Dynamic quasi-Monte Carlo for nonlinear filters

Frederick E. Daum; Jim Huang

doi:10.1117/12.484889

25 August 2003 Dynamic quasi-Monte Carlo for nonlinear filters

Frederick E. Daum, Jim Huang

Proceedings Volume 5096, Signal Processing, Sensor Fusion, and Target Recognition XII; (2003) https://doi.org/10.1117/12.484889
Event: AeroSense 2003, 2003, Orlando, Florida, United States

Abstract

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.

Citation Download Citation

Frederick E. Daum and Jim Huang "Dynamic quasi-Monte Carlo for nonlinear filters", Proc. SPIE 5096, Signal Processing, Sensor Fusion, and Target Recognition XII, (25 August 2003); https://doi.org/10.1117/12.484889

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