Simultaneous Mode, Input and State Estimation for Switched Linear Stochastic Systems
This work addresses a specific estimation problem in control systems, such as autonomous vehicles, but appears incremental as it builds on existing multiple-model and filtering techniques.
The paper tackles the problem of simultaneously estimating mode, input, and state in hidden mode switched linear stochastic systems with unknown inputs, proposing a filtering algorithm that uses a multiple-model approach and achieves convergence to the true or closest model under certain conditions, as demonstrated in a simulation of intention-aware vehicles at an intersection.
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 call generalized innovation is a Gaussian white noise. We also provide an asymptotic analysis for the proposed algorithm and provide sufficient conditions for asymptotically achieving 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.