Wireless sensor networks (WSN) are often deployed in harsh environments, where electromagnetic interference, damaged sensors, or the landscape itself cause the network to suffer from faulty links and missing data. In this paper, we develop an unbiased finite impulse response (UFIR) filtering algorithm for optimal consensus on estimates in distributed WSN. Simulations are provided assuming two possible scenarios with missing data. The results show that the distributed UFIR filter is more robust than the distributed Kalman filter against missing data.

This paper presents a multi-sensor decentralized fusion unbiased finite impulse response (UFIR) filter for discrete time-invariant state-space models. Fusion is provided in the minimum variance sense. By calculating the cross covariance between any of two local filters for the extended state-space model, linear optimal weights are derived to fuse local UFIR estimates. Simulation conduced for a two-state polynomial model shows that the proposed fusion UFIR filter has higher robustness than the fusion Kalman filter against errors in the noise statistics and temporary model uncertainties.