Risk-sensitive filtering for jump Markov linear systems

Date

2008-01-01

Author

Orguner, Umut
Demirekler, Muebeccel

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

3
views

0
downloads

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

Collections

Department of Electrical and Electronics Engineering, Article