抄録
A history based particle filter is introduced for state estimation of problems of stochastic hybrid systems. A multiple hypothesis approach to particle filters is proposed to estimate system states and modes simultaneously. The hypotheses are composed by possible mode sequences, then particle filters are applied to each hypothesis to test which hypothesis is the most acceptable. A performance of the proposed approach compared with a standard particle filter and the interactive multiple model based particle filter is discussed based on the numerical simulations.
