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Proceedings Paper

Regularized multitarget particle filter for sensor management
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Paper Abstract

Sensor management in support of Level 1 data fusion (multisensor integration), or Level 2 data fusion (situation assessment) requires a computationally tractable multitarget filter. The theoretically optimal approach to this multi-target filtering is a suitable generalization of the recursive Bayes nonlinear filter. However, this optimal filter is intractable and computationally challenging that it must usually be approximated. We report on the approximation of a multi-target non-linear filtering for Sensor Management that is based on the particle filter implementation of Stein-Winter probability hypothesis densities (PHDs). Our main focus is on the operational utility of the implementation, and its computational efficiency and robustness for sensor management applications. We present a multitarget Particle Filter (PF) implementation of the PHD that include clustering, regularization, and computational efficiency. We present some open problems, and suggest future developments. Sensor management demonstrations using a simulated multi-target scenario are presented.

Paper Details

Date Published: 17 May 2006
PDF: 11 pages
Proc. SPIE 6235, Signal Processing, Sensor Fusion, and Target Recognition XV, 62350N (17 May 2006); doi: 10.1117/12.666128
Show Author Affiliations
A. El-Fallah, Scientific Systems Co., Inc. (United States)
A. Zatezalo, Scientific Systems Co., Inc. (United States)
R. Mahler, Lockheed Martin Corp. (United States)
R. K. Mehra, Scientific Systems Co., Inc. (United States)
M. Alford, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 6235:
Signal Processing, Sensor Fusion, and Target Recognition XV
Ivan Kadar, Editor(s)

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