A New Location Estimation System for Wireless Networks Based on Linear Discriminant Functions and Hidden Markov Models

Nuño-Barrau, Galo; Páz-Borrallo, José M.

doi:10.1155/asp/2006/68154

Cited by 24 publications

(25 citation statements)

References 30 publications

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“…Traditional deterministic methods can be easily implemented based on k nearest neighbors (k-NN) and the computational complexity is often low. Some other more advanced deterministic algorithms such as support vector machine [59] and linear discriminant analysis [60] show better localization accuracy with higher computational cost.…”

Section: A Basic Fingerprint Localization (Overview)mentioning

confidence: 99%

Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons

Chan

2016

IEEE Commun. Surv. Tutorials

1,106

639

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The growing commercial interest in indoor locationbased services (ILBS) has spurred recent development of many indoor positioning techniques. Due to the absence of GPS (global positioning system) signal, many other signals have been proposed for indoor usage. Among them, Wi-Fi (802.11) emerges as a promising one due to the pervasive deployment of wireless LANs (WLANs). In particular, Wi-Fi fingerprinting has been attracting much attention recently because it does not require line-ofsight measurement of access points (APs) and achieves high applicability in complex indoor environment.This survey overviews recent advances on two major areas of Wi-Fi fingerprint localization: advanced localization techniques and efficient system deployment. Regarding advanced techniques to localize users, we present how to make use of temporal or spatial signal patterns, user collaboration, and motion sensors.Regarding efficient system deployment, we discuss recent advances on reducing offline labor-intensive survey, adapting to fingerprint changes, calibrating heterogeneous devices for signal collection, and achieving energy efficiency for smartphones. We study and compare the approaches through our deployment experiences, and discuss some future directions.

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Section: A Basic Fingerprint Localization (Overview)mentioning

confidence: 99%

Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons

Chan

2016

IEEE Commun. Surv. Tutorials

1,106

639

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“…On the other side, keeping too much space between them will degrade the accuracy. The recommended spacing among the grids is 1-3 meters [12].…”

Section: Making Gridsmentioning

confidence: 99%

An Experimental Study on the Behavior of Received Signal Strength in Indoor Environment

Ullah

Custodio

Shah

et al. 2013

2013 11th International Conference on Frontiers of Information Technology

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Due to widespread availability of WiFi networks in buildings, indoor location based systems become a reality. To provide indoor location based services (LBS), finding the current location of a human, a computer, a mobile device or equipment such as a small UAV (like Quadcopter) is of great interest. The most prominent method for this purpose is the received signal strength (RSS)-based location from WiFi Access Points (APs) inside a building. Considerable amount of research is carried out in estimating current location and providing different services based on different wireless technologies. On the other hand, little attention has been paid to study and analyze the behavior of the received signal strength itself. It is challenging because the intensity of signal can change very frequently due to environmental features like topology, temperature, interaction with objects etc. In this paper, we study the behavior of WiFi signals in an indoor environment for RSS based localization by analyzing signals from three different access points by using triangulation technique. Our method is based on fingerprinting method. The experimental results reveal that the behavior of signal changes very frequently. The results lead us to the conclusion that understanding signals behavior is important before estimating current location and providing different LBS.

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“…Ekahau Positioning Engine [12] and Cisco Wireless Location Appliance [9] are examples of two famous commercial location systems in the second category. In addition, some studies [15,31,45,46] have developed tracking systems based on localization systems. Most of these systems have been surveyed in several papers [8,18,28].…”

Section: Introductionmentioning

confidence: 99%