An Improved Hybrid RSS/TDOA Wireless Sensors Localization Technique Utilizing Wi-Fi Networks
This paper presents a hybrid localization algorithm for wireless sensor networks (WSNs) that simultaneously exploits received signal strength (RSS) and time difference of arrival (TDOA) measurements. The accuracy and convergence reliability of the proposed hybrid scheme are also enhanced by incorporating RSS measurements from Wi-Fi networks via cooperative communications between Wi-Fi and sensor networks. To this end, two different types of estimators based on Taylor-series (TS) expansion and maximum-likelihood (ML) estimation are first proposed to solve the set of nonlinear RSS/TDOA equations taking into account measurement errors. The corresponding Cramér-Rao lower bound (CRLB) for the established scheme is then derived and utilized as a performance measure for the two estimators. Simulation results show that the proposed hybrid positioning approach significantly outperforms the previously considered localization solutions in WSNs, thanks to the joint process of the received signals’ power and time difference of arrival. The advantages of the proposed scheme in providing high location accuracy, fast convergence, low complexity implementation, and low power consumption make it an attractive localization solution via WSNs.
KeywordsCooperative mobile positioning Maximum-likelihood method RSS Taylor-series TDOA Wi-Fi
This research is supported by the NSF CCSS Award 1309658 through a project entitled EAGER: Localization in Ad-Hoc Wireless Networks: Investigation into Fusing Dempster-Shafer Theory and Support Vector Machines. The authors gratefully acknowledge the encouragement and motivation from the Program Director Professor Zhi Tian, Fellow of the IEEE. The authors thank the EECS Department at the University of Toledo for partial support through assistantships and tuition waivers.
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