Resource allocation of distributed MIMO radar based on the hybrid action space reinforcement learning
Abstract The distributed multiple-input multiple-output (MIMO) radar system exhibits superior target localization capability by jointly processing target information from multiple radars under different observation angles. To improve the resource utilization of the distributed MIMO radar system, thi...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-06-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-02698-1 |
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| author | Jin Zhu Wenxu Liu Feifei Lyu Siwei Li Tianyang Zhang |
| author_facet | Jin Zhu Wenxu Liu Feifei Lyu Siwei Li Tianyang Zhang |
| author_sort | Jin Zhu |
| collection | DOAJ |
| description | Abstract The distributed multiple-input multiple-output (MIMO) radar system exhibits superior target localization capability by jointly processing target information from multiple radars under different observation angles. To improve the resource utilization of the distributed MIMO radar system, this paper proposes a hybrid action space reinforcement learning (HAS-RL) method, aiming to maximize the target localization performance under the radar resource constraints. Specifically, the Cramer–Rao Lower Bound (CRLB) incorporating the transmit radar power and receive radar selection is first derived and employed as the target localization performance metric of the distributed MIMO radar system. Subsequently, the radar resource allocation problem is modeled as a constrained optimization problem with continuous and discrete variables, and a hybrid action space reinforcement learning is proposed to solve the above optimization problem. Simulation results demonstrate that the proposed HAS-RL method can obtain better target localization performance under the given radar resource constraints. |
| format | Article |
| id | doaj-art-e12b828282ca4c5db0117c5e21687f8b |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e12b828282ca4c5db0117c5e21687f8b2025-08-20T02:31:04ZengNature PortfolioScientific Reports2045-23222025-06-0115111110.1038/s41598-025-02698-1Resource allocation of distributed MIMO radar based on the hybrid action space reinforcement learningJin Zhu0Wenxu Liu1Feifei Lyu2Siwei Li3Tianyang Zhang4School of Artificial Intelligence, Xidian UniversityCETC Key Laboratory of Aerospace Information Applications, The 54th Research Institute of China Electronics Technology Group CorporationCETC Key Laboratory of Aerospace Information Applications, The 54th Research Institute of China Electronics Technology Group CorporationSchool of Artificial Intelligence, Xidian UniversitySchool of Artificial Intelligence, Xidian UniversityAbstract The distributed multiple-input multiple-output (MIMO) radar system exhibits superior target localization capability by jointly processing target information from multiple radars under different observation angles. To improve the resource utilization of the distributed MIMO radar system, this paper proposes a hybrid action space reinforcement learning (HAS-RL) method, aiming to maximize the target localization performance under the radar resource constraints. Specifically, the Cramer–Rao Lower Bound (CRLB) incorporating the transmit radar power and receive radar selection is first derived and employed as the target localization performance metric of the distributed MIMO radar system. Subsequently, the radar resource allocation problem is modeled as a constrained optimization problem with continuous and discrete variables, and a hybrid action space reinforcement learning is proposed to solve the above optimization problem. Simulation results demonstrate that the proposed HAS-RL method can obtain better target localization performance under the given radar resource constraints.https://doi.org/10.1038/s41598-025-02698-1Resource allocationRadar signal processingReinforcement learning |
| spellingShingle | Jin Zhu Wenxu Liu Feifei Lyu Siwei Li Tianyang Zhang Resource allocation of distributed MIMO radar based on the hybrid action space reinforcement learning Scientific Reports Resource allocation Radar signal processing Reinforcement learning |
| title | Resource allocation of distributed MIMO radar based on the hybrid action space reinforcement learning |
| title_full | Resource allocation of distributed MIMO radar based on the hybrid action space reinforcement learning |
| title_fullStr | Resource allocation of distributed MIMO radar based on the hybrid action space reinforcement learning |
| title_full_unstemmed | Resource allocation of distributed MIMO radar based on the hybrid action space reinforcement learning |
| title_short | Resource allocation of distributed MIMO radar based on the hybrid action space reinforcement learning |
| title_sort | resource allocation of distributed mimo radar based on the hybrid action space reinforcement learning |
| topic | Resource allocation Radar signal processing Reinforcement learning |
| url | https://doi.org/10.1038/s41598-025-02698-1 |
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