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In this paper, we propose a filtering algorithm for simultaneously estimatingthe mode, input and state of hidden mode switched linear stochastic systemswith unknown inputs. Using a multiple-model approach with a bank of linearinput and state filters for each mode, our algorithm relies on the ability tofind the most probable model as a mode estimate, which we show is possible withinput and state filters by identifying a key property, that a particularresidual signal we call generalized innovation is a Gaussian white noise. Wealso provide an asymptotic analysis for the proposed algorithm and providesufficient conditions for asymptotically achieving convergence to the truemodel (consistency), or to the 'closest' model according to aninformation-theoretic measure (convergence). A simulation example ofintention-aware vehicles at an intersection is given to demonstrate theeffectiveness of our approach.

Simultaneous Mode, Input and State Estimation for Switched Linear Stochastic Systems