基于UD分解的自适应扩展集员估计方法

A UD Factorization-based Adaptive Extended Set-membership Filter

用于非线性椭球估计的扩展集员算法在实际应用中存存着数值稳定性差、计算复杂度高以及滤波器参数难以选择等问题.本文提出了一种基于UD分解的自适应扩展集员估计算法,用于解决非线性系统时变状态和参数的联合估计和定界问题.新算法将UD分解与序列更新和选择更新策略结合起米,改进了传统扩展集员算法的数值稳定性和实时性能;同时,对滤波器参数进行自适应选择以进一步降低计算复杂度并达到次优估计结果.仿真实验表明了该算法的有效性和鲁棒性.

The extended set-membership filter 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 is developed and applied to nonlinear joint estimation of both time-varying states and parameters. As a 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.