GNSS Shadow Matching: The Challenges Ahead
GNSS Shadow Matching: The Challenges Ahead
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发表时间：
2015-09
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通讯作者：
P. Groves;Lei Wang;M. Adjrad;C. Ellul
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作者：
P. Groves;Lei Wang;M. Adjrad;C. Ellul
GNSS阴影匹配是一种新技术，它使用3D映射，以提高定位精度在密集的城市地区从几十米到五米内，潜在less.This本文提出了第一次全面审查阴影匹配的误差来源，并提出了一个研究和开发计划，采取技术从概念验证到一个强大的，可靠的和准确的城市定位产品。还包括对最新技术水平的总结。阴影匹配中的误差源可以分为六类：初始化、建模、传播、环境复杂性、观测和算法近似。性能也受到环境几何形状的影响，有时需要处理解决方案的模糊性。对于每个误差源，解释了其原因及其如何影响位置解。例子，在可用的地方，并提出了改进的阴影匹配算法，以减轻每个错误。然后提出了在阴影匹配中进行质量控制的方法，包括不确定性确定、模糊性检测和离群点检测。随后讨论了阴影匹配如何与传统的基于测距的GNSS和其他导航和定位技术相结合。其中包括简要审查使用三维测绘增强测距型全球导航卫星系统的方法。最后，阴影匹配的实际工程挑战进行了评估，包括系统架构，有效的GNSS信号预测和三维测绘数据的采集。
GNSS shadow matching is a new technique that uses 3D mapping to improve positioning accuracy in dense urban areas from tens of meters to within five meters, potentially less. This paper presents the first comprehensive review of shadow matching’s error sources and proposes a program of research and development to take the technology from proof of concept to a robust, reliable and accurate urban positioning product. A summary of the state of the art is also included. Error sources in shadow matching may be divided into six categories: initialization, modelling, propagation, environmental complexity, observation, and algorithm approximations. Performance is also affected by the environmental geometry and it is sometimes necessary to handle solution ambiguity. For each error source, the cause and how it impacts the position solution is explained. Examples are presented, where available, and improvements to the shadow-matching algorithms to mitigate each error are proposed. Methods of accommodating quality control within shadow matching are then proposed, including uncertainty determination, ambiguity detection, and outlier detection. This is followed by a discussion of how shadow matching could be integrated with conventional ranging-based GNSS and other navigation and positioning technologies. This includes a brief review of methods to enhance ranging-based GNSS using 3D mapping. Finally, the practical engineering challenges of shadow matching are assessed, including the system architecture, efficient GNSS signal prediction and the acquisition of 3D mapping data.