Air Force Institute of Technology, Wright-Patterson AFB, OH 45433-7765, USA.
Military Institute of Armament Technology, 05-220 Zielonka, Poland.
Sensors (Basel). 2018 Jul 11;18(7):2232. doi: 10.3390/s18072232.
Pedestrian navigation in outdoor environments where global navigation satellite systems (GNSS) are unavailable is a challenging problem. Existing technologies that have attempted to address this problem often require external reference signals or specialized hardware, the extra size, weight, power, and cost of which are unsuitable for many applications. This article presents a real-time, self-contained outdoor navigation application that uses only the existing sensors on a smartphone in conjunction with a preloaded digital elevation map. The core algorithm implements a particle filter, which fuses sensor data with a stochastic pedestrian motion model to predict the user's position. The smartphone's barometric elevation is then compared with the elevation map to constrain the position estimate. The system developed for this research was deployed on Android smartphones and tested in several terrains using a variety of elevation data sources. The results from these experiments show the system achieves positioning accuracies in the tens of meters that do not grow as a function of time.
在无法使用全球导航卫星系统 (GNSS) 的户外环境中进行行人导航是一个具有挑战性的问题。现有的试图解决此问题的技术通常需要外部参考信号或专用硬件，而这些硬件的额外尺寸、重量、功率和成本不适合许多应用。本文介绍了一种实时、独立的户外导航应用程序，该应用程序仅使用智能手机上现有的传感器和预加载的数字高程图。核心算法实现了一个粒子滤波器，该滤波器融合了传感器数据和随机行人运动模型，以预测用户的位置。然后将智能手机的气压高程与高程图进行比较，以约束位置估计。为这项研究开发的系统部署在 Android 智能手机上，并使用各种高程数据源在几种地形上进行了测试。这些实验的结果表明，该系统能够实现几十米的定位精度，且不会随时间增长。
