Exact particle flow for nonlinear filters

Fred Daum; Jim Huang; Arjang Noushin

doi:10.1117/12.839590

27 April 2010 Exact particle flow for nonlinear filters

Fred Daum, Jim Huang, Arjang Noushin

Proceedings Volume 7697, Signal Processing, Sensor Fusion, and Target Recognition XIX; 769704 (2010) https://doi.org/10.1117/12.839590
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

Abstract

We have invented a new theory of exact particle flow for nonlinear filters. This generalizes our theory of particle flow that is already many orders of magnitude faster than standard particle filters and which is several orders of magnitude more accurate than the extended Kalman filter for difficult nonlinear problems. The new theory generalizes our recent log-homotopy particle flow filters in three ways: (1) the particle flow corresponds to the exact flow of the conditional probability density; (2) roughly speaking, the old theory was based on incompressible flow (like subsonic flight in air), whereas the new theory allows compressible flow (like supersonic flight in air); (3) the old theory suffers from obstruction of particle flow as well as singularities in the equations for flow, whereas the new theory has no obstructions and no singularities. Moreover, our basic filter theory is a radical departure from all other particle filters in three ways: (a) we do not use any proposal density; (b) we never resample; and (c) we compute Bayes' rule by particle flow rather than as a point wise multiplication.

Citation Download Citation

Fred Daum, Jim Huang, and Arjang Noushin "Exact particle flow for nonlinear filters", Proc. SPIE 7697, Signal Processing, Sensor Fusion, and Target Recognition XIX, 769704 (27 April 2010); https://doi.org/10.1117/12.839590

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