Developing UFIR Filtering with Consensus on Estimates for Distributed Wireless Sensor Networks

Recent decades have celebrated a growing interest to wireless sensor networks (WSNs), both in theory and applications. Organized to have a large number of nodes, the WSN allows for redundant measurements that makes the distributed optimal estimation an adequate sensor fusion technique. The estimators developed for WSNs should ensure the consensus in the network while respecting restrictions imposed by the battery life, real-time estimation, and low computing burden. In this work, we develop the unbiased finite impulse response (UFIR) filtering technique to operate under consensus on the estimates in the distributed WSN. Properly tuned on optimal horizons, the distributed UFIR filter with consensus on estimates reduces the mean square error (MSE) as compared to the centralized UFIR. It also demonstrates higher robustness against model errors while respecting the restrictions of the WSN.

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