25 August 2004 Quasi-Monte Carlo hybrid particle filters
Author Affiliations +
We describe a new hybrid particle filter that has two novel features: (1) it uses quasi-Monte Carlo samples rather than the conventional Monte Carlo sampling, and (2) it implements Bayes' rule exactly using smooth densities from the exponential family. Theory and numerical experiments over the last decade have shown that quasi-Monte Carlo sampling is vastly superior to Monte Carlo samples for certain high dimensional integrals, and we exploit this fact to reduce the computational complexity of our new particle filter. The main problem with conventional particle filters is the curse of dimensionality. We mitigate this issue by avoiding particle depletion, by implementing Bayes' rule exactly using smooth densities from the exponential family.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Frederick E. Daum, Frederick E. Daum, Jim Huang, Jim Huang, } "Quasi-Monte Carlo hybrid particle filters", Proc. SPIE 5428, Signal and Data Processing of Small Targets 2004, (25 August 2004); doi: 10.1117/12.532487; https://doi.org/10.1117/12.532487


Dynamic quasi-Monte Carlo for nonlinear filters
Proceedings of SPIE (August 25 2003)
Nonlinear filtering with quasi-Monte Carlo methods
Proceedings of SPIE (January 05 2004)
Nonlinear filters with particle flow
Proceedings of SPIE (September 04 2009)
Quasi-Monte Carlo particle filters: the JV filter
Proceedings of SPIE (May 19 2006)

Back to Top