Department of Geomatics Engineering, Schulich School of Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada.
Sensors (Basel). 2011;11(12):11390-414. doi: 10.3390/s111211390. Epub 2011 Nov 30.
Most portable systems like smart-phones are equipped with low cost consumer grade sensors, making them useful as Pedestrian Navigation Systems (PNS). Measurements of these sensors are severely contaminated by errors caused due to instrumentation and environmental issues rendering the unaided navigation solution with these sensors of limited use. The overall navigation error budget associated with pedestrian navigation can be categorized into position/displacement errors and attitude/orientation errors. Most of the research is conducted for tackling and reducing the displacement errors, which either utilize Pedestrian Dead Reckoning (PDR) or special constraints like Zero velocity UPdaTes (ZUPT) and Zero Angular Rate Updates (ZARU). This article targets the orientation/attitude errors encountered in pedestrian navigation and develops a novel sensor fusion technique to utilize the Earth's magnetic field, even perturbed, for attitude and rate gyroscope error estimation in pedestrian navigation environments where it is assumed that Global Navigation Satellite System (GNSS) navigation is denied. As the Earth's magnetic field undergoes severe degradations in pedestrian navigation environments, a novel Quasi-Static magnetic Field (QSF) based attitude and angular rate error estimation technique is developed to effectively use magnetic measurements in highly perturbed environments. The QSF scheme is then used for generating the desired measurements for the proposed Extended Kalman Filter (EKF) based attitude estimator. Results indicate that the QSF measurements are capable of effectively estimating attitude and gyroscope errors, reducing the overall navigation error budget by over 80% in urban canyon environment.
大多数便携式系统,如智能手机,都配备了低成本的消费级传感器,使其成为有用的行人导航系统(PNS)。这些传感器的测量结果受到仪器和环境问题引起的误差的严重干扰,使得这些传感器的无辅助导航解决方案的应用受到限制。与行人导航相关的总体导航误差预算可以分为位置/位移误差和姿态/方向误差。大多数研究都是针对解决和减少位移误差展开的,这些研究要么利用行人航位推算(PDR),要么利用特殊约束,如零速度更新(ZUPT)和零角速率更新(ZARU)。本文针对行人导航中遇到的姿态/方向误差,开发了一种新的传感器融合技术,即使在地球磁场受到干扰的情况下,也能利用地球磁场来估计姿态和速率陀螺仪误差,在行人导航环境中,假设全球导航卫星系统(GNSS)导航被拒绝。由于地球磁场在行人导航环境中会严重退化,因此开发了一种新的基于准静态磁场(QSF)的姿态和角速率误差估计技术,以便在高度受扰的环境中有效利用磁场测量值。然后,使用 QSF 方案为基于扩展卡尔曼滤波器(EKF)的姿态估计器生成所需的测量值。结果表明,QSF 测量值能够有效地估计姿态和陀螺仪误差,在城市峡谷环境中,将整体导航误差预算降低了 80%以上。