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Indoor localisation of a mobile agent using prototype based techniques

Bwana, Robert (2018) Indoor localisation of a mobile agent using prototype based techniques. Master's Internship Report, Computing Science.

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The adoption and use of mobile agents relies on the ability to determine their positions. While mobile agents in outdoor environments can make use of GPS systems or topographic imaging, indoor mobile agents may not be able to reliably do so. This research sets out to investigate the potential of mobile agents to determine their indoor position without the use of any extra marking or assisting technologies. I attempt to determine locality using dimensionality reduction and mapping algorithms to map information inherent in the working environment to the location of the mobile agent. While many linear and non-linear mapping algorithms are available, my research, outlining one of two approaches taken, focuses on using the Self-Organised Neighbour Embedding (SONE) algorithm and the Self- Organised Map (SOM) algorithm. These had been proven in prior research to successfully map higher dimensional data to determine locality in lower dimensional space. The approach would focus on using wireless local area network signal strengths which the mobile agent would be able to measure with pre-existing inbuilt capabilities. This wireless signal information was gathered over a period of 2 weeks at an industrial warehouse representing real world usage scenarios. The results of the research suggest that while the wireless signal data does indeed contain information which could be of use in indoor localisation, the 2 unsupervised learning algorithms used were not sufficient to overcome the inherent noise in the data. This would indicate that semi-supervised or probabilistic embedding methods could yet manage to successfully map the wireless signal information onto the lower dimensional geographic space.

Item Type: Thesis (Master's Internship Report)
Supervisor name: Bunte, K.
Degree programme: Computing Science
Thesis type: Master's Internship Report
Language: English
Date Deposited: 31 Jul 2018
Last Modified: 01 Aug 2018 12:41

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