access icon free Recursive Bayesian estimation for Markov jump linear systems with unknown mode-dependent state delays

This study considers the minimum mean square error estimation problem for a class of jump Markov linear systems with unknown mode-dependent state delays. In order to show the difficulties caused by the unknown delays, the online Bayesian equation of the investigated system is firstly developed by incorporating the time-delay estimation into the recursion of system states. However, computing such optimal estimation causes an exponential increase in the requirement of computation and storage load. Therefore two different approximation techniques: interacting multiple-model approximation and detection–estimation method are utilised to obtain two suboptimal but executable filtering algorithms, respectively. Simulation results of the proposed methods for a system are presented to illustrate the effectiveness.

Inspec keywords: Bayes methods; linear systems; recursive estimation; delay estimation; Markov processes; mean square error methods; filtering theory

Other keywords: minimum mean square error estimation problem; filtering algorithms; system states; recursive Bayesian estimation; online Bayesian equation; optimal estimation; jump Markov linear systems; detection–estimation method; unknown mode-dependent state delays; time-delay estimation; multiple-model approximation; Markov jump linear systems; approximation techniques

Subjects: Markov processes; Markov processes; Interpolation and function approximation (numerical analysis); Filtering methods in signal processing; Interpolation and function approximation (numerical analysis); Signal processing theory

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