| Presentation | 1995/4/28 Pattern Recognition Of Radar Target by means of Unsupervised Neural Network Vector Quantizer Chih-ping Lin, Motoaki Sano, Matsuo Sekine, |
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| PDF Download Page | PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | The ship target embedded in sea clutter was observed by using millimeter wave (MMW) radar. We use the unsupervised neural network vector quantizer to discriminate the target and sea clutter. The general unsupervised competitive learning algorithm was modified to be suitable for learning of MMW radar image by inserting neighborhood factor. The method we propose owns simple, low computational load, and no interative learning characteristics. We can separate the target and clutter in MMW radar image sucessfully. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Vector quantization / Neural network / Radar / Clutter / Target |
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| Conference Information | |
| Committee | SANE |
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| Conference Date | 1995/4/28(1days) |
| Place (in Japanese) | (See Japanese page) |
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| Topics (in Japanese) | (See Japanese page) |
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| Registration To | Space, Aeronautical and Navigational Electronics (SANE) |
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| Language | ENG |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Pattern Recognition Of Radar Target by means of Unsupervised Neural Network Vector Quantizer |
| Sub Title (in English) | |
| Keyword(1) | Vector quantization |
| Keyword(2) | Neural network |
| Keyword(3) | Radar |
| Keyword(4) | Clutter |
| Keyword(5) | Target |
| 1st Author's Name | Chih-ping Lin |
| 1st Author's Affiliation | The Graduate Shool at Nagatsuta, Tokyo Institute of Technology() |
| 2nd Author's Name | Motoaki Sano |
| 2nd Author's Affiliation | The Graduate Shool at Nagatsuta, Tokyo Institute of Technology |
| 3rd Author's Name | Matsuo Sekine |
| 3rd Author's Affiliation | The Graduate Shool at Nagatsuta, Tokyo Institute of Technology |
| Date | 1995/4/28 |
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| Volume (vol) | vol.95 |
| Number (no) | 26 |
| Page | pp.pp.- |
| #Pages | 8 |
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