| Presentation | 2021-11-04 A Radar Cross Section Analysis to Generate Micro-Doppler Signatures Simulation Data for Machine Learning Ryotaro Ohashi, Hiroshi Suenobu, Dai Sasakawa, Michio Takikawa, Yoshio Inasawa, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | We investigate the expansion of training data by simulated data for a machine learning model that identifies drones from micro-Doppler (μ-D) signatures. In order to obtain the μ-D signatures, the time characteristics of radar cross section (RCS) are needed. In this paper, we propose a simple, fast method to calculate the RCS of a scatter with rotator. In this method, the scatter is divided into moving parts and static parts, and the entire RCS of scatter is calculated by summation of the observation angle characteristics of scattered field of the moving parts and the scattered field of the static parts. The RCS and μ-D signature of a drone calculated by proposed method agree well with the full-model analysis and experimental results. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | radar cross section / micro-doppler / drone |
| Paper # | EMT2021-32 |
| Date of Issue | 2021-10-28 (EMT) |
| Conference Information | |
| Committee | EMT / IEE-EMT |
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| Conference Date | 2021/11/4(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Electromagnetic Theory, etc. |
| Chair | Hiroyuki Deguchi(Doshisha Univ.) / Akira Matsushima(Kumamoto Univ.) |
| Vice Chair | Hideki Kawaguchi(Muroran Inst. of Tech) |
| Secretary | Hideki Kawaguchi(Miyazaki Univ.) / (Mitsubishi Electric) |
| Assistant | Kazuki Niino(Kyoto Univ.) / Junichiro Sugisaka(Kitami Inst. of Tech.) |
| Paper Information | |
| Registration To | Technical Committee on Electromagnetic Theory / Technical Meeting on Electromagnetic Theory |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Radar Cross Section Analysis to Generate Micro-Doppler Signatures Simulation Data for Machine Learning |
| Sub Title (in English) | |
| Keyword(1) | radar cross section |
| Keyword(2) | micro-doppler |
| Keyword(3) | drone |
| 1st Author's Name | Ryotaro Ohashi |
| 1st Author's Affiliation | Mitsubishi Electric Corporation(Mitsubishi Electric Corp.) |
| 2nd Author's Name | Hiroshi Suenobu |
| 2nd Author's Affiliation | Mitsubishi Electric Corporation(Mitsubishi Electric Corp.) |
| 3rd Author's Name | Dai Sasakawa |
| 3rd Author's Affiliation | Mitsubishi Electric Corporation(Mitsubishi Electric Corp.) |
| 4th Author's Name | Michio Takikawa |
| 4th Author's Affiliation | Mitsubishi Electric Corporation(Mitsubishi Electric Corp.) |
| 5th Author's Name | Yoshio Inasawa |
| 5th Author's Affiliation | Mitsubishi Electric Corporation(Mitsubishi Electric Corp.) |
| Date | 2021-11-04 |
| Paper # | EMT2021-32 |
| Volume (vol) | vol.121 |
| Number (no) | EMT-226 |
| Page | pp.pp.19-24(EMT), |
| #Pages | 6 |
| Date of Issue | 2021-10-28 (EMT) |