Convex Combination for Wireless Localization Using Biased RSS Measurements

IF 1.9 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Qi Wang, Fei Li, Teng Shao, Chao Xu
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引用次数: 0

Abstract

Received signal strength- (RSS-) based localization in wireless sensor networks (WSNs) has attracted significant attention due to its advantages of low cost and simple implementation. In practice, RSS measurements may suffer from sensor biases, which deteriorates the localization accuracy. However, most of the existing localization methods are designed for bias-free measurements. In this paper, we propose a convex combination method for RSS localization in the presence of sensor biases. The parameter vector composed of unknown location and sensor biases is estimated simultaneously by using a convex combination of some virtual points. These virtual points form a convex hull, into which the parameter vector falls with large probability. By this, the original nonconvex estimation problem is converted to be convex. Numerical examples demonstrate the superiority of the proposed method in terms of localization accuracy, compared to the existing semidefinite programming (SDP) methods.
利用有偏差的 RSS 测量进行无线定位的凸面组合
基于接收信号强度(RSS)的无线传感器网络(WSN)定位因其成本低、实施简单等优点而备受关注。实际上,RSS 测量可能会受到传感器偏差的影响,从而降低定位精度。然而,现有的定位方法大多是针对无偏差测量而设计的。本文提出了一种在存在传感器偏差的情况下进行 RSS 定位的凸组合方法。通过使用一些虚拟点的凸组合来同时估计由未知位置和传感器偏差组成的参数向量。这些虚拟点形成一个凸壳,参数矢量很有可能落入该凸壳中。这样,原来的非凸估计问题就转换成了凸估计问题。数值实例证明,与现有的半定量编程(SDP)方法相比,所提出的方法在定位精度方面更胜一筹。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.50
自引率
4.30%
发文量
94
审稿时长
3.6 months
期刊介绍: International Journal of Distributed Sensor Networks (IJDSN) is a JCR ranked, peer-reviewed, open access journal that focuses on applied research and applications of sensor networks. The goal of this journal is to provide a forum for the publication of important research contributions in developing high performance computing solutions to problems arising from the complexities of these sensor network systems. Articles highlight advances in uses of sensor network systems for solving computational tasks in manufacturing, engineering and environmental systems.
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