Kernel-Based Positioning in Wireless Local Area Networks

Abstract

The recent proliferation of Location-Based Services (LBSs) has necessitated the development of effective indoor positioning solutions. In such a context, Wireless Local Area Network (WLAN) positioning is a particularly viable solution in terms of hardware and installation costs due to the ubiquity of WLAN infrastructures. This paper examines three aspects of the problem of indoor WLAN positioning using received signal strength (RSS). First, we show that, due to the variability of RSS features over space, a spatially localized positioning method leads to improved positioning results. Second, we explore the problem of access point (AP) selection for positioning and demonstrate the need for further research in this area. Third, we present a kernelized distance calculation algorithm for comparing RSS observations to RSS training records. Experimental results indicate that the proposed system leads to a 17 percent (0.56 m) improvement over the widely used K-nearest neighbor and histogram-based methods.Index Terms-Location-dependent and sensitive mobile applications, applications of pattern recognition, nonparametric statistics, support services for mobile computing.

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

“…Battiti et al [63] used a multi-layer perceptron for indoor positioning, with a positioning accuracy of 2.3 m. Nowicki et al [64] proposed a method that used Deep Neural Network (DNN) combined with stacked autoencoder (SAE). The DNN used in this method was the first to be used for Wi-Fi fingerprinting and could achieve better performance in image analysis than other methods.…”

“…The DNN used in this method was the first to be used for Wi-Fi fingerprinting and could achieve better performance in image analysis than other methods. The SAE Nowicki [64] used could determine the floor or building and reduce dimensionality of the input data. The experiment results were better than other networks without autoencoders.…”

Taxonomy of Fundamental Concepts of Localization in Cyber-Physical and Sensor Networks

“…These methods generally rely on the use of the probability theory by first generating probability densities for the training data and then by computing the Maximum-Likelihood. Kernel-based nonlinear methods have also been investigated for similarity computation, such as in [41]. Nonetheless, these methods often require the collection of large data samples in the training phase and high processing capabilities [62], which is generally beyond the capacity of low-cost cyber-physical devices.…”

Graph-Based Semi-Supervised Learning for Indoor Localization Using Crowdsourced Data

“…The next category includes those methods that are based on WiFi RSS fingerprints also known as fingerprint-based methods. Originally proposed by P. Bahl et al [7], various fingerprint-based localization systems have been designed and developed during the last decade [7][8][9].…”