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【24h】Measurement Dissemination-Based Distributed Bayesian Filter Using the Latest-In-and-Full-Out Exchange Protocol for Networked Unmanned Vehicles

机译针对网络无人驾驶车辆使用最新进出式交换协议的基于测量传播的分布式贝叶斯滤波器

【摘要】This paper presents a measurement dissemin-ation-based distributed Bayesian filter (DBF) for a network of unmanned ground vehicles (UGVs). The DBF utilizes the latest-in-and-full-out (LIFO) exchange protocol to disseminate the sensor measurements within local neighbors. Different from existing statistics dissemination strategies that transmit posterior distributions or likelihood functions, each UGV under LIFO only exchanges latest available measurements, which significantly reduces the transmission burden between each pair of UGVs to scale linearly with the network size. Under the condition of fixed and undirected topology, LIFO can guarantee non-intermittent dissemination of all measurements over the network within a finite time. Two types of LIFO-based DBF algorithms are then derived to estimate individual probability density function (PDF) of the static target and moving target, respectively. For the former, each UGV locally fuses the newly received measurements, while for the latter, a set of measurement history is stored and sequentially fused. The consistency of LIFO-based DBF is proved that the estimated target position converges in probability to the true target position. The effectiveness of this method is experimentally demonstrated by the target localization via multiple mobile robots with sonar sensors in an indoor environment.

【摘要机译】本文提出了一种用于无人地面飞行器(UGV)网络的基于测量传播的分布式贝叶斯滤波器(DBF)。 DBF利用最新的全功能(LIFO)交换协议在本地邻居中传播传感器测量结果。与传输后验分布或似然函数的现有统计信息传播策略不同,LIFO下的每个UGV仅交换最新的可用度量,这大大减少了每对UGV之间的传输负担,从而可以随网络规模线性缩放。在固定和非定向拓扑的条件下,LIFO可以保证在有限的时间内通过网络非间歇地分发所有测量。然后导出两种基于LIFO的DBF算法,分别估计静态目标和运动目标的单独概率密度函数(PDF)。对于前者,每个UGV在本地融合新接收的测量值,而对于后者,存储一组测量历史记录并顺序融合。证明了基于LIFO的DBF的一致性,即估计目标位置在概率上收敛于真实目标位置。通过在室内环境中通过多个带有声纳传感器的移动机器人进行目标定位,实验证明了该方法的有效性。

【作者】Chang Liu;Shengbo Eben Li;J. Karl Hedrick;

【作者单位】Department of Mechanical Engineering, University of California, Berkeley, CA, USA; Department of Mechanical Engineering, University of California, Berkeley, CA, USA; Department of Mechanical Engineering, University of California, Berkeley, CA, USA;

【年(卷),期】2017(64),11

【年度】2017

【页码】8756-8766

【总页数】11

【原文格式】PDF

【正文语种】eng

【中图分类】;

【关键词】Probability density function;Bayes methods;Topology;Robot sensing systems;Network topology;Position measurement;Atmospheric measurements;

机译 概率密度函数;贝叶斯方法;拓扑;机器人传感系统;网络拓扑;位置测量;大气测量;
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