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Proceedings ArticleDOI

A multi-pronged approach for indoor positioning with WiFi, magnetic and cellular signals

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TLDR
Preliminary measurements show that magnetic field strength and cellular signal strength satisfy three properties needed for localization: time-in-variance, location representativeness and universality, and how much accuracy is achieved by the hybrid localization.
Abstract
As smartphones are increasingly popular, location-based services (LBSs) have become one of the crucial applications in daily lives. While outdoor localization is relatively easy leveraging GPS signals, localization in indoor environments is difficult due to the lack of GPS. Thus, due to the pervasive deployment of WiFi access points, there have been numerous studies on WiFi based indoor positioning. However, the multi-path fading of WiFi signals causes time-varying received signal strengths of WiFi signals, which leads to poor accuracy of WiFi localization. Moreover, WiFi scanning period, about 3∼4 seconds in general smartphone, may provide poor quality of services in the context of refreshment interval. Motivated by these limitations, we study the usability of tw o other sources that are currently available in smartphones: magnetic field strength and cellular signal strength, for indoor positioning purposes. Our preliminary measurements show that these two sources satisfy three properties needed for localization: time-in-variance, location representativeness and universality. Because these three sensors have their own characteristics, some problems in single sensory data based localization could be complementarily overcome. We will show how to combine the three smartphone sensory data for indoor positioning based on each module's characteristics, and how much accuracy is achieved by the hybrid localization.

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Citations
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Journal ArticleDOI

Review of Indoor Positioning: Radio Wave Technology

TL;DR: The recent advanced algorithms can offer precise positioning behaviour for an unknown environment in indoor locations as well as the traditional ranging parameters in addition to advanced parameters such as channel state information (CSI), reference signal received power (RSRP), andreference signal received quality (RSRQ) are presented.
Journal ArticleDOI

An Indoor Positioning System Based on Static Objects in Large Indoor Scenes by Using Smartphone Cameras.

TL;DR: An indoor positioning system to locate users in large indoor scenes by using smartphone cameras and integrating algorithms of deep learning and computer vision that is able to achieve positioning accuracy within 1 m.

A Drift Eliminated Attitude & Position Estimation Algorithm In 3D

TL;DR: The low-computation cost complementary filter is employed with a heuristic drift elimination method that is shown to remove, almost entirely, the drift caused by the gyroscope and hence generate a fairly accurate attitude and drift-eliminated position estimate.
Proceedings ArticleDOI

A smart parking system using WiFi and wireless sensor network

TL;DR: A smart parking system combining WiFi and wireless sensor network is proposed, in which geomagnetic sensors are used to detect the occupation of parking spaces, and WiFi is used for navigation.
Book ChapterDOI

Smartphone-Based Indoor Positioning Technologies

TL;DR: This chapter focuses on indoor positioning technologies with smartphones, and in particular, emphasize the technologies based on radio frequency (RF) and built-in sensors.
References
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Proceedings ArticleDOI

RADAR: an in-building RF-based user location and tracking system

TL;DR: RADAR is presented, a radio-frequency (RF)-based system for locating and tracking users inside buildings that combines empirical measurements with signal propagation modeling to determine user location and thereby enable location-aware services and applications.
Proceedings ArticleDOI

Indoor localization without the pain

TL;DR: Despite the absence of any explicit pre-deployment calibration, EZ yields a median localization error of 2m and 7m in a small building and a large building, which is only somewhat worse than the 0.7m and 4m yielded by the best-performing but calibration-intensive Horus scheme from prior work.
Proceedings ArticleDOI

Calibration-free WLAN location system based on dynamic mapping of signal strength

TL;DR: This work addressed issues related to some aspects of location systems through, an architecture based on wireless sniffers and by constructing a location model based on signal propagation models, in which its parameters are calculated in real time.
Proceedings ArticleDOI

A global self-localization technique utilizing local anomalies of the ambient magnetic field

TL;DR: A Monte Carlo Localization (MCL) technique based on the hypothesis that the ambient magnetic field may remain sufficiently stable for longer periods of time, giving support for self-localization techniques utilizing the local deviations of the field.
Journal ArticleDOI

Outdoor-to-indoor signal correlation and its influence on indoor system performance at 2 GHz

TL;DR: In this article, the levels of correlation between the transmissions of outdoor and indoor base stations are found to significantly influence indoor system performance and therefore the choice of indoor base station deployment strategy.
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How do I make my WIFI signal stable?

However, the multi-path fading of WiFi signals causes time-varying received signal strengths of WiFi signals, which leads to poor accuracy of WiFi localization.