Generalized Gromov method for stochastic particle flow filters

Fred Daum; Jim Huang; Arjang Noushin

doi:10.1117/12.2248723

2 May 2017 Generalized Gromov method for stochastic particle flow filters

Fred Daum, Jim Huang, Arjang Noushin

Proceedings Volume 10200, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI; 102000I (2017) https://doi.org/10.1117/12.2248723
Event: SPIE Defense + Security, 2017, Anaheim, CA, United States

Abstract

We describe a new algorithm for stochastic particle flow filters using Gromov’s method. We derive a simple exact formula for Q in certain special cases. The purpose of using stochastic particle flow is two fold: improve estimation accuracy of the state vector and improve the accuracy of uncertainty quantification. Q is the covariance matrix of the diffusion for particle flow corresponding to Bayes’ rule.

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Citation Download Citation

Fred Daum, Jim Huang, and Arjang Noushin "Generalized Gromov method for stochastic particle flow filters", Proc. SPIE 10200, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI, 102000I (2 May 2017); https://doi.org/10.1117/12.2248723

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