Stochastic systems: estimation, identification and adaptive control
Stochastic systems: estimation, identification and adaptive control
Finite-dimensional filters for passive tracking of Markov jump linear systems
Automatica (Journal of IFAC)
Indefinite-quadratic estimation and control: a unified approach to H2 and H∞ theories
Indefinite-quadratic estimation and control: a unified approach to H2 and H∞ theories
Optimal filtering of discrete-time hybrid systems
Journal of Optimization Theory and Applications
An improvement to the interacting multiple model (IMM) algorithm
IEEE Transactions on Signal Processing
Online Bayesian estimation of transition probabilities for Markovian jump systems
IEEE Transactions on Signal Processing
Expectation maximization algorithms for MAP estimation of jumpMarkov linear systems
IEEE Transactions on Signal Processing
Particle filters for state estimation of jump Markov linear systems
IEEE Transactions on Signal Processing
On state estimation of discrete-time Markov jump linear systems
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
State estimation for Markovian Jump Linear Systems with bounded disturbances
Automatica (Journal of IFAC)
| Hi-index | 22.15 |
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.