Risk-sensitive filtering for jump Markov linear systems

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.