基于凸优化算法的水声传感器阵列综合
发布时间：2018-09-16 22:01
【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。
[Abstract]:Sparse array imaging technology has been widely studied in the field of underwater detection. Aiming at the problems of high sidelobe level and low imaging contrast caused by the distributed characteristics of sparse array, an array synthesis method based on weighted norm minimization is proposed for underwater ultrasonic sensor array. The objective function and constraint conditions are set up, several iterations are carried out, and the position of the array element and the element excitation are optimized simultaneously. Finally, the area array of 20 脳 20 is synthesized into a sparse array of 51 elements, and the sidelobe level is below -15 dB. The performance of beam map of sparse array synthesized by convex optimization array is compared with that of beam map synthesized by Chebyshev array, and the imaging simulation is carried out. The results show that the array obtained by convex optimization not only guarantees the quality of imaging, but also ensures the image quality. It can greatly reduce the number of emitter elements, reduce the complexity of the system, and obtain the expected sidelobe level.
【作者单位】： 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;南京信息工程大学江苏省气象传感网技术工程中心;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;南京邮电大学电子科技与工程学院;
【基金】：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
【分类号】：TH766;TP212
，
本文编号：2244954
[Abstract]:Sparse array imaging technology has been widely studied in the field of underwater detection. Aiming at the problems of high sidelobe level and low imaging contrast caused by the distributed characteristics of sparse array, an array synthesis method based on weighted norm minimization is proposed for underwater ultrasonic sensor array. The objective function and constraint conditions are set up, several iterations are carried out, and the position of the array element and the element excitation are optimized simultaneously. Finally, the area array of 20 脳 20 is synthesized into a sparse array of 51 elements, and the sidelobe level is below -15 dB. The performance of beam map of sparse array synthesized by convex optimization array is compared with that of beam map synthesized by Chebyshev array, and the imaging simulation is carried out. The results show that the array obtained by convex optimization not only guarantees the quality of imaging, but also ensures the image quality. It can greatly reduce the number of emitter elements, reduce the complexity of the system, and obtain the expected sidelobe level.
【作者单位】： 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;南京信息工程大学江苏省气象传感网技术工程中心;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;南京邮电大学电子科技与工程学院;
【基金】：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
【分类号】：TH766;TP212
，
本文编号：2244954
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