Efficient and robust localization for wireless networks
Interfacing the location of a source (Mobile Station) and a set of physical sensors (Base Stations) empowers the study of wireless localization algorithms, with the goal of performing more precise and sustainable detections. Conventional localization methodologies utilize various models to estimate...
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| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2010
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| Online Access: | http://hdl.handle.net/10356/40389 |
| _version_ | 1811685838327644160 |
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| author | Guan, Ming Tao. |
| author2 | Law Choi Look |
| author_facet | Law Choi Look Guan, Ming Tao. |
| author_sort | Guan, Ming Tao. |
| collection | NTU |
| description | Interfacing the location of a source (Mobile Station) and a set of physical sensors (Base Stations) empowers the study of wireless localization algorithms, with the goal of performing more precise and sustainable detections. Conventional localization methodologies utilize various models to estimate the location of the source node, including Angle of Arrival (AoA), Time of Arrival (ToA), Time Difference of Arrival (TDoA), and Received Signal Strength (RSS). Those methodologies have their own advantages and drawbacks, thus, a combined method is proposed by adding memory assistance (used in RSS) into TDoA localization system to enhance detection robustness. Moreover, source station and sensor stations communicate on transmit-receive basis in wireless networks, which makes the geometric setup of detection sensors important. An efficient algorithm is proposed to convert stationary source stations into temporary sensors, in order to maintain the detection area but save energy. Trilateration, least squares, and bounding boxes algorithms are also studied and utilized to gain a complete localization sequence. Last but not the least, Ant Colony Optimization (ACO) is studied to glue the robust memory assisted method and the efficient geometric algorithm together, with a synchronized weighted system. MATLAB simulations are conducted to support proposed methods, with basic investigations on the Monte Carlo (MC) technique and the Cramer Rao Lower Bound (CRLB). |
| first_indexed | 2024-10-01T04:50:53Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/40389 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:50:53Z |
| publishDate | 2010 |
| record_format | dspace |
| spelling | ntu-10356/403892023-07-07T17:09:22Z Efficient and robust localization for wireless networks Guan, Ming Tao. Law Choi Look School of Electrical and Electronic Engineering Positioning and Wireless Technology Centre Tony Quek DRNTU::Engineering::Electrical and electronic engineering::Wireless communication systems Interfacing the location of a source (Mobile Station) and a set of physical sensors (Base Stations) empowers the study of wireless localization algorithms, with the goal of performing more precise and sustainable detections. Conventional localization methodologies utilize various models to estimate the location of the source node, including Angle of Arrival (AoA), Time of Arrival (ToA), Time Difference of Arrival (TDoA), and Received Signal Strength (RSS). Those methodologies have their own advantages and drawbacks, thus, a combined method is proposed by adding memory assistance (used in RSS) into TDoA localization system to enhance detection robustness. Moreover, source station and sensor stations communicate on transmit-receive basis in wireless networks, which makes the geometric setup of detection sensors important. An efficient algorithm is proposed to convert stationary source stations into temporary sensors, in order to maintain the detection area but save energy. Trilateration, least squares, and bounding boxes algorithms are also studied and utilized to gain a complete localization sequence. Last but not the least, Ant Colony Optimization (ACO) is studied to glue the robust memory assisted method and the efficient geometric algorithm together, with a synchronized weighted system. MATLAB simulations are conducted to support proposed methods, with basic investigations on the Monte Carlo (MC) technique and the Cramer Rao Lower Bound (CRLB). Bachelor of Engineering 2010-06-15T04:37:38Z 2010-06-15T04:37:38Z 2010 2010 Final Year Project (FYP) http://hdl.handle.net/10356/40389 en Nanyang Technological University 91 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering::Wireless communication systems Guan, Ming Tao. Efficient and robust localization for wireless networks |
| title | Efficient and robust localization for wireless networks |
| title_full | Efficient and robust localization for wireless networks |
| title_fullStr | Efficient and robust localization for wireless networks |
| title_full_unstemmed | Efficient and robust localization for wireless networks |
| title_short | Efficient and robust localization for wireless networks |
| title_sort | efficient and robust localization for wireless networks |
| topic | DRNTU::Engineering::Electrical and electronic engineering::Wireless communication systems |
| url | http://hdl.handle.net/10356/40389 |
| work_keys_str_mv | AT guanmingtao efficientandrobustlocalizationforwirelessnetworks |