scholarly journals Indoor Location Wi-Fi Fingerprinting using Invariant Received Signal Strength

2016 ◽  
Vol 9 (4) ◽  
pp. 206-213
Author(s):  
M. N. Husen ◽  
S. Lee
2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Kai Dong ◽  
Zhen Ling ◽  
Xiangyu Xia ◽  
Haibo Ye ◽  
Wenjia Wu ◽  
...  

The development of the Internet of Things has accelerated research in the indoor location fingerprinting technique, which provides value-added localization services for existing WLAN infrastructures without the need for any specialized hardware. The deployment of a fingerprinting based localization system requires an extremely large amount of measurements on received signal strength information to generate a location fingerprint database. Nonetheless, this requirement can rarely be satisfied in most indoor environments. In this paper, we target one but common situation when the collected measurements on received signal strength information are insufficient, and show limitations of existing location fingerprinting methods in dealing with inadequate location fingerprints. We also introduce a novel method to reduce noise in measuring the received signal strength based on the maximum likelihood estimation, and compute locations from inadequate location fingerprints by using the stochastic gradient descent algorithm. Our experiment results show that our proposed method can achieve better localization performance even when only a small quantity of RSS measurements is available. Especially when the number of observations at each location is small, our proposed method has evident superiority in localization accuracy.


2017 ◽  
Vol 255 ◽  
pp. 118-133 ◽  
Author(s):  
Yuri Álvarez López ◽  
María Elena de Cos Gómez ◽  
Fernando Las-Heras Andrés

2014 ◽  
Vol 23 (07) ◽  
pp. 1450094 ◽  
Author(s):  
WEIHONG FAN ◽  
MAJID AHMADI ◽  
FENG XUE

Localization and tracking technology based on received signal strength indicator (RSSI) is one of the most popular topics because of its low demand on hardware and cost. But the complexity of the indoor environment, leads to the uncertainty of the radio propagation which can seriously affect the positioning accuracy based on the received signal strength. Focused on the wall reflection in the indoor environment, the radio propagation characteristic based on ray-tracing model is analyzed and one strategy for the near wall localization is presented. The actual hardware platform and experimental test results show the applicability of the empirical logarithmic path loss model for localization and the effect of the wall reflection.


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