Source Localization Using RSS Measurements with Sensor Position Uncertainty

IF 1.9 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Qi Wang, Xianqing Li
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引用次数: 0

Abstract

Received signal strength- (RSS-) based localization has attracted considerable attention for its low cost and easy implementation. In plenty of existing work, sensor positions, which play an important role in source localization, are usually assumed perfectly known. Unfortunately, they are often subject to uncertainties, which directly leads to effect on localization result. To tackle this problem, we study the RSS-based source localization with sensor position uncertainty. Sensor position uncertainty will be modeled as two types: Gaussian random variable and unknown nonrandom variable. For either of the models, two semidefinite programming (SDP) methods are proposed, i.e., SDP-1 and SDP-2. The SDP-1 method proceeds from the nonconvex problem with respect to the maximum likelihood (ML) estimation and then obtains an SDP problem using proper approximation and relaxation. The SDP-2 method first transfers the sensor position uncertainties to the source position and then obtains an SDP formulation following a similar idea as in SDP-1 method. Numerical examples demonstrate the performance superiority of the proposed methods, compared to some existing methods assuming perfect sensor position information.
利用传感器位置不确定的RSS测量进行源定位
基于接收信号强度(RSS)的定位以其低成本和易于实现而引起了人们的广泛关注。在大量现有工作中,传感器位置在源定位中起着重要作用,通常被认为是完全已知的。遗憾的是,它们经常受到不确定性的影响,这直接导致对定位结果的影响。为了解决这个问题,我们研究了具有传感器位置不确定性的基于RSS的源定位。传感器位置不确定性将被建模为两种类型:高斯随机变量和未知非随机变量。对于任何一种模型,都提出了两种半定规划(SDP)方法,即SDP-1和SDP-2。SDP-1方法从关于最大似然(ML)估计的非凸问题开始,然后使用适当的近似和松弛来获得SDP问题。SDP-2方法首先将传感器位置不确定性转移到源位置,然后按照与SDP-1方法类似的思想获得SDP公式。数值算例表明,与假设传感器位置信息完美的现有方法相比,所提出的方法具有性能优势。
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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