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Ship target recognition
Includes bibliographical references.
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| Main Author: | |
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| Other Authors: | |
| Format: | Thesis |
| Language: | English |
| Published: |
Department of Electrical Engineering
2014
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| _version_ | 1867613301338275840 |
|---|---|
| access_status_str | Open Access |
| author | Robinson, Anthony David |
| author2 | Inggs, Michael |
| author_browse | Inggs, Michael Robinson, Anthony David |
| author_facet | Inggs, Michael Robinson, Anthony David |
| author_sort | Robinson, Anthony David |
| collection | Thesis |
| description | Includes bibliographical references. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/9229 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:33:57.504Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2014 |
| publishDateRange | 2014 |
| publishDateSort | 2014 |
| publisher | Department of Electrical Engineering |
| publisherStr | Department of Electrical Engineering |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/9229 Ship target recognition Robinson, Anthony David Inggs, Michael Electrical Engineering Includes bibliographical references. In this report the classification of ship targets using a low resolution radar system is investigated. The thesis can be divided into two major parts. The first part summarizes research into the applications of neural networks to the low resolution non-cooperative ship target recognition problem. Three very different neural architectures are investigated and compared, namely; the Feedforward Network with Back-propagation, Kohonen's Supervised Learning Vector Quantization Network, and Simpson's Fuzzy Min-Max neural network. In all cases, pre-processing in the form of the Fourier-Modified Discrete Mellin Transform is used as a means of extracting feature vectors which are insensitive to the aspect angle of the radar. Classification tests are based on both simulated and real data. Classification accuracies of up to 93 are reported. The second part is of a purely investigative nature, and summarizes a body of research aimed at exploring new ground. The crux of this work is centered on the proposal to use synthetic range profiling in order to achieve a much higher range resolution (and hence better classification accuracies). Included in this work is a comprehensive investigation into the use of super-resolution and noise reducing eigendecomposition techniques. Algorithms investigated include the Principal Eigenvector Method, the Total Least Squares Method, and the MUSIC method. A final proposal for future research and development concerns the use of time domain averaging to improve the classification performance of the radar system. The use of an iterative correlation algorithm is investigated. 2014-11-05T17:19:28Z 2014-11-05T17:19:28Z 1996 Master Thesis Masters MSc http://hdl.handle.net/11427/9229 eng application/pdf Department of Electrical Engineering Faculty of Engineering and the Built Environment University of Cape Town |
| spellingShingle | Electrical Engineering Robinson, Anthony David Ship target recognition |
| thesis_degree_str | Master's |
| title | Ship target recognition |
| title_full | Ship target recognition |
| title_fullStr | Ship target recognition |
| title_full_unstemmed | Ship target recognition |
| title_short | Ship target recognition |
| title_sort | ship target recognition |
| topic | Electrical Engineering |
| url | http://hdl.handle.net/11427/9229 |
| work_keys_str_mv | AT robinsonanthonydavid shiptargetrecognition |