摘要：

针对无线传感器网络(WSN)中节点故障或异常情况下的系统状态估计问题, 提出一种基于k-medoids信任的分布式H_∞融合滤波方法, 旨在提高传感器故障下系统状态估计的鲁棒性和精确性。该方法的主要步骤为: 首先, 各传感器节点分别采集局部测量信息并执行分布式H_∞滤波更新局部状态估计; 然后, 各近邻传感器节点之间交换局部状态估计后, 通过建立k-medoids信任机制将获取的局部状态估计分为信任估计和非信任估计, 舍弃非信任估计而保留信任估计; 最后, 设计一种分布式扩散融合策略, 该策略计算信任估计的自适应权重并实时融合更新局部状态估计。通过目标跟踪的仿真结果表明, 在测量干扰故障、数据重放故障、错误数据注入故障下, 所提方法比基于信任的分布式卡尔曼滤波算法对传感器节点故障或异常更具弹性, 验证了所提方法的有效性和优越性。

关键词: 无线传感器网络, 状态估计, 分布式H_∞滤波, 传感器故障, 扩散融合, 目标跟踪

Abstract:

To address the issues during system state estimation in the presence of node failures or anomalies in Wireless Sensor Networks (WSNs), a k-medoids-trust-based distributed H_∞ fusion filtering method is proposed to improve the robustness and accuracy of system state estimation in the event of sensor failures. The method has the following primary steps. First, each sensor node independently collects local measurement information and performs distributed H_∞ filtering to update its local state estimations. Subsequently, a k-medoids trust mechanism is established to divide the obtained local state estimations into trusted and untrusted estimations after the local state estimations are exchanged between neighboring sensor nodes. Untrusted estimations are discarded, whereas the trusted estimations are retained. A distributed diffusion fusion strategy is then designed that calculates the adaptive weights of the trusted estimations and fuses and updates the local state estimation in real time. The effectiveness and superiority of the proposed state estimation method are demonstrated using a target tracking simulation example. The results from simulated target tracking show that the proposed method is more resilient to sensor node faults or anomalies than the trust-based distributed Kalman filtering algorithm under measurement interference, data replay, and erroneous data injection faults, thus verifying the effectiveness and superiority of the proposed method.

Key words: Wireless Sensor Network (WSN), state estimation, distributed H_∞ filtering, sensor failure, diffusion fusion, target tracking