Sequential simulation-based estimation of jump Markov linear systems
Jump Markov linear systems (JMLS) are linear systems whose parameters evolve with time according to a finite state Markov chain. Our aim is to recursively compute optimal conditional mean state estimates for JMLS. We present efficient simulation-based algorithms called particle filters to solve the optimal filtering problem. Our algorithms combine sequential importance sampling, a selection scheme and Markov chain Monte Carlo methods. They use several variance reduction methods to make the most of the statistical structure of JMLS.