Multi-ship Formation Recognition for HFSWR in a Long Coherent Integration Time
The deep learning method has been proven to be perfect in the field of multi-ship formation (MSF) recognition for high-frequency surface wave radar (HFSWR). However, the range-Doppler (RD) images of MSF are not always available in large quantities for training. And there is diversification in format...
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| Veröffentlicht in: | IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences Communications and Computer Sciences, 2024, pp.2024EAL2061 |
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| container_title | IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences |
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| creator | Wang, Jiaqi Liu, Aijun Yu, Changjun |
| description | The deep learning method has been proven to be perfect in the field of multi-ship formation (MSF) recognition for high-frequency surface wave radar (HFSWR). However, the range-Doppler (RD) images of MSF are not always available in large quantities for training. And there is diversification in formation styles. In this paper, we propose a signal processing method for HFSWR formation recognition, which performs RD imaging through coherent accumulation and motion compensation. In the Doppler profile, the peaks are equal to sub-targets. The experiments based on actual RD background verify the feasibility and robustness of the proposed method. |
| doi_str_mv | 10.1587/transfun.2024EAL2061 |
| format | Article |
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| ispartof | IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 2024, pp.2024EAL2061 |
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| subjects | formation recognition high-frequency surface wave radar motion compensate robustness |
| title | Multi-ship Formation Recognition for HFSWR in a Long Coherent Integration Time |
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