论文标题
数据驱动的符合导航：概念，模型和实验验证
Data-Driven Meets Navigation: Concepts, Models, and Experimental Validation
论文作者
论文摘要
导航的目的是确定载人和自主平台，人类和动物的位置，速度和方向。在基于模型的非线性估计框架中，通常需要在几个传感器（例如惯性传感器和全球导航卫星系统）之间进行融合。最近，与基于模型的方法相比，在各个领域采用的数据驱动方法显示出最先进的性能。在本文中，我们回顾了在自主导航和传感器融合实验室（ANSFL）中开发和实验证明的多学科，基于数据驱动的导航算法（ANSFL），包括适合人类和动物应用，多样化的自主平台以及多用途导航和融合方法
The purpose of navigation is to determine the position, velocity, and orientation of manned and autonomous platforms, humans, and animals. Obtaining accurate navigation commonly requires fusion between several sensors, such as inertial sensors and global navigation satellite systems, in a model-based, nonlinear estimation framework. Recently, data-driven approaches applied in various fields show state-of-the-art performance, compared to model-based methods. In this paper we review multidisciplinary, data-driven based navigation algorithms developed and experimentally proven at the Autonomous Navigation and Sensor Fusion Lab (ANSFL) including algorithms suitable for human and animal applications, varied autonomous platforms, and multi-purpose navigation and fusion approaches
