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Distributed UFIR Filtering Over WSNs With Consensus on Estimates

机译：通过估计的共识，通过WSN分发UFIR过滤

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摘要

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(infinity) filter is investigated in detail under different operation scenarios and tuning modes.
机译：由于使用Kalman滤波器（KF）实现了不同类型的共识的解决方案，分布式无线传感器网络（WSN）技术近年来历史迅速发展。在本文中，我们采用了一种被称为非偏见的有限脉冲响应（UFIR）滤波的强大线性技术，并为分布式WSN开发，并在估计上得到最佳共识。一批分布式UFIR（DUFIR）滤波器旨在在个人和平均数据视线上运行，然后使用递归用迭代算法表示。基于由具有50个节点的WSN覆盖的循环行驶和快速机动对象的模拟，DUFIR滤波器被示出对分布式KF（DKF）具有更高的鲁棒性。对于由八个节点覆盖的WSN覆盖的移动车辆的已知地面真理进行了实验验证。在不同的操作场景和调整模式下详细研究了DUFIR滤波器，DKF和DH（Infinity）滤波器之间的折衷。

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