Risk-sensitive particle filters for mitigating sample impoverishment

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2008-10-01

Author

Orguner, Umut

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Risk-sensitive filters (RSF) put a penalty to higher-order moments of the estimation error compared to conventional filters as the Kalman filter minimizing the mean square error (MSE). The result is a more cautious filter, which can be interpreted as an implicit and automatic way to increase the state noise covariance. On the other hand, the process of jittering, or roughening, is well known in particle filters to mitigate sample impoverishment. The purpose of this contribution is to introduce risk-sensitive particle filters (RSPF) as an alternative approach to mitigate sample impoverishment based on constructing explicit risk functions from a general class of factorizable functions. It is first shown that RSF can be done in nonlinear systems using a recursion of an infinite dimensional information state which involves general risk functions. Then, this information state calculation is carried out using particle approximations. Some alternative approaches, generalizations, specific cases, comparison to existing methods of sample impoverishment mitigation and issues related to the selection of risk functions and parameters are examined. Performance of the resulting filter using various risk functions is illustrated on a simulated scenario and compared with the roughening method.

Subject Keywords

Signal Processing, Electrical and Electronic Engineering

URI

https://hdl.handle.net/11511/35288

Journal

IEEE TRANSACTIONS ON SIGNAL PROCESSING

DOI

https://doi.org/10.1109/tsp.2008.928520

Collections

Department of Electrical and Electronics Engineering, Article

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Citation Formats

U. Orguner, “Risk-sensitive particle filters for mitigating sample impoverishment,” IEEE TRANSACTIONS ON SIGNAL PROCESSING, pp. 5001–5012, 2008, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/35288.