Zero curvature particle flow for nonlinear filters

Fred Daum; Jim Huang

doi:10.1117/12.2009364

23 May 2013 Zero curvature particle flow for nonlinear filters

Fred Daum, Jim Huang

Proceedings Volume 8745, Signal Processing, Sensor Fusion, and Target Recognition XXII; 87450Q (2013) https://doi.org/10.1117/12.2009364
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

Abstract

We derive a new algorithm for computing Bayes’ rule using particle flow that has zero curvature. The flow is computed by solving a vector Riccati equation exactly in closed form rather than solving a PDE, with a significant reduction in computational complexity. Our theory is valid for any smooth nowhere vanishing probability densities, including highly multimodal non-Gaussian densities. We show that this new flow is similar to the extended Kalman filter in the special case of nonlinear measurements with Gaussian noise. We also outline more general particle flows, including: constant curvature, geodesic flow, non-constant curvature, piece-wise constant curvature, etc.

Citation Download Citation

Fred Daum and Jim Huang "Zero curvature particle flow for nonlinear filters", Proc. SPIE 8745, Signal Processing, Sensor Fusion, and Target Recognition XXII, 87450Q (23 May 2013); https://doi.org/10.1117/12.2009364

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