论文标题
自适应步长学习，并应用于速度的应用程序辅助惯性导航系统
Adaptive Step Size Learning with Applications to Velocity Aided Inertial Navigation System
论文作者
论文摘要
自动水下车辆（AUV）通常在许多水下应用中使用。最近，在文献中，多旋转无人自动驾驶汽车（UAV）的使用引起了更多关注。通常，这两个平台都采用惯性导航系统（INS），并为准确的导航解决方案提供帮助。在AUV导航中，多普勒速度日志（DVL）主要用于帮助INS，而对于无人机，通常使用全球导航卫星系统（GNSS）接收器。辅助传感器和INS之间的融合需要在估计过程中定义步长参数。它负责解决方案频率更新，并最终导致其准确性。步长的选择在计算负载和导航性能之间构成了权衡。通常，与INS操作频率（数百个赫兹）相比，辅助传感器更新频率被认为要慢得多。对于大多数平台来说，这种高率是不必要的，特别是对于低动力学AUV。在这项工作中，提出了一种基于监督机器学习的自适应调整方案，以选择适当的INS步骤尺寸。为此，定义了一个速度误差，允许INS/DVL或INS/GNSS在亚最佳工作条件下起作用，但可以最大程度地减少计算负载。模拟和现场实验的结果显示了使用所提出的方法的好处。此外，建议的框架可以应用于任何类型的传感器或平台之间的任何其他融合场景。
Autonomous underwater vehicles (AUV) are commonly used in many underwater applications. Recently, the usage of multi-rotor unmanned autonomous vehicles (UAV) for marine applications is receiving more attention in the literature. Usually, both platforms employ an inertial navigation system (INS), and aiding sensors for an accurate navigation solution. In AUV navigation, Doppler velocity log (DVL) is mainly used to aid the INS, while for UAVs, it is common to use global navigation satellite systems (GNSS) receivers. The fusion between the aiding sensor and the INS requires a definition of step size parameter in the estimation process. It is responsible for the solution frequency update and, eventually, its accuracy. The choice of the step size poses a tradeoff between computational load and navigation performance. Generally, the aiding sensors update frequency is considered much slower compared to the INS operating frequency (hundreds Hertz). Such high rate is unnecessary for most platforms, specifically for low dynamics AUVs. In this work, a supervised machine learning based adaptive tuning scheme to select the proper INS step size is proposed. To that end, a velocity error bound is defined, allowing the INS/DVL or the INS/GNSS to act in a sub-optimal working conditions, and yet minimize the computational load. Results from simulations and field experiment show the benefits of using the proposed approach. In addition, the proposed framework can be applied to any other fusion scenarios between any type of sensors or platforms.
