RSS-based localization of isotropically decaying source with unknown power and pathloss factor. (August 2016)

• Record Type: Journal Article
• Authors: Sun, Shunyuan; Sun, Li; Ding, Zhiguo
• Is Part Of: Chaos, solitons and fractals. Volume 89(2016)
• Page Start: 391
• Page End: 396
• Publication Date: 2016-08
• Subjects: Received signal strength -- Localization -- Least-squares -- Cramer–Rao lower bound -- Smart hospital -- Medical equipment; Chaotic behavior in systems -- Periodicals; Solitons -- Periodicals; Fractals -- Periodicals; Chaotic behavior in systems; Fractals; Solitons; Periodicals; 003.7
• DOI: 10.1016/j.chaos.2016.01.031
• Languages: English
• ISSNs: 0960-0779

Abstract

Abstract: This paper addresses the localization of an isotropically decaying source based on the received signal strength (RSS) measurements that are collected from nearby active sensors that are position-known and wirelessly connected, and it propose a novel iterative algorithm for RSS-based source localization in order to improve the location accuracy and realize real-time location and automatic monitoring for hospital patients and medical equipment in the smart hospital. In particular, we consider the general case where the source power and pathloss factor are both unknown. For such a source localization problem, we propose an iterative algorithm, in which the unknown source position and two other unknown parameters (i.e. the source power and pathloss factor) are estimated in an alternating way based on each other, with our proposed sub-optimum initial estimate on source position obtained based on the RSS measurements that are collected from a few (closest) active sensors with largest RSS values. Analysis and simulation study show that our proposed iterative algorithm guarantees globally convergence to the least-squares (LS) solution, where for our suitably assumed independent and identically distributed (i.i.d.) zero-mean Gaussian RSS measurement errors the converged localization performance achieves the optimum that corresponds to the Cramer–Rao lower bound (CRLB).