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Proceedings Paper

Particle flow for nonlinear filters with log-homotopy
Author(s): Fred Daum; Jim Huang
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Paper Abstract

We describe a new nonlinear filter that is vastly superior to the classic particle filter. In particular, the computational complexity of the new filter is many orders of magnitude less than the classic particle filter with optimal estimation accuracy for problems with dimension greater than 2 or 3. We consider nonlinear estimation problems with dimensions varying from 1 to 20 that are smooth and fully coupled (i.e. dense not sparse). The new filter implements Bayes' rule using particle flow 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 derive the ODE that describes particle flow. This paper was written for normal engineers, who do not have homotopy for breakfast.

Paper Details

Date Published: 16 April 2008
PDF: 12 pages
Proc. SPIE 6969, Signal and Data Processing of Small Targets 2008, 696918 (16 April 2008); doi: 10.1117/12.764909
Show Author Affiliations
Fred Daum, Raytheon Co. (United States)
Jim Huang, Raytheon Co. (United States)

Published in SPIE Proceedings Vol. 6969:
Signal and Data Processing of Small Targets 2008
Oliver E. Drummond, Editor(s)

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