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Title Hybrid Indoor Position Estimation Technique using Fingerprinting and MinMax Approach

Author(s) Shakeel Ahmed

Abstract Position estimation means locating position with reference to some coordinate system, i.e. two dimensional (x, y) or with reference of an object to some known land mark. This thesis focuses on indoor position estimation using Bluetooth, which is a low cost, easily available Radio Frequency (RF) based wireless technology. Most of the latest indoor positioning systems use Bluetooth due to its low cost and wide spread use in most of the wireless gadgets including smart phones, digital watches, and other handheld devices. Accuracy is one of the most challenging issues in position estimation, which depends on accurate signal transmission and reception, conversion of received signal to distance estimates and modeling of distance estimates to localize object position. Position estimation consists of two main steps, signal measurements and position estimation based on signal. In this thesis, we have focused on both steps, i.e. signal modeling and localization or position estimation. In step one, we perform real time experiments to collect Bluetooth signal measurements, i.e. Received Signal Strength Indicator (RSSI), which is a parameter widely used for distance and position estimation. Experimental and simulation results conclude that there is 10 dBm variation in RSSI due to additive noise, multipath, shadowing, interferences with physical objects and hence affect distance estimation, which ultimately results in position estimation error. Real time experimental results validate this variation, and conclude that if optimized radio propagational constants are chosen, position estimation accuracy up to 1.32 m can be achieved in the presence of 10 dBm variation in the radio signal. In step two, we propose a new hybrid position estimation approach which integrates fingerprinting based K-Nearest Neighbors (K-NN) and lateration based MinMax position estimation technique. The novel idea in our proposed hybrid approach is use of Euclidian distance formulation instead of indoor radio propagation model to convert the signal to distance. We have tested our proposed hybrid position estimation technique in Matlab 7.1 using real time experimental data and compared its results with fingerprinting and lateration based position estimation techniques. Simulation results show that, the proposed hybrid approach performs better in terms of mean error compared to Trilateration, MinMax, K-NN, and existing Hybrid approach. Keywords: Localization, Distance Estimation, Fingerprinting, K-NN, MinMax, Trilateration

Type Thesis/Dissertation MS

Faculty Engineering and Computer Science

Department Computer Science

Language English

Publication Date 2019-03-13

Subject Computer Science, Indoor Positioning System

Publisher NUML

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Format PDF

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Relation

Coverage

Rights NUML

Category MSCS Thesis

Description MSCS Thesis by Shakeel Ahmed