Enhance RSS-Based Indoor Localization Accuracy by Leveraging Environmental Physical Features

Peng XIANG, Peng JI, Dian ZHANG*

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

6 Citations (Scopus)


Indoor localization technologies based on Radio Signal Strength (RSS) attract many researchers' attentions, since RSS can be easily obtained by wireless devices without additional hardware. However, such technologies are apt to be affected by indoor environments and multipath phenomenon. Thus, the accuracy is very difficult to improve. In this paper, we put forward a method, which is able to leverage various other resources in localization. Besides the traditional RSS information, the environmental physical features, e.g., the light, temperature, and humidity information, are all utilized for localization. After building a comprehensive fingerprint map for the above information, we propose an algorithm to localize the target based on Naïve Bayesian. Experimental results show that the successful positioning accuracy can dramatically outperform traditional pure RSS-based indoor localization method by about 39%. Our method has the potential to improve all the radio frequency (RF) based localization approaches.

Original languageEnglish
Article number8956757
JournalWireless Communications and Mobile Computing
Publication statusPublished - 9 Jul 2018
Externally publishedYes

Bibliographical note

Tis research was supported in part by Shenzhen Peacock Talent Grant 827-000175 and China NSFC Grants 61202377 and U1301251.

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