The Community for Technology Leaders
Green Image
Issue No. 01 - Jan. (2018 vol. 17)
ISSN: 1536-1233
pp: 113-126
Xiaohua Tian , School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
Mei Wang , School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
Wenxin Li , School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
Binyao Jiang , School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
Dong Xu , School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
Xinbing Wang , School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
Jun Xu , School of Computer Science, Georgia Institute of Technology, Atlanta, GA
ABSTRACT
Recent study presents a fundamental limit of the RSS fingerprinting based indoor localization. In this paper, we theoretically show that the temporal correlation of the RSS can further improve accuracy of the fingerprinting localization. In particular, we construct a theoretical framework to evaluate how the temporal correlation of the RSS can influence reliability of location estimation, which is based on a newly proposed radio propagation model considering the time-varying property of signals from Wi-Fi APs. The framework is then applied to analyze localization in the one-dimensional physical space, which reveals the fundamental reason why localization performance can be improved by leveraging temporal correlation of the RSS. We extend our analysis to high-dimensional scenarios and mathematically depict the boundaries in the RSS sample space, which distinguish one physical location from another. Moreover, we develop an algorithm to utilize temporal correlation of the RSS to improve the location estimation accuracy, where the process for choosing key design parameters are provided through experiments. Experiment results show that the localization reliability and accuracy can be improved by up to 13 and 30 percent with appropriate leveraging the RSS temporal correlation information.
INDEX TERMS
Correlation, Estimation, Radio propagation, Reliability, Mobile computing, Training, Analytical models
CITATION

X. Tian et al., "Improve Accuracy of Fingerprinting Localization with Temporal Correlation of the RSS," in IEEE Transactions on Mobile Computing, vol. 17, no. 1, pp. 113-126, 2018.
doi:10.1109/TMC.2017.2703892
512 ms
(Ver 3.3 (11022016))