EESM-based fingerprint algorithm for Wi-Fi indoor positioning system

Abstract

In recent years, great improvements took place in smartphone industry. Along with the development of cloud services and web applications, lots of developers also take great effort on developing smartphone applications. Among various applications related to social networks, LBS (Location Based Service) is the key technique which is the basic for social interactive activities. While GPS (global positioning system) works well enough outdoors, Wi-Fi RSS (receive signal strength)-based fingerprinting system is the most promising solution for indoors. A novel EESM-based fingerprint algorithm which improves the positioning performance by involving channel estimation for creating more robust fingerprints is proposed in this paper. Moreover, a cloud computing based indoor positioning system is also introduced for evaluating performances. Both simulations and experiments show that EESM-based fingerprints are more stable and representative in the multipath indoor environments. An up to 30% reduction in error distance in a classic indoor positioning system using k-NN matching algorithm is provided by our proposed EESM-based fingerprints.

Environmental exposure assessment using indoor/outdoor detection on smartphones

“…A participatory sensing approach can also be used to train the classification model [47], thus alleviating bootstrapping issues in such methods. Quite often, indoor localisation uses Received Signal Strength (RSS) as a metric for location fingerprinting [15,20,46,50]. RSS fingerprints are then mapped to relative spatial coordinates which are geographically connected to user movement paths.…”

Application Based Multimodal Biometric System for Speech Controlled Device

“…presented which includes better speed, memory management and portability Various fingerprint matching systems have been proposed which emphasizes on minutiae information, local ridges [11]; some studies are based on singularity point's position, orientation [12], and relative distance detection, novel printing novel EESM-based fingerprint algorithm for indoor positioning [13]. Similarly, Le Hoang Thai and Ha Nhat Tam proposed a paper titled "Fingerprint recognition using standardized fingerprint model" [16] which gives a detailed explanation of step by step implementation of fingerprint recognition and generates an accuracy of 98.33-99.4%.…”