LAUSR

This paper proposes the problem of joint state estimation and mode recognition for nonlinear stochastic systems with unknown sensor mode. The considered sensor mode is represented by a random finite set, whose elements can be one specific mode or a set of certain modes. A set-valued mode recognition-based Bayesian estimation framework is proposed to propagate the posterior density of the state conditioned on sensor modes and measurements, where the mode is recognized based on the maximum correntropy criterion. Furthermore, a mode-separability metric is proposed to discern the reliability of mode recognition, and utilized to derive two distinct implementation schemes, including state estimation based on separable and inseparable modes. Simulation results of fault detection and target tracking are provided to demonstrate the superiority of the proposed method in terms of state estimation accuracy and mode recognition effectiveness.