Name
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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.automatica.2007.04.018 | Automatica |
Keywords | Field | DocType |
Risk-sensitive estimation,Jump Markov linear system,IMM,Multiple-model estimation | Markov process,Forward algorithm,Linear filter,Linear system,Control theory,Markov chain,Filter (signal processing),Gaussian process,System identification,Mathematics | Journal |
Volume | Issue | ISSN |
44 | 1 | 0005-1098 |
Citations | PageRank | References |
11 | 0.67 | 12 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Umut Orguner | 1 | 548 | 40.11 |
| Mübeccel Demirekler | 2 | 152 | 19.39 |