Fourier transform particle flow for nonlinear filters

Fred Daum; Jim Huang

doi:10.1117/12.2001666

23 May 2013 Fourier transform particle flow for nonlinear filters

Fred Daum, Jim Huang

Proceedings Volume 8745, Signal Processing, Sensor Fusion, and Target Recognition XXII; 87450R (2013) https://doi.org/10.1117/12.2001666
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

Abstract

We derive five new algorithms to design particle flow for nonlinear filters using the Fourier transform of the PDE that determines the flow of particles corresponding to Bayes’ rule. This exploits the fact that our PDE is linear with constant coefficients. We also use variance reduction and explicit stabilization to enhance robustness of the filter. Our new filter works for arbitrary smooth nowhere vanishing probability densities.

Citation Download Citation

Fred Daum and Jim Huang "Fourier transform particle flow for nonlinear filters", Proc. SPIE 8745, Signal Processing, Sensor Fusion, and Target Recognition XXII, 87450R (23 May 2013); https://doi.org/10.1117/12.2001666

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