State Estimation for Markov Jump Linear Systems with Quantized Measurements: A Quantized IMMPF Algorithm

In this paper, the problem of state estimation for Markov Jump Linear Systems (MJLSs) with quantized measurements is investigated. A quantized interacting multiple model particle filter (IMMPF) algorithm with a combination of the interacting multiple model (IMM) estimation framework and a particle filter is proposed. Our proposed algorithm is efficient to overcome the exponential computing difficulty with the application of IMM hypothesis merging method and deal with the nonlinearity of the quantizer by representing the posterior probability density under quantized observation with the particle filter. Both the state and the probability distribution of the mode can be estimated in our estimation framework. Simulation results demonstrate that the quantized IMMPF has significant advantage in estimate accuracy over standard IMM directly using quantized measurements and performs better than moving horizon Monte Carlo (MHMC) method on time exhausting aspect.