Bayesian multisensor navigation incorporating pseudoranges and multipath model

M. Khider, T. Jost, Elena Abdo Sanchez, P. Robertson, M. Angermann
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引用次数: 11

Abstract

Indoor and urban canyons are application areas that are becoming increasingly important for navigation application. However, achieving the required accuracy and availability is still a challenge. Multisensor navigation is one of the techniques that has shown promising results in addressing the challenges of such areas. Being able to incorporate raw low-level sensor data is advantageous as it allows to incorporate all available information and more accurate estimation models. In this paper a Particle Filter based multisensor positioning system is extended to use pseudorange measurements of a GPS sensor instead of a calculated position solution. Using pseudoranges, any number of visible satellites can improve positioning accuracy, when combined with measurements from other sensors like an electronic compass, a barometric altimeter or a foot mounted inertial measurement unit (IMU). Additionally, statistical error models for pseudoranges are integrated and tested. Our results show that by using the models within the Bayesian framework yields promising results in terms of error mitigation.
基于伪距离和多路径模型的贝叶斯多传感器导航
室内和城市峡谷是导航应用中越来越重要的应用领域。然而,实现所需的准确性和可用性仍然是一个挑战。多传感器导航是在解决这些领域的挑战方面显示出有希望的结果的技术之一。能够合并原始的低级传感器数据是有利的,因为它允许合并所有可用的信息和更准确的估计模型。本文将基于粒子滤波的多传感器定位系统扩展为使用GPS传感器的伪距测量值来代替计算的位置解。使用伪卫星,任何数量的可见卫星都可以提高定位精度,当与电子罗盘、气压高度计或脚装惯性测量单元(IMU)等其他传感器的测量相结合时。此外,还集成和测试了伪橙子的统计误差模型。我们的结果表明,通过在贝叶斯框架内使用模型,在减少错误方面产生了有希望的结果。
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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