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Deep Net Localization - eavesdropping in mobile acoustic underwater sensor networks

机译：深度网络本地化-监听移动声水下传感器网络

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Navigation is a challenging task for underwater devices, as there is no global navigation system available. This often restricted Autonomous Underwater Vehicles (AUV) in their operations, either spatially because they depend on reference markers that need to be deployed on the mission side, or by dive time as they need to surface regularly to geo-reference themselves via satellite. For most applications the position and attitude information of an AUV is crucial for a meaningful interpretation of the collected data. In this paper, we present Deep Net Localization (DNL), a novel localization approach for stationary and mobile nodes in an acoustic underwater sensor network. For that purpose a communication protocol was developed that enables network nodes to self-localize their position and attitude in the network by eavesdropping on the communication of other nodes. This is achieved by combining modern acoustic USBL modems and pressure sensors with a Bayes estimator. Without the need to actively transmit acoustic signals, DNL saves energy and reduces the overall workload of the communication channel. Furthermore this minimizes interference effects with other acoustic sensors, like multi-beam sonar, which are essential for the measurement purpose of a sensor node. In order to assess the approach we first tested the general functioning in a Monte-Carlo-Simulation. Subsequently the method was evaluated with real data that was collected during a sea trial in the Middle Atlantic Ocean.
机译：对于水下设备，导航是一项艰巨的任务，因为没有可用的全球导航系统。这通常会限制自主式水下航行器（AUV）的运行，在空间上是因为它们依赖于需要部署在任务侧的参考标记，或者是由于他们需要定期浮出水面以通过卫星对其自身进行地理参考，因此需要潜水时间。对于大多数应用，AUV的位置和姿态信息对于有意义地解释所收集的数据至关重要。在本文中，我们介绍了深网定位（DNL），这是一种用于水下声传感器网络中固定节点和移动节点的新颖定位方法。为此目的，开发了一种通信协议，该协议使网络节点可以通过监听其他节点的通信来自我定位其在网络中的位置和姿态。这是通过将现代的声学USBL调制解调器和压力传感器与贝叶斯估计器相结合来实现的。无需主动传输声音信号，DNL可以节省能源并减少通信通道的总体工作量。此外，这将与其他声波传感器（如多波束声纳）的干扰效应降至最低，这对于传感器节点的测量目的至关重要。为了评估该方法，我们首先在蒙特卡罗模拟中测试了总体功能。随后，使用在大西洋中部海域试航期间收集的真实数据对方法进行了评估。

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