k-medoids-Trust-Based Distributed H∞ Fusion Filtering Method

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.