An Enhanced Adaptive Set-Membership Filter for Nonlinear Ellipsoidal Estimation

The extended set-membership filter (ESMF) for nonlinear ellipsoidal estimation suffers from numerical instability, computation complexity, as well as the difficulty in filter parameter selection. In this paper, a UD factorization-based adaptive set-membership filter (AESMF) is developed and applied to nonlinear joint estimation of both time-varying states and parameters. As the result of using the proposed UD factorization, combined with a new sequential and selective measurement update strategy, the numerical stability and real-time applicability of conventional ESMF are substantially improved. Furthermore, an adaptive selection scheme of the filter parameters is derived to reduce the computation complexity and achieve sub-optimal estimation. Simulation results have shown the efficiency and robustness of the proposed method.