| 引用本文: | 马伯乐,朱世强,孙贵青.基于特征值的单矢量水听器目标检测算法[J].国防科技大学学报,2019,41(1):95-100.[点击复制] |
| MA Bole,ZHU Shiqiang,SUN Guiqing.Single vector hydrophone target detection based on eigenvalue[J].Journal of National University of Defense Technology,2019,41(1):95-100[点击复制] |
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| 基于特征值的单矢量水听器目标检测算法 |
| 马伯乐1,2, 朱世强1,3, 孙贵青1 |
| (1. 浙江大学 海洋学院， 浙江 舟山 316000;2. 中国人民解放军92721部队， 浙江 舟山 316000;3. 之江实验室， 浙江 杭州 310000) |
| 摘要: |
| 针对水下目标检测在低信噪比与非平稳背景噪声情况下性能下降的问题，结合特征值检测算法原理，给出一种单矢量水听器联合信息互相关检测算法。该算法利用电子旋转导向角度与振速信息构成一种组合振速，并结合声压信息得到一种互相关值，在大快拍无信号条件下，该值满足渐进高斯分布；将该值除以解析振速与声压信息的协方差矩阵最小特征值，得到一种检测统计量；通过与门限值比较，实现目标检测。理论分析可见，所提检测算法无须背景噪声的先验信息，并且可以通过调节导向角度提高检测性能；在单目标情况下，利用检测统计量与导向角度的对应关系可实现目标方位估计。仿真与实测数据结果表明，相比于单矢量水听器最大最小特征值检测算法与能量检测算法，所提算法检测性能优良，适合于单矢量水听器目标预警检测。 |
| 关键词: 单矢量水听器 最大最小特征值检测 能量检测 |
| DOI：10.11887/j.cn.201901014 |
| 投稿日期：2017-11-15 |
| 基金项目:国家自然科学基金资助项目（50909096） |
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| Single vector hydrophone target detection based on eigenvalue |
| MA Bole1,2, ZHU Shiqiang1,3, SUN Guiqing1 |
| (1. Ocean College, Zhejiang University, Zhoushan 316000, China;2.
2. The PLA Unit 92721, Zhoushan 316000, China;3. Zhejiang Lab, Hangzhou 310000, China) |
| Abstract: |
| Aiming at solving the decline of detection performance under low signal noise ratio and nonstationary background noise and combining the principle of eigenvalue detection, a combination information cross-correlation detection algorithm based on single vector hydrophone was presented. This algorithm makes an assemble velocity by using electronic rotation angle and velocity information, and obtains a cross-correlation value with pressure. This value satisfies the asymptotic Gaussian distribution under large snapshot without target signal. This value is divided by minimum eigenvalue of analytical velocity covariance matrix to get a detection statistic. Finally, compared with the threshold, the object detection is achieved. The analysis of theory shows this algorithm does not need to know any prior information of background noise, and the detection performance can be improved by adjusting the guiding angle. This algorithm can achieve bearing estimation by using the relationship between detection statistic and guiding angle as to single target. The simulation and real data prove the superiority of the proposed algorithm, compared with the maximum-minimum eigenvalue detection algorithm and the energy detection algorithm. |
| Key words: single vector hydrophone maximum-minimum eigenvalue-detection energy detection |
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