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Sensors 2016, 16(9), 1346; doi:10.3390/s16091346

Practical Performance Analysis for Multiple Information Fusion Based Scalable Localization System Using Wireless Sensor Networks

2,†,‡,* , 2,†
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Shenzhen Institute of Radio Testing & Tech., Shenzhen 518000, China
Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin 14195, Germany
Author to whom correspondence should be addressed.
Academic Editors: Lyudmila Mihaylova, Byung-Gyu Kim and Debi Prosad Dogra
Received: 27 June 2016 / Revised: 1 August 2016 / Accepted: 9 August 2016 / Published: 23 August 2016
(This article belongs to the Special Issue Scalable Localization in Wireless Sensor Networks)
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In practical localization system design, researchers need to consider several aspects to make the positioning efficiently and effectively, e.g., the available auxiliary information, sensing devices, equipment deployment and the environment. Then, these practical concerns turn out to be the technical problems, e.g., the sequential position state propagation, the target-anchor geometry effect, the Non-line-of-sight (NLOS) identification and the related prior information. It is necessary to construct an efficient framework that can exploit multiple available information and guide the system design. In this paper, we propose a scalable method to analyze system performance based on the Cramér–Rao lower bound (CRLB), which can fuse all of the information adaptively. Firstly, we use an abstract function to represent all of the wireless localization system model. Then, the unknown vector of the CRLB consists of two parts: the first part is the estimated vector, and the second part is the auxiliary vector, which helps improve the estimation accuracy. Accordingly, the Fisher information matrix is divided into two parts: the state matrix and the auxiliary matrix. Unlike the theoretical analysis, our CRLB can be a practical fundamental limit to denote the system that fuses multiple information in the complicated environment, e.g., recursive Bayesian estimation based on the hidden Markov model, the map matching method and the NLOS identification and mitigation methods. Thus, the theoretical results are approaching the real case more. In addition, our method is more adaptable than other CRLBs when considering more unknown important factors. We use the proposed method to analyze the wireless sensor network-based indoor localization system. The influence of the hybrid LOS/NLOS channels, the building layout information and the relative height differences between the target and anchors are analyzed. It is demonstrated that our method exploits all of the available information for the indoor localization systems and serves as an indicator for practical system evaluation. View Full-Text
Keywords: indoor localization; Cramér–Rao lower bound; Bayesian estimation; non-line-of-sight; wireless sensor network indoor localization; Cramér–Rao lower bound; Bayesian estimation; non-line-of-sight; wireless sensor network

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zhao, Y.; Li, X.; Zhang, S.; Meng, T.; Zhang, Y. Practical Performance Analysis for Multiple Information Fusion Based Scalable Localization System Using Wireless Sensor Networks. Sensors 2016, 16, 1346.

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