Autonomous underwater vehicle-based concurrent detection and classification of buried targets using higher order spectral analysis
Autonomous underwater vehicle-based concurrent detection and classification of buried targets using higher order spectral analysis
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DOI：
10.1109/joe.2006.872216
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发表时间：
2006-06
影响因子：
4.1
通讯作者：
M. Montanari;J. Edwards;H. Schmidt
中科院分区：
文献类型：
--
作者：
M. Montanari;J. Edwards;H. Schmidt
提出了一种基于自主式水下机器人（AUV）的水雷目标并行检测与分类（CDAC）处理方案。在检测阶段，AUV使用简单的能量检测器结合峰值跟踪机制来寻找感兴趣的对象。在检测时，处理机制改变为高阶光谱（HOS）分类过程。通过理论分析、仿真和海上试验，证明了该系统在降低水雷探测虚警率方面具有良好的应用前景。HOS分类机制也被证明在所有情况下都比经典谱估计有一些好处。在意大利海岸外的通用海洋学阵列技术声纳（GOATS 2002）实验期间，还在AUV上现场演示了系统概念的组件，而其他组件则使用全面的AUV声纳模拟框架进行了演示
This paper presents a processing concept for autonomous underwater vehicle (AUV)-based concurrent detection and classification (CDAC) of mine-like objects. In the detection phase, the AUV seeks objects of interest using a simple energy detector combined with a peak tracking mechanism. Upon detection, the processing mechanism changes to a higher order spectral (HOS) classification process. The system is demonstrated through theory, simulation and at-sea experiments to have promise in reducing the false alarm rate of mine detections. The HOS classification mechanism is also shown to have some benefit over classical spectral estimation in all cases. Components of the system concept were also demonstrated live onboard the AUV during the Generic Oceanographic Array Technology Sonar (GOATS 2002) experiment off the coast of Italy, while others are demonstrated using a comprehensive AUV sonar simulation framework