| 基于确定压缩采样的矢量水听器阵列舰船目标方位估计 |
| |
| 引用本文： | 井岩,沈毅,冯乃章,万广南,孙明健.基于确定压缩采样的矢量水听器阵列舰船目标方位估计[J].仪器仪表学报,2016,37(6):1267-1276. |
| |
| 作者姓名： | 井岩 沈毅 冯乃章 万广南 孙明健 |
| |
| 作者单位： | 哈尔滨工业大学（威海）,哈尔滨工业大学,哈尔滨工业大学（威海）,哈尔滨工业大学(威海),哈尔滨工业大学（威海） |
| |
| 基金项目： | 国家自然科学基金 (61371045);山东省重点研发计划(2015GGX103016);中国博士后科学基金面上项目（2015M571413）资助的课题; |
| |
| 摘 要： | 使用较少的硬件电路和计算量实现高分辨率的目标方位估计一直是海域防御系统具有挑战性的研究工作。本文将压缩感知理论应用于矢量水听器阵列，利用确定测量矩阵建立了确定压缩采样的矢量水听器阵列（deterministic compressive sampling vector hydrophone array，DCV）结构，并将其与改进的多重信号分类算法（modified multiple signal classification，MMUSIC）相结合，提出了确定压缩采样的矢量水听器阵列的MMUSIC（DCV-MMUSIC）算法。将该算法用于相关或非相关模拟舰船目标的方位估计，通过对算法的可行性、高分辨能力和改变测量次数、信噪比和快拍数等多种仿真，得出该算法可以在低信噪比和小快拍下对舰船目标进行方向估计，并且具有使用硬件电路少、计算量低和估计偏差小等优点。又将其用于实际商船目标的方位估计，得到了较好的方位估计性能。
|
| 关 键 词： | 确定测量矩阵 压缩采样 矢量水听器 舰船目标 方向估计 改进的多重信号分类算法 |
| 收稿时间： | 2016/3/23 0:00:00 |
| 修稿时间： | 2016/5/25 0:00:00 |
Bearing estimation based on deterministic compressive sampling vector hydrophone array for ship target |
| |
| Affiliation: | 1. School of Astronautics, Harbin Institute of Technology,Harbin 150080, China; 2. School of Information
and Electrical Engineering, Harbin Institute of Technology at Weihai, Weihai 264209, China,School of Astronautics, Harbin Institute of Technology,Harbin 150080, China,School of Information and Electrical Engineering, Harbin Institute of Technology at Weihai, Weihai 264209, China,School of Information and Electrical Engineering, Harbin Institute of Technology at Weihai, Weihai 264209, China and School of Information and Electrical Engineering, Harbin Institute of Technology at Weihai, Weihai 264209, China |
| |
| Abstract: | The bearing estimation with higher resolution, less hardware circuit and computational cost is a challenging work for port defense systems. In this paper, the structure of deterministic compressive sampling vector hydrophone array (DCV) is obtained by deterministic measurement matrix under compressive sensing frame. A novel bearing estimation algorithm, named as DCV-MMUSIC, is proposed by extending modified multiple signal classification (MMUSIC) to DCV architecture, which can be used to bearing estimation of correlated or uncorrelated simulative ship target. By the various simulation of feasibility, high resolution, different of measurements, signal noise ratio (SNR) and snap, which draw conclusions that this algorithm can be applied to bearing estimation of ship target in lower SNR and fewer snap, and has advantages of less computational cost, hardware structure and estimation bias. The algorithm is used to bearing estimation of actual merchant ship, and better performance of bearing estimation is obtained. |
| |
| Keywords: | deterministic measurement matrix compressive sampling vector hydrophone ship target bearing estimation modified multiple signal classification algorithm |
|
| 点击此处可从《仪器仪表学报》浏览原始摘要信息 |
|
点击此处可从《仪器仪表学报》下载全文 |
|
