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Title Real Time Dynamic Indoor Positioning using Machine Learning Techniques

Author(s) Haseeb Bari

Abstract Position estimation is the process to find the actual location of an object with reference to some coordinate system or known landmark. This thesis focuses on position estimation of an object dynamically moving in an indoor environment. Previous studies focused more on static position estimation and used traditional position estimation techniques. In traditional position estimation techniques, RSSI measurements are used for distance estimation, which requires modelling of radio propagation to get distance estimates. Modelling of radio propagation in indoor environment is a challenging task due to multipath fading, reflection, refraction of light, temperature and presence of humans etc. All these parameters affecting the received signal and produces variations in RSSI. Due to variations in RSSI, distance and position estimation error occurs. To address the issue, this thesis presents fingerprinting based position estimation with the help of machine learning. Our proposed machine learning based indoor position estimation system consists of two steps. In step one, we perform real time experiments using Bluetooth Low Energy (BLE) Beacons and developed a radio fingerprints map. In second step, we investigated five different types of machine learning techniques. These techniques are Naive Bayes, K-Nearest Neighbours (KNN), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) and Decision Tree in order to enhance position estimation accuracy especially for mobile objects. Real time experiments are performed to evaluate performance of our proposed real time dynamic object tracking system, using five different trajectories in a 10 x 10 meters’ indoor setup. These trajectories represent real time dynamic movement in different directions and speed. Experimental results show that LDA achieved highest mean accuracy of 79.34 % followed by SVM 78.38 %, while K-NN achieved 70.04 %. Keywords: Position Estimation, Localization, Bluetooth, RSSI, Machine Learning.

Type Thesis/Dissertation MS

Faculty Engineering and Computer Science

Department Computer Science

Language English

Publication Date 2020-01-14

Subject Computer Science, Indoor Positioning System

Publisher NUML

Contributor(s) Haseeb Bari, Dr. Fazli Subhan ( Supervisor)

Format PDF,

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Rights NUML