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Summary In this paper, we propose a filtering algorithm for simultaneously estimating the mode, input and state of hidden mode switched linear stochastic systems with unknown inputs. Using a multiple‐model approach with a bank of linear input and state filters for each mode, our algorithm relies on the ability to find the most probable model as a mode estimate, which we show is possible with input and state filters by identifying a key property, that a particular residual signal we callgeneralized innovationis a Gaussian white noise. We also provide an asymptotic analysis for the proposed algorithm and provide sufficient conditions forasymptoticallyachieving convergence to the true model (consistency), or to the “closest” model according to an information‐theoretic measure (convergence). A simulation example of intention‐aware vehicles at an intersection is given to demonstrate the effectiveness of our approach.
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Free, publicly-accessible full text available December 16, 2025
