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Proceedings Paper

Nonlinear filters with log-homotopy
Author(s): Fred Daum; Jim Huang
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Paper Abstract

We derive and test a new nonlinear filter that implements Bayes' rule using an ODE rather than with a pointwise multiplication of two functions. This avoids one of the fundamental and well known problems in particle filters, namely "particle collapse" as a result of Bayes' rule. We use a log-homotopy to construct this ODE. Our new algorithm is vastly superior to the classic particle filter, and we do not use any proposal density supplied by an EKF or UKF or other outside source. This paper was written for normal engineers, who do not have homotopy for breakfast.

Paper Details

Date Published: 25 September 2007
PDF: 15 pages
Proc. SPIE 6699, Signal and Data Processing of Small Targets 2007, 669918 (25 September 2007); doi: 10.1117/12.725684
Show Author Affiliations
Fred Daum, Raytheon (United States)
Jim Huang, Raytheon (United States)


Published in SPIE Proceedings Vol. 6699:
Signal and Data Processing of Small Targets 2007
Oliver E. Drummond; Richard D. Teichgraeber, Editor(s)

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