李文强,李鹏,姜路,蒋威,李剑乔.基于凸优化算法的水声传感器阵列综合[J].电子测量与仪器学报,2017,31(10):1614-1620
基于凸优化算法的水声传感器阵列综合
Underwater acoustic sensor array synthesis method based on convex optimization algorithm
  
DOI:10.13382/j.jemi.2017.10.013
中文关键词:  稀疏基阵  水下成像  凸优化  阵列综合
英文关键词:sparse array  underwater acoustic imaging  convex optimization  array synthesis
基金项目:江苏省重点研发计划社会发展项目(BE2015692)、国家自然科学基金(41075115)、江苏省第11批六大高峰人才项目(2014 XXRJ 006) 资助
作者单位
李文强 1. 南京信息工程大学 江苏省大气环境与装备技术协同创新中心南京210044;2. 南京信息工程大学江苏省气象传感网技术工程中心南京210044;3. 南京信息工程大学 江苏省气象探测与信息处理重点实验室南京210044 
李鹏 1. 南京信息工程大学 江苏省大气环境与装备技术协同创新中心南京210044;2. 南京信息工程大学江苏省气象传感网技术工程中心南京210044;3. 南京信息工程大学 江苏省气象探测与信息处理重点实验室南京210044 
姜路 1. 南京信息工程大学 江苏省大气环境与装备技术协同创新中心南京210044;2. 南京信息工程大学江苏省气象传感网技术工程中心南京210044;3. 南京信息工程大学 江苏省气象探测与信息处理重点实验室南京210044 
蒋威 1. 南京信息工程大学 江苏省大气环境与装备技术协同创新中心南京210044;2. 南京信息工程大学江苏省气象传感网技术工程中心南京210044;3. 南京信息工程大学 江苏省气象探测与信息处理重点实验室南京210044 
李剑乔 南京邮电大学 电子科技与工程学院南京210003 
AuthorInstitution
Li Wenqiang 1. Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China; 2.Jiangsu Meteorological Sensor Network Technology Engineering Center, Nanjing University of Information Science and Technology, Nanjing 210044, China; 3. Jiangsu Key Laboratory of Meteorological Observation and Information Procession, Nanjing University of Information Science and Technology, Nanjing 210044, China 
Li Peng 1. Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China; 2.Jiangsu Meteorological Sensor Network Technology Engineering Center, Nanjing University of Information Science and Technology, Nanjing 210044, China; 3. Jiangsu Key Laboratory of Meteorological Observation and Information Procession, Nanjing University of Information Science and Technology, Nanjing 210044, China 
Jiang Lu 1. Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China; 2.Jiangsu Meteorological Sensor Network Technology Engineering Center, Nanjing University of Information Science and Technology, Nanjing 210044, China; 3. Jiangsu Key Laboratory of Meteorological Observation and Information Procession, Nanjing University of Information Science and Technology, Nanjing 210044, China 
Jiang Wei 1. Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China; 2.Jiangsu Meteorological Sensor Network Technology Engineering Center, Nanjing University of Information Science and Technology, Nanjing 210044, China; 3. Jiangsu Key Laboratory of Meteorological Observation and Information Procession, Nanjing University of Information Science and Technology, Nanjing 210044, China 
Li Jianqiao School of Electronic Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China 
摘要点击次数: 2724
全文下载次数: 14092
中文摘要:
      稀疏基阵成像技术已在水下探测领域广为研究。针对稀疏基阵的分布特性带来的波束旁瓣电平升高、成像对比度降低等问题,提出一种基于加权范数最小化的阵列综合方法用于水下超声传感器阵列,设定目标函数及约束条件,进行多次的迭代,同时优化阵元位置与阵元激励,最后将20×20的面阵综合优化成51个阵元的稀疏基阵,旁瓣电平在-15 dB以下,并将凸优化阵列综合得到的稀疏基阵的波束图与切比雪夫阵列综合得到的波束图性能进行比较,且进行成像仿真,结果表明,采用凸优化获得的基阵在保证成像质量的同时,可以大幅减少发射阵元的数目,降低系统复杂性,并获得预期的旁瓣电平。
英文摘要:
      At present, sparse arrays have been widely used in the field of underwater acoustic imaging. Aiming at the distribution characteristics of sparse array raising the sidelobe level, reducing the imaging contrast and other issues, a kind of underwater acoustic sensor array synthesis method based on convex optimization algorithm is proposed. On the basis of the appropriate object function, this method makes multiple iteration operations on the planar array, and optimizes the position of the array element and the element excitation. Finally, the planar array of 20×20 elements is optimally integrated into sparse array of 51 elements, the sidelobe lever is under -15 dB. By comparing the beam pattern performance based on convex optimization algorithm with the Chebyshev arrays synthesis method, the results show that the sparse array optimized in this study not only ensures the imaging quality, but also significantly reduces the number of array elements, and reduces sidelobe level. In addition, it decreases the complexity of the system and the design cost.
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