Enhanced Distributed Consensus Filtering Over Lossy Wireless Sensor Networks With Application to Target Speed Tracking
Yu Feng, Kaiwei Dai, Xiang Chen
Abstract
This article presents a novel way of distributed consensus filtering with enhanced robustness over wireless sensor networks, where bounded-power disturbances related to modeling uncertainty and white Gaussian noises are included. Sensors are assumed to receive local observations and transmit them to their neighbors through lossy links. A distributed consensus filtering scheme, consisting of an optimal filter, a separated damping term, and a consensus tuning parameter, is considered through exchanging prior estimates among neighboring nodes, to achieve the cohesiveness among local estimates together with robust optimal estimation performance. A sufficient and necessary condition is conducted for determining the consensus parameter, leading to a nonconservative design way. Finally, to show the effectiveness of the presented results, an experiment of target speed tracking is included.