Distributed UFIR Filtering Over WSNs With Consensus on Estimates

Abstract

The distributed wireless sensor network (WSN) technologies have experienced rapid developments in recent years owing to mobility, scalability, and flexibility of solutions with different kinds of consensus achieved using the Kalman filter (KF). In this article, we employ a robust linear technique known as unbiased finite impulse response (UFIR) filtering and develop it for distributed WSNs with optimal consensus on estimates. A batch of distributed UFIR (dUFIR) filter is designed to operate on individual and average data horizons and then represented with an iterative algorithm using recursions. Based on simulations of a circularly traveling and rapidly maneuvering object covered by a WSN with 50 nodes, the dUFIR filter is shown to have higher robustness against the distributed KF (dKF). An experimental verification is conducted for a known ground truth of a moving vehicle covered by a WSN with eight nodes. The trade-off between the dUFIR filter, dKF, and dH <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> filter is investigated in detail under different operation scenarios and tuning modes.