ACCURATE INDOOR POSITION ESTIMATION TECHNIQUE USING FINGERPRINTING AND LATERATION-BASED APPROACH IN BLUETOOTH TECHNOLOGY
The first part consists of experimental analysis of Bluetooth signal parameters in order to select the best suitable parameter for position estimation. This part also presents a comprehensive experimental analysis to observe the relationship between signal parameters and distance, so that the main...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2012
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| Online Access: | http://utpedia.utp.edu.my/3322/1/FAZLI_SUBHAN.pdf http://utpedia.utp.edu.my/3322/ |
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| Summary: | The first part consists of experimental analysis of Bluetooth signal parameters in order to select the best suitable parameter for position estimation.
This part also presents a comprehensive experimental analysis to observe the relationship between signal parameters and distance, so that the main source of
distance estimation error can be identified. After selecting the best suitable parameter for position estimation, the next issue is to address the distance estimation error and identify its causes. This is handled in the second part of the thesis, which addresses
the problem of communication holes. It presents an extended Gradient RSS predictor and filter, which is used to predict and filter RSS measurements in communication holes. The prediction and filtering process is based on the selected signal parameter based on our experimental observations. The refined output measurements are then given to the position estimation algorithm, which is handled in the third part of thesis. The third part of this thesis presents a new filter based hybrid position estimation
technique, which integrates the features of fingerprinting and lateration approach. The novel approach used in the proposed hybrid approach is the use of Euclidian distance
formula for distance estimation instead of propagation model. Simulation and experimental results validate the performance of proposed hybrid technique and
improve the accuracy up to 53.64 % and 25.58 % compared to Lateration and fingerprinting approach, respectively. In summary, this thesis presents a complete framework for indoor position estimation using Bluetooth networks. |
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