Paper Info
Title
Unsupervised Learning of Signal Strength Models for Device-Free Localization
Abstract
RSS-based device-free localization (DFL) systems make use of the received signal strength (RSS) changes in a network of static wireless nodes to locate and track people. Current DFL systems require calibration, which depending on the method and required accuracy, can be very expensive in terms of time and effort, making DFL system deployment and maintenance challenging. This paper implements unsupervised learning of signal strength models (UnLeSS), a Baum-Welch based method to learn the parameters of a hidden Markov model (HMM) for each link, including the RSS distribution during the no-crossing state and the crossing state. The system uses the HMM to estimate the probability of each link being in the crossed state. As a demonstration of its effectiveness, the per-link probability is used in a radio tomographic imaging algorithm to track the location of a person. Experiments are conducted in two different homes to determine the performance of UnLeSS. We demonstrate that our system is capable of estimating the crossing/no-crossing distribution with Kullback-Leibler divergence maximum of 1.43. UnLeSS is capable of tracking a person with high accuracy (0.66 m) without a calibration period.
Year
DOI
Venue
2019
10.1109/WoWMoM.2019.8792970
2019 IEEE 20th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM)
Keywords
DocType
ISBN
unsupervised learning,signal strength models,RSS-based device-free localization systems,received signal strength changes,static wireless nodes,Baum-Welch based method,hidden Markov model,HMM,RSS distribution,per-link probability,DFL systems,Kullback-Leibler divergence maximum,UnLeSS,radio tomographic imaging algorithm
Conference
978-1-7281-0271-9
Citations 
PageRank 
References 
0
0.34
8
Authors
2
Name
Order
Citations
PageRank
Amal Al-Husseiny100.34
Neal Patwari23805241.58