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Indoor location tracking using Signal Strength Pinpoints

Takens, S.P. (2010) Indoor location tracking using Signal Strength Pinpoints. Master's Thesis / Essay, Computing Science.

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Abstract

There have been many research efforts on location awareness in an indoor environment. Most of them rely on specialized equipment or motion detectors. This research focuses on signal strengths from WiFi transmitters and uses these signal strengths to calibrate virtual pinpoints. A pinpoint is a collection of stored signal strengths over time on a predetermined location. These pinpoints can then be used to situate the surroundings of an environment and determine the current measurement its position by providing the necessary information needed to the tracking methods proposed in this paper. The experiments of this research are set up with low-end routers in an actual indoor work environment to get the baseline results with less than perfect circumstances. The tracking methods used are based on different locating techniques. These techniques vary from converting signal strength to distance, or using signal strength as a ratio difference between distances, to using signal strength to get the average distance variation in an area. All these techniques allow us to calculate the distance to the locations of the signals. These distances are then converted into a position by calculating the radical centre of the corresponding circles. The results of this research are satisfactory considering the conditions of the experiments. The precision of the tracking methods is good enough to locate the receiver in a small room for all methods. If the methods are combined the average precision is improved to a minimum distance of about one meter. For another testing purpose the signal strengths are also preset manually, this is done to proof that the tracking methods produce suitable positioning results. Which demonstrates that the methods would be capable of tracking throughout the environment.

Item Type: Thesis (Master's Thesis / Essay)
Degree programme: Computing Science
Thesis type: Master's Thesis / Essay
Language: English
Date Deposited: 15 Feb 2018 07:44
Last Modified: 15 Feb 2018 07:44
URI: http://fse.studenttheses.ub.rug.nl/id/eprint/9352

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