Autonomous data processing and behaviors for adaptive and collaborative underwater sensing
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2013
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| Online Access: | http://hdl.handle.net/1721.1/77025 |
| _version_ | 1811089008932945920 |
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| author | Rowe, Keja S |
| author2 | Henrik Schmidt. |
| author_facet | Henrik Schmidt. Rowe, Keja S |
| author_sort | Rowe, Keja S |
| collection | MIT |
| description | Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012. |
| first_indexed | 2024-09-23T14:12:23Z |
| format | Thesis |
| id | mit-1721.1/77025 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T14:12:23Z |
| publishDate | 2013 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/770252019-04-10T12:39:48Z Autonomous data processing and behaviors for adaptive and collaborative underwater sensing Rowe, Keja S Henrik Schmidt. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012. Cataloged from PDF version of thesis. Includes bibliographical references (p. 73). In this thesis, I designed, simulated and developed behaviors for active riverine data collection platforms. The current state-of-the-art in riverine data collection is plagued by several issues which I identify and address. I completed a real-time test of my behaviors to insure they worked as designed. Then, in a joint effort between the NATO Undersea Research Center (NURC) and Massachusetts Institute of Technology (MIT) I assisted the Shallow Water Autonomous Mine Sensing Initiative (SWAMSI)'11 experiment and demonstrated the viability of multi-static sonar tracking techniques for seabed and sub-seabed targets. By detecting the backscattered energy at the monostatic and several bi-static angles simultaneously, the probabilities of both target detection and target classification should be improved. However, due to equipment failure, we were not able to show the benefits of this technique. by Keja S. Rowe. M.Eng. 2013-02-14T15:39:39Z 2013-02-14T15:39:39Z 2012 2012 Thesis http://hdl.handle.net/1721.1/77025 825776206 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 73 p. application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Rowe, Keja S Autonomous data processing and behaviors for adaptive and collaborative underwater sensing |
| title | Autonomous data processing and behaviors for adaptive and collaborative underwater sensing |
| title_full | Autonomous data processing and behaviors for adaptive and collaborative underwater sensing |
| title_fullStr | Autonomous data processing and behaviors for adaptive and collaborative underwater sensing |
| title_full_unstemmed | Autonomous data processing and behaviors for adaptive and collaborative underwater sensing |
| title_short | Autonomous data processing and behaviors for adaptive and collaborative underwater sensing |
| title_sort | autonomous data processing and behaviors for adaptive and collaborative underwater sensing |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/77025 |
| work_keys_str_mv | AT rowekejas autonomousdataprocessingandbehaviorsforadaptiveandcollaborativeunderwatersensing |