A Joint Indoor WLAN Localization and Outlier Detection Scheme Using LASSO and Elastic-Net Optimization Techniques

Abstract

Abstract-In this paper, we introduce two indoor Wireless Local Area Network (WLAN) positioning methods using augmented sparse recovery algorithms. These schemes render a sparse user's position vector, and in parallel, minimize the distance between the online measurement and radio map. The overall localization scheme for both methods consists of three steps: 1) coarse localization, obtained from comparing the online measurements with clustered radio map. A novel graph-based method is proposed to cluster the offline fingerprints. In the online phase, a Region Of Interest (ROI) is selected within which we search for the user's location; 2) Access Point (AP) selection; and 3) fine localization through the novel sparse recovery algorithms. Since the online measurements are subject to inordinate measurement readings, called outliers, the sparse recovery methods are modified in order to jointly estimate the outliers and user's position vector. The outlier detection procedure identifies the APs whose readings are either not available or erroneous. The proposed localization methods have been tested with Received Signal Strength (RSS) measurements in a typical office environment and the results show that they can localize the user with significantly high accuracy and resolution which is superior to the results from competing WLAN fingerprinting localization methods.

Modern WLAN Fingerprinting Indoor Positioning Methods and Deployment Challenges

“…The weighted connection (edge) between two nodes is regulated by a similarity measure between the nodes [109]. This similarity is based on the fact that spatially close RPs should receive similar readings from the same set of APs.…”

“…Hence, the correlated predictors should be allowed to jointly borrow strength from each other. GLMNET-based localization incorporates the above features as follows [109]:…”

A Novel Indoor Positioning System Using Kernel Local Discriminant Analysis in Internet‐of‐Things

“…A GS-based positioning system is reformulated to cope with outliers during online phase only. The work [22] proposed a joint WLAN based localization and outlier detection scheme. The scheme consists of three phases of course localization, AP selection, and fine localization using sparse recovery algorithms.…”

Precise Performance Analysis of the Box-Elastic Net Under Matrix Uncertainties

“…This combination allows for learning a sparse model where few of the entries are non-zero like the LASSO, while still maintaining the regularization properties of the ridge regression. The EN has been used in many applications [3], [4], [5], [6], [7], [8]. In this paper, we refer to (1) as the standard EN, but we focus on a modified version that we call the Box-EN which solves the following optimization instead…”