梯度计
水下
声纳
磁强计
老板
海洋工程
遥感
声学
计算机科学
遥控水下航行器
航空航天工程
工程类
电气工程
地质学
物理
磁场
人工智能
机械工程
海洋学
机器人
量子力学
移动机器人
作者
T.R. Clem,George I. Allen,J. Bono,Robert J. McDonald,D. Overway,G. Sulzberger,Sankaran Kumar,David A. King
标识
DOI:10.1109/oceans.2004.1405594
摘要
Optical identification, which can provide compelling confirmation that a proud sonar contact is a mine, is obviously not possible for fully buried mines. One interesting sensor-fusion concept under consideration to confirm buried sonar contacts is to reacquire the contacts at short range using an unmanned underwater vehicle (UUV) carrying a magnetic sensor and a bottom-looking sonar, such as the Buried Object Scanning Sonar (BOSS) developed by Florida Atlantic University. Two magnetic sensors are currently being developed by the Office of Naval Research (ONR) for buried minehunting. The Quantum Magnetics' Realtime Tracking Gradiometer (RTG) is a multichannel tensor gradiometer being developed using fluxgate technology. An existing RTG prototype has been integrated with an existing BOSS prototype onboard a tow body and underwater tow tests have been conducted over targets to demonstrate the effectiveness of the BOSS/RTG fusion concept. A new miniaturized version of the RTG is being developed for operation onboard relatively small unmanned underwater vehicles (UUVs). The Polatomic Laser Scalar Gradiometer (LSG) is a multichannel scalar magnetic sensor designed to operate on relatively small UUVs. It is based on the electron-spin resonance (ESR) properties of helium-4 gas in accordance with the Zeeman effect, very similar in concept to the US Navy's AN/ASQ-208. The LSG has attained increased sensitivity over the AN/ASQ-81 by the use of a laser in place of incoherent light for optical pumping. In this paper, the sensors and system configurations being pursued are discussed. Relevant tests that have been conducted for individual sensors and sensor combinations and test results are described. Current status of these developments and future plans to test and demonstrate these technologies and concepts are presented.
科研通智能强力驱动
Strongly Powered by AbleSci AI