A Review of Sensor System Schemes for Integrated Navigation

U. Iqbal, A. Abosekeen, M. Elsheikh, A. Noureldin, M. Korenberg
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引用次数: 2

Abstract

GNSS navigation requires an unobstructed line-of-sight view of four or more satellites with suitable geometry to compute latitude, longitude, altitude, and time. GNSS signal weakens in degraded environments such as Urban Canyons, Tunnels, Under Passes, and Green Tunnels. Therefore, GNSS alone cannot provide reliable navigation support in challenging environments. To address this limitation, GNSS can be augmented with multiple other navigation sensors to provide an integrated solution, including inertial measurement units, magnetometers, and radars. Low-cost, small size and lightweight MEMS sensors are used for a wide range of navigation applications. However, adding each sensor increases the complexity of the systems as each sensor independently measures a particular parameter. Multi-sensor data fusion techniques, such as Kalman Filter (KF), play a vital role in improving the navigation accuracy of the system. This paper reviews multiple sensor schemes for integrating two accelerometers, a gyroscope, a magnetometer, and Adaptive Cruise Control Radar augmented with GNSS to provide an integrated multisensor navigation system. These multiple sensor schemes were tested in an actual road trajectory in Kingston. In addition, GNSS outages were intentionally introduced on this road trajectory to examine the performance of different Schemes for various motion dynamics.
综合导航传感器系统方案综述
GNSS导航需要四颗或更多卫星的无遮挡视线,并具有合适的几何形状来计算纬度、经度、高度和时间。在城市峡谷、隧道、地下通道、绿色隧道等退化环境中,GNSS信号减弱。因此,仅靠GNSS无法在具有挑战性的环境中提供可靠的导航支持。为了解决这一限制,GNSS可以与多个其他导航传感器一起增强,以提供一个集成的解决方案,包括惯性测量单元、磁力计和雷达。低成本、小尺寸和轻量化的MEMS传感器被广泛用于导航应用。然而,增加每个传感器增加了系统的复杂性,因为每个传感器独立测量一个特定的参数。卡尔曼滤波(KF)等多传感器数据融合技术对提高系统导航精度起着至关重要的作用。本文回顾了集成两个加速度计、陀螺仪、磁力计和增强GNSS的自适应巡航控制雷达的多种传感器方案,以提供集成的多传感器导航系统。这些多种传感器方案在金斯敦的实际道路轨迹中进行了测试。此外,有意在这条道路轨迹上引入GNSS中断,以检查不同方案在各种运动动力学下的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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