Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - RSS-based localization of isotropically decaying source with unknown power and pathloss factor
AU - Sun, Shunyuan
AU - Sun, Li
AU - Ding, Zhiguo
PY - 2016/8
Y1 - 2016/8
N2 - 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).
AB - 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).
KW - Received signal strength
KW - Localization
KW - Least-squares
KW - Cramer–Rao lower bound
KW - Smart hospital
KW - Medical equipment
U2 - 10.1016/j.chaos.2016.01.031
DO - 10.1016/j.chaos.2016.01.031
M3 - Journal article
VL - 89
SP - 391
EP - 396
JO - Chaos, Solitons and Fractals
JF - Chaos, Solitons and Fractals
SN - 0960-0779
ER -