Distributed Recursive Filtering for a Class of State-Saturated Nonlinear Systems: A Novel Reputation-Aware Mechanism
针对传感器网络中状态饱和非线性系统，提出一种声誉感知的分布式递归滤波算法，通过构建声誉模型识别并拒绝不可靠数据，以提升估计精度和鲁棒性，并在移动机器人室内定位案例中验证了有效性。
This article is concerned with the distributed recursive filtering problem for a class of nonlinear time-varying systems over sensor networks (SNs) under state saturations via a reputation-aware mechanism (RAM). The state saturation is considered to describe the inevitable constraints of physical equipment, while the RAM is introduced to eliminate abnormal data caused by sensor faults or malicious attacks. To address these challenges, a reputation-aware distributed recursive filtering (RADRF) algorithm is developed, where a novel reputation model is constructed to assign credibility scores to neighboring sensors, thereby identifying and rejecting unreliable data. Furthermore, a saturation-dependent recursive filter is designed, through which the upper bound of the covariance of filtering error dynamics (UBCFEDs) is determined by solving a recursive matrix equation. The filter gain is subsequently parameterized by minimizing the trace of the UBCFED so as to guarantee the optimality of estimation performance under nonlinear saturation effects and reputation-based weighting. To validate the feasibility and effectiveness of the developed approach, an illustrative case study concerning the indoor localization of a mobile robot is conducted. Simulation results demonstrate that the proposed RADRF algorithm significantly improves estimation accuracy and robustness compared with schemes that neglect the RAM.