<p>The traditional approach of Magnetic Tracking Systems (MTS) utilizes approximate models and Parameter Extraction (PE) for Position and Orientation (P&O) determination. The approximate models give inaccurate P&O information outside the "constrained region". PE is an iterative, intensive process for P&O calculations, which limits the speed of the tracking process.</p> <p>Our MTS approach aims at accurate real-time P&O tracking. We utilize Artificial Neural Networks (ANN) with PE functionality to carry out the computational task for real-time P&O tracking. We apply Space Mapping (SM) modeling afterwards for system calibration to improve the accuracy of P&0 determination</p> <p>This thesis addresses a different approach for P&O determination. The main motivation of this work is to determine the P&O in a fast and accurate manner. It this work, we mathematically develop and experimentally implement our MTS for both 2-D and 3-D examples. The results show good match between our extracted P&O based on our MTS approach and the actual P&0 measured values.</p> / Master of Applied Science (MASc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/14034 |
| Date | 09 1900 |
| Creators | Wang, Kai |
| Contributors | Bakr, Mohamed H., Deen, Jamal, Electrical and Computer Engineering |
| Source Sets | McMaster University |
| Detected Language | English |
| Type | thesis |
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