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Using spatial correlation of ocean current for velocity estimate of underwater drifting nodes

机译:利用洋流的空间相关性估计水下漂流节点的速度

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The ability to navigate underwater is a key requirement for most underwater applications. Mobile devices, drifters, and also human divers, all require precise navigation capability to perform long term missions while being submerged. In the absence of GPS reception, performing underwater navigation (UN) requires an accurate state-space-model (SSM) to allow the tracked node (TN) to self-estimate its location. However, irregularities in nodes motion (mostly due to unpredictable changes of ocean current) makes it hard to reliably determine the SSM. In this paper, we rely on spatial correlation of ocean current, and propose to estimate the drift velocity of the TN as a combination of the drift velocities of anchors, and to directly use it as part of the SSM. This not only augments the amount of information available for the TN to track its own position, but also offers an unbiased velocity estimate which increases the reliability of the SSM. Since ocean current may not be always correlated (for example, in the case of turbulence), we offer two unbiased confidence indexes: one which is based on the range to the anchor, and a second which is based on the homogeneity of the current velocity field. To evaluate the potential of utilizing node spatial dependencies for UN, we collect trajectories of drifting nodes from both model-based simulations and a sea trial performed in Israel. Our results suggest that nodes drifting motion shows strong spatial correlation which could greatly enhance the performance of tracking algorithms.
机译:水下航行的能力是大多数水下应用的关键要求。移动设备,漂流者以及人类潜水者都需要精确的导航功能,才能在被淹没时执行长期任务。在没有GPS接收的情况下,执行水下导航(UN)需要准确的状态空间模型(SSM),以允许被跟踪的节点(TN)自行估计其位置。但是,节点运动的不规则性(主要是由于洋流的不可预测的变化)使得难以可靠地确定SSM。在本文中,我们依靠洋流的空间相关性,并提出将TN的漂移速度作为锚的漂移速度的组合来估计,并将其直接用作SSM的一部分。这不仅增加了可用于TN跟踪其自身位置的信息量,而且还提供了无偏速度估计,从而提高了SSM的可靠性。由于洋流可能并不总是相关的(例如,在湍流的情况下),我们提供了两个无偏置信度指数:一个基于对锚的范围,而第二个基于当前速度的均匀性领域。为了评估将节点空间依赖性用于联合国的潜力,我们从基于模型的模拟和在以色列进行的海试中收集了漂移节点的轨迹。我们的结果表明,节点漂移运动显示出很强的空间相关性,可以大大提高跟踪算法的性能。

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