TY - JOUR
T1 - Enhance RSS-Based Indoor Localization Accuracy by Leveraging Environmental Physical Features
AU - XIANG, Peng
AU - JI, Peng
AU - ZHANG, Dian
N1 - Tis research was supported in part by Shenzhen Peacock Talent Grant 827-000175 and China NSFC Grants 61202377 and U1301251.
PY - 2018/7/9
Y1 - 2018/7/9
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85050719447&partnerID=8YFLogxK
U2 - 10.1155/2018/8956757
DO - 10.1155/2018/8956757
M3 - Journal Article (refereed)
AN - SCOPUS:85050719447
VL - 2018
JO - Wireless Communications and Mobile Computing
JF - Wireless Communications and Mobile Computing
SN - 1530-8669
M1 - 8956757
ER -