Risk-sensitive filtering for jump Markov linear systems

Orguner U., Demirekler M.

AUTOMATICA, vol.44, no.1, pp.109-118, 2008 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 44 Issue: 1
  • Publication Date: 2008
  • Doi Number: 10.1016/j.automatica.2007.04.018
  • Journal Name: AUTOMATICA
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.109-118
  • Keywords: risk-sensitive estimation, jump Markov linear system, IMM, multiple-model estimation, DISCRETE-TIME, STATE ESTIMATION, TARGET TRACKING, ALGORITHMS, PARAMETERS
  • Middle East Technical University Affiliated: Yes

Abstract

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. (C) 2007 Elsevier Ltd. All rights reserved.