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

Abstract

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.