Radar Waveform Design Method Based on Cascade Optimization Processing under Missing Clutter Prior Data
Cognitive radar waveform design often relies on accurate clutter prior information. When prior information data is missing, the constructed clutter model will be severely mismatched, affecting the radar’s ability to suppress clutter. Aiming at the radar waveform optimization problem under missing cl...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
China Science Publishing & Media Ltd. (CSPM)
2023-02-01
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| Series: | Leida xuebao |
| Subjects: | |
| Online Access: | https://radars.ac.cn/cn/article/doi/10.12000/JR22166 |
| _version_ | 1797421459144966144 |
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| author | Yingkui ZHANG Guohao SUN Suchuan ZHONG Xianxiang YU |
| author_facet | Yingkui ZHANG Guohao SUN Suchuan ZHONG Xianxiang YU |
| author_sort | Yingkui ZHANG |
| collection | DOAJ |
| description | Cognitive radar waveform design often relies on accurate clutter prior information. When prior information data is missing, the constructed clutter model will be severely mismatched, affecting the radar’s ability to suppress clutter. Aiming at the radar waveform optimization problem under missing clutter prior data, this paper establishes point and block-like missing scenarios under the completely random missing mechanism, designs a waveform optimization model with constant modulus and similarity constraints, and proposes a radar waveform training algorithm based on priority filling−reinforcement learning cascade optimization: that is, a cascade method in which the reinforcement learning agent interacts with the clutter environment repaired by a filling algorithm, with the optimization goal of maximizing the signal-to-noise ratio, and the optimal configuration strategy with waveform parameters is obtained through iterative training. Finally, simulations verify the superiority of the proposed algorithm under different missing probability conditions. The results show that the proposed algorithm outperforms the traditional non-cascading optimization algorithm, regarding clutter suppression and effectively improves the detection ability of radar. |
| first_indexed | 2024-03-09T07:17:44Z |
| format | Article |
| id | doaj.art-56c4185a0279478083c4ce8fb5e4cd7e |
| institution | Directory Open Access Journal |
| issn | 2095-283X |
| language | English |
| last_indexed | 2024-03-09T07:17:44Z |
| publishDate | 2023-02-01 |
| publisher | China Science Publishing & Media Ltd. (CSPM) |
| record_format | Article |
| series | Leida xuebao |
| spelling | doaj.art-56c4185a0279478083c4ce8fb5e4cd7e2023-12-03T08:12:23ZengChina Science Publishing & Media Ltd. (CSPM)Leida xuebao2095-283X2023-02-0112123524610.12000/JR22166R22166Radar Waveform Design Method Based on Cascade Optimization Processing under Missing Clutter Prior DataYingkui ZHANG0Guohao SUN1Suchuan ZHONG2Xianxiang YU3School of Aeronautics and Astronautics, Sichuan University, Chengdu 610207, ChinaSchool of Aeronautics and Astronautics, Sichuan University, Chengdu 610207, ChinaSchool of Aeronautics and Astronautics, Sichuan University, Chengdu 610207, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaCognitive radar waveform design often relies on accurate clutter prior information. When prior information data is missing, the constructed clutter model will be severely mismatched, affecting the radar’s ability to suppress clutter. Aiming at the radar waveform optimization problem under missing clutter prior data, this paper establishes point and block-like missing scenarios under the completely random missing mechanism, designs a waveform optimization model with constant modulus and similarity constraints, and proposes a radar waveform training algorithm based on priority filling−reinforcement learning cascade optimization: that is, a cascade method in which the reinforcement learning agent interacts with the clutter environment repaired by a filling algorithm, with the optimization goal of maximizing the signal-to-noise ratio, and the optimal configuration strategy with waveform parameters is obtained through iterative training. Finally, simulations verify the superiority of the proposed algorithm under different missing probability conditions. The results show that the proposed algorithm outperforms the traditional non-cascading optimization algorithm, regarding clutter suppression and effectively improves the detection ability of radar.https://radars.ac.cn/cn/article/doi/10.12000/JR22166waveform designclutter suppressionmissing clutter prior datapriority fillingreinforcement learningcascade optimization |
| spellingShingle | Yingkui ZHANG Guohao SUN Suchuan ZHONG Xianxiang YU Radar Waveform Design Method Based on Cascade Optimization Processing under Missing Clutter Prior Data Leida xuebao waveform design clutter suppression missing clutter prior data priority filling reinforcement learning cascade optimization |
| title | Radar Waveform Design Method Based on Cascade Optimization Processing under Missing Clutter Prior Data |
| title_full | Radar Waveform Design Method Based on Cascade Optimization Processing under Missing Clutter Prior Data |
| title_fullStr | Radar Waveform Design Method Based on Cascade Optimization Processing under Missing Clutter Prior Data |
| title_full_unstemmed | Radar Waveform Design Method Based on Cascade Optimization Processing under Missing Clutter Prior Data |
| title_short | Radar Waveform Design Method Based on Cascade Optimization Processing under Missing Clutter Prior Data |
| title_sort | radar waveform design method based on cascade optimization processing under missing clutter prior data |
| topic | waveform design clutter suppression missing clutter prior data priority filling reinforcement learning cascade optimization |
| url | https://radars.ac.cn/cn/article/doi/10.12000/JR22166 |
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