Indoor WLAN localization via adaptive Lasso Bayesian inference and convex optimization

Abstract

There has been growing interest in indoor positioning system technologies due to the important role of real-time indoor positioning services in modern technologies such as security services and emergency healthcare. Currently, many large companies such as Apple, Microsoft, and Google have researched location-based services (LBS), as they are key for network optimization and extensive computing applications. Multiple techniques were proposed using fingerprinting-based location methods due to their ability to obtain accurate results within several meters. However, their major drawback is that the received signal strength (RSS) can fluctuate with time and different environment, giving RSS distribution a multimodal distribution. Thus, in this paper, we established a framework consisting of k-mean-symmetrical-Hölderdivergence, a statistical model that encapsulates Cauchy-Schwarz divergence and skews Bhattacharyya divergence, to measure dissimilarities among signals that have multivariate distributions. In other words, the traditional k-mean is extended to metaalgorithms to detect the cluster that RSS is related to. Our second approach was hierarchical Bayesian model based on adaptive lasso criterion to recover sparse signals to optimize the accuracy of the indoor location estimation by solving the l1minimization problem. The experimental results showed that the proposed system had substantially improved localization estimation accuracy compared to traditional fingerprinting-based localization methods. ABOUT THE AUTHORI recently completed my PhD in electrical and computer engineering at Western Michigan University in December 2016. My teaching experience and practical skills in communication systems and signal processing, alongside with my research background, My current research focuses machine learning algorithms, UAV channel modeling, Brainwave signals detectins, mobile communication, signal processing,, and peer-to-peer/ad hock networking, espicially with applications to group localization, tracking, and monitoring. I'm a member of the Institute of Electrical and Electronics Engineers (IEEE).

Rfid-Based Users’ Localization in the Crowded Areas

“…Basically, any group of people that gathered to do any activities will have the concern for loss people of even got hurt like the concert, football matches, and marathons which held in different countries and attract more than 15000 participants [11]. Several kinds of wireless technology are used in both outdoor and indoor localization which are mainly used to track specific users in congested areas, some of these techniques are using GPS (Global Positioning System) [12], [13] however such a signal may be weak or even not available in some areas [14], [15]. Another approach that using different sensors along with the smartphone to sense a person's activity in real-time can be used as well this is called Smartphone-based Human Activity Recognition (HAR) [14].…”