Device-free target tracking
Radio tomographic imaging (RTI) is a device-free target localization algorithm that has been developed recently. It uses received signal strength changes of links in a wireless sensor network as the input to identify the target location at a specific time. We can localize the target without attachin...
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| Format: | Thesis |
| Language: | English |
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2016
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| Online Access: | http://hdl.handle.net/10356/68576 |
| _version_ | 1826120584023506944 |
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| author | Ni Yun |
| author2 | Francois Quitin |
| author_facet | Francois Quitin Ni Yun |
| author_sort | Ni Yun |
| collection | NTU |
| description | Radio tomographic imaging (RTI) is a device-free target localization algorithm that has been developed recently. It uses received signal strength changes of links in a wireless sensor network as the input to identify the target location at a specific time. We can localize the target without attaching a device to it. Compared with other device-free localization methods, RTI is relatively low cost.
The objective of this project is to 1) implement radio tomographic imaging algorithms on simulated data and 2) apply a recursive Bayesian filter to infer target trajectories over time. Specifically, we have implemented the Kalman and particle filter. It is shown with computer simulations that both types are able to improve the accuracy of target trajectory estimates. |
| first_indexed | 2024-10-01T05:19:08Z |
| format | Thesis |
| id | ntu-10356/68576 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:19:08Z |
| publishDate | 2016 |
| record_format | dspace |
| spelling | ntu-10356/685762023-07-04T15:06:34Z Device-free target tracking Ni Yun Francois Quitin School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering Radio tomographic imaging (RTI) is a device-free target localization algorithm that has been developed recently. It uses received signal strength changes of links in a wireless sensor network as the input to identify the target location at a specific time. We can localize the target without attaching a device to it. Compared with other device-free localization methods, RTI is relatively low cost. The objective of this project is to 1) implement radio tomographic imaging algorithms on simulated data and 2) apply a recursive Bayesian filter to infer target trajectories over time. Specifically, we have implemented the Kalman and particle filter. It is shown with computer simulations that both types are able to improve the accuracy of target trajectory estimates. Master of Science (Signal Processing) 2016-05-27T03:57:21Z 2016-05-27T03:57:21Z 2016 Thesis http://hdl.handle.net/10356/68576 en 68 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Ni Yun Device-free target tracking |
| title | Device-free target tracking |
| title_full | Device-free target tracking |
| title_fullStr | Device-free target tracking |
| title_full_unstemmed | Device-free target tracking |
| title_short | Device-free target tracking |
| title_sort | device free target tracking |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | http://hdl.handle.net/10356/68576 |
| work_keys_str_mv | AT niyun devicefreetargettracking |