Abstract An array of pressure sensors can be used to correct the drift in inertial navigation systems for underwater vehicles (UVs) in absence of other navigation support systems such as acoustic positioning, GPS and Doppler velocity measurements. To date, multiple pressure sensor arrays have been designed, proposed, and tested to prove the concept. However, it has not been researched the inclusion of non-linearities is required in the post-processing. This paper focuses on the use of machine learning as a novel approach to improve the post-processing accuracy, including non-linearities caused by the vehicle acceleration on the estimated speed compared to the linear parametric equation methodology. A series of towing tank experiments have been conducted over an array of pressure sensors located on an UV platform. The results show that pressure measurement array requires the use of non-linear post-processing methodologies as linear methodologies are not able to accurately account for vehicle acceleration effects.

中文翻译：

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用于速度测量的水下航行器压力传感器阵列的机器学习后处理

摘要 压力传感器阵列可用于在没有其他导航支持系统（如声学定位、GPS 和多普勒速度测量）的情况下校正水下航行器 (UV) 惯性导航系统的漂移。迄今为止，已经设计、提出并测试了多个压力传感器阵列以证明该概念。然而，尚未研究在后处理中需要包含非线性。本文侧重于使用机器学习作为一种新方法来提高后处理精度，包括与线性参数方程方法相比，车辆加速度对估计速度造成的非线性。已经在位于 UV 平台上的一系列压力传感器上进行了一系列拖曳水箱实验。