10.1117/12.603168
Event: Defense and Security, 2005, Orlando, Florida, United States

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.

Citation Download Citation

Hazem Kamel and Wael Badawy "Comparison between smoothing and auxiliary particle filter in tracking a maneuverable target in a multiple sensor network", Proc. SPIE 5810, Acquisition, Tracking, and Pointing XIX, (19 May 2005); https://doi.org/10.1117/12.603168

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