Risk-sensitive filtering for jump Markov linear systems

Date

2008-01-01

Author

Orguner, Umut
Demirekler, Muebeccel

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In this paper, a risk-sensitive multiple-model filtering algorithm is derived using the reference probability methods. First, the approximation of the interacting multiple-model (IMM) algorithm is identified in the reference probability domain. Then, the same type of approximation is used to derive the finite-dimensional risk-sensitive filtering algorithm. The derived algorithm reduces to the IMM filter when the risk-sensitive parameter goes to zero and reduces to the risk-sensitive filter for linear Gauss-Markov systems when the number of models is unity. The algorithm performs better in a simulated uncertain parameter scenario than the IMM filter.

Subject Keywords

Control and Systems Engineering, Electrical and Electronic Engineering

URI

https://hdl.handle.net/11511/45833

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Department of Electrical and Electronics Engineering, Article