Efficient particle filtering for Jump Markov Systems
We address here the problem of developing efficient particle filtering techniques in order to estimate the state of Jump Markov Systems (JMS). These processes are often met in signal processing (target tracking, communication…). Our algorithm takes advantage of the structure of the process. We apply our algorithm to time varying autoregressive processes.