文章基本信息

标题：Estimating Directional Data From Network Topology for Improving Tracking Performance
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作者：Slavisa Tomic ; Marko Beko ; Rui Dinis 等
期刊名称：Journal of Sensor and Actuator Networks
电子版ISSN：2224-2708
出版年度：2019
卷号：8
期号：2
页码：30-49
DOI：10.3390/jsan8020030
出版社：MDPI Publishing
摘要：This work proposes a novel approach for tracking a moving target in non-line-of-sight (NLOS) environments based on range estimates extracted from received signal strength (RSS) and time of arrival (TOA) measurements. By exploiting the known architecture of reference points to act as an improper antenna array and the range estimates, angle of arrival (AOA) of the signal emitted by the target is first estimated at each reference point. We then show how to take advantage of these angle estimates to convert the problem into a more convenient, polar space, where a linearization of the measurement models is easily achieved. The derived linear model serves as the main building block on top of which prior knowledge acquired during the movement of the target is incorporated by adapting a Kalman filter (KF). The performance of the proposed approach was assessed through computer simulations, which confirmed its effectiveness in combating the negative effect of NLOS bias and superiority in comparison with its naive counterpart, which does not take prior knowledge into consideration.
关键词：target tracking; non-line-of-sight (NLOS); received signal strength (RSS); time of arrival (TOA); angle of arrival (AOA); Kalman filter (KF) target tracking ; non-line-of-sight (NLOS) ; received signal strength (RSS) ; time of arrival (TOA) ; angle of arrival (AOA) ; Kalman filter (KF)