| 机动目标跟踪时组网机会阵雷达功率分配算法 |
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| 引用本文： | 韩清华,龙伟军,杨 振,陈 军,汪 飞.机动目标跟踪时组网机会阵雷达功率分配算法[J].雷达科学与技术,2023,21(1):16-23. |
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| 作者姓名： | 韩清华 龙伟军 杨 振 陈 军 汪 飞 |
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| 作者单位： | 1. 南京信息工程大学电子与信息工程学院， 江苏南京 210044；2. 枣庄学院人工智能学院， 山东枣庄 277160；3. 南京航空航天大学雷达成像与微波光子技术教育部重点实验室， 江苏南京 211106 |
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| 基金项目： | 国家自然科学基金(No.62071440)；航空自然科学基金(No.20200020052005)；江苏省自然科学基金(No.BK20190772)；中国高校产学研创新基金(No.2021ITA09023)；雷达成像与微波光子技术教育部重点实验室基金（南京航空航天大学）（No.NJ20210005)；枣庄学院“青檀学者”人才项目经费资助 |
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| 摘 要： | 针对机动目标的高动态属性导致雷达系统不能精确地分配系统资源问题，本文提出了一种基于改进的当前统计模型的组网机会阵雷达功率分配算法。该算法通过改进的当前统计模型预测机动目标运动状态，采用预测的条件克拉美罗界作为功率分配时目标跟踪性能的衡量基准。针对目标信息的不确定性，引入随机变量表征目标RCS，建立基于机会约束规划的功率资源分配模型，并设计混合智能优化算法求解满足机会约束的最优功率分配。仿真结果表明，预测的条件克拉美罗界能够提供一个更加精确的跟踪性能衡量边界，该算法能够有效提高雷达系统资源利用率。
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| 关 键 词： | 机动目标 机会阵雷达 功率资源分配 目标跟踪 机会约束规划 |
Power Allocation Algorithm of Netted Opportunistic Array Radar for Maneuvering Target Tracking |
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| HAN Qinghu,LONG Weijun,YANG Zhen,CHEN Jun,WANG Fei.Power Allocation Algorithm of Netted Opportunistic Array Radar for Maneuvering Target Tracking[J].Radar Science and Technology,2023,21(1):16-23. |
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| Authors: | HAN Qinghu LONG Weijun YANG Zhen CHEN Jun WANG Fei |
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| Institution: | 1. School of Electronic and Information Engineering, Nanjing University of Information Science and Technology,Nanjing 210044, China; 2. School of Artificial Intelligence, Zaozhuang University, Zaozhuang 277160, China;3. Key Laboratory of Radar Imaging and Microwave Photonics, Nanjing University of Aeronautics and Astronautics,Nanjing 211106, China |
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| Abstract: | Aiming at the problem that the radar system cannot allocate system resources accurately due to the high dynamic attributes of maneuvering targets, a power allocation algorithm based on the modified current statistical model is proposed for netted opportunistic array radar system in this paper. This algorithm predicts the motion state of the maneuvering target through the modified current statistical model, and the predicted conditional Cramér?Rao lower bound is employed as the criterion of the target tracking performance during power allocation. For the uncertainty of target information, random variables are introduced to characterize the target RCS. A power resource allocation model based on chance?constrained programming is established, and a hybrid intelligent optimization algorithm is designed to solve the optimal power allocation under the chance constraints. The simulation results show that the predicted conditional Cramér?Rao lower bound can provide a more accurate measurement bound for tracking performance, and the algorithm can effectively improve the utilization of radar system resources. |
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