State and Mode Estimation for Discrete-Time Jump Markov Systems
SIAM Journal on Control and Optimization
Brief paper: Risk-sensitive filtering for jump Markov linear systems
Automatica (Journal of IFAC)
A novel interacting multiple model algorithm
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
A new multiple model filter with switch time conditions
IEEE Transactions on Signal Processing
Efficient particle filtering for jump Markov systems. Application to time-varying autoregressions
IEEE Transactions on Signal Processing
Iterative algorithms for state estimation of jump Markov linearsystems
IEEE Transactions on Signal Processing
Particle filters for state estimation of jump Markov linear systems
IEEE Transactions on Signal Processing
Brief paper: A detection-estimation scheme for state estimation in switching environments
Automatica (Journal of IFAC)
A multiple model multiple hypothesis filter for Markovian switching systems
Automatica (Journal of IFAC)
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In this paper, state estimation problem for discrete-time Markov jump linear systems is considered. First, three equalities are proposed. Next, they are applied to the state estimation problem of considered systems so that a novel suboptimal algorithm in the sense of minimum mean-square error estimate is obtained where the computation and storage load of the suboptimal algorithm is not ever-increasing with the length of the noise observation sequence. The proposed algorithm and the suboptimal adaptive algorithm proposed in [1] are all based on a truncated approximation strategy. However, compared with the algorithm of [1], the proposed algorithm requires much less approximations. Computer simulations are carried out to evaluate the performance of the proposed algorithm.