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30 September 2013 Particle flow with non-zero diffusion for nonlinear filters, Bayesian decisions and transport
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Abstract
We derive a new algorithm for particle flow with non-zero diffusion corresponding to Bayes’ rule, and we report the results of Monte Carlo simulations which show that the new filter is an order of magnitude more accurate than the extended Kalman filter for a difficult nonlinear filter problem. Our new algorithm is simple and fast to compute, and it has an especially nice intuitive formula, which is the same as Newton’s method to solve the maximum likelihood estimation (MLE) problem (but for each particle rather than only the MLE), and it is also the same as the extended Kalman filter for the special case of Gaussian densities (but for each particle rather than just the point estimate). All of these particle flows apply to arbitrary multimodal densities with smooth nowhere vanishing non- Gaussian densities.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Fred Daum and Jim Huang "Particle flow with non-zero diffusion for nonlinear filters, Bayesian decisions and transport", Proc. SPIE 8857, Signal and Data Processing of Small Targets 2013, 88570A (30 September 2013); https://doi.org/10.1117/12.2021370
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