系统工程与电子技术

• 传感器与信号处理 • 上一篇    下一篇

基于不确定相关机会规划的机会阵方向图综合

龙伟军1,2, 龚树凤3, 韩清华1, 商妮1, 魏海涛4   

  1. (1. 南京航空航天大学电子信息工程学院, 江苏 南京 210016; 2. 南京电子技术研究所,
    江苏 南京 210039; 3. 浙江工业大学信息工程学院, 浙江 杭州 310000;
    4.卫星导航系统与装备技术国家重点实验室, 河北 石家庄 050081)
  • 出版日期:2016-12-28 发布日期:2010-01-03

Pattern synthesis for OAR based on uncertain dependent-chance programming

LONG Weijun1,2, GONG Shufeng3, HAN Qinghua1, SHANG Ni1, WEI Haitao4   

  1. (1. College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;
    2. Nanjing Research Institute of Electronics Technology, Nanjing 210039, China; 3. College of Information
    Engineering, Zhejiang University of Technology, Hangzhou 310000, China; 4. State Key Laboratory of
    Satellite Navigation System and Equipment Technology, Shijiazhuang 050081, China)
  • Online:2016-12-28 Published:2010-01-03

摘要:

围绕机会阵雷达(opportunistic array radar, OAR)阵列动态机会组阵的资源管理问题,以面向多任务为应用需求背景,针对机会布置在平台3D空间多个区域内的天线单元,提出了一种基于现代数学不确定性理论中的相关机会约束规划方法用于机会阵方向图综合。该方法建立在不确定性理论和模糊数学基础上,考虑OAR大量天线单元空间位置分布的不确定性和各单元激励(开/关)状态的不确定性,用模糊随机变量来刻画不确定环境中的模糊性和随机性,在天线资源受约束的不确定条件下,建立不确定规划模型来实现方向图综合。并设计将遗传算法和模糊随机模拟算法相结合的智能混合优化算法以获得模型的最优解。最后利用仿真实例验证了不确定规划模型和所设计算法的可行性和鲁棒性。

Abstract:

The problem of the antennas pattern synthesis for opportunistic array radar (OAR) is studied. When antennas are distributed in different areas, a new pattern synthesis algorithm is proposed to solve multi-task antennas resource management. This algorithm is based on uncertain theory, which takes into consideration the uncertainty of the antenna elements,such as location distribution, elements excited state and other uncertainty factors. It adopts fuzzy random variables to describe the fuzziness and randomness in uncertain environments, and builds a fuzzy random dependent-chance programming model in the case of the antennas constrained. In order to obtain the optimal solution, a new hybrid intelligent searching method is designed, which includes genetic algorithms and fuzzy random simulation. Finally, simulation examples and results prove the validity and robust stability of the uncertain programming and the design.