GNSS‐ISR data fusion: General framework with application to the high‐latitude ionosphere
GNSS‐ISR data fusion: General framework with application to the high‐latitude ionosphere
复制标题
GNSS-ISR 数据融合：应用于高纬度电离层的通用框架
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
V. Pankratius
中科院分区：
文献类型：
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作者：
J. Semeter;M. Hirsch;F. Lind;A. Coster;P. Erickson;V. Pankratius
提出了一种将非相干散射雷达（ISR）测量的电子密度与全球导航卫星系统（GNSS）测量的总电子含量（TEC）融合的数学框架。这两个测量值都被视为未知密度场的投影（对于GNSS-TEC，投影是断层扫描;对于ISR，投影是局部空间区域上的加权平均值），并且应用离散逆理论来获得比单独从任一模态获得的更高保真度的场表示。本文探索的具体实现使用内插ISR密度场作为组合逆问题的初始猜测，随后使用最大熵正则化来求解。模拟涉及一个密集的GNSS接收器网络附近的扑克平坦ISR证明了这种方法的潜力，以解决子束结构ISR测量。概述了几个未来的发展方向，包括（1）使用较低级别（滞后产品）ISR数据的数据融合，（2）考虑不同的时间采样率，（3）应用基于物理的正则化，（4）考虑非最佳观测几何，（5）使用ISR模拟框架进行最佳实验设计。
A mathematical framework is presented for the fusion of electron density measured by incoherent scatter radar (ISR) and total electron content (TEC) measured using global navigation satellite systems (GNSS). Both measurements are treated as projections of an unknown density field (for GNSS‐TEC the projection is tomographic; for ISR the projection is a weighted average over a local spatial region) and discrete inverse theory is applied to obtain a higher fidelity representation of the field than could be obtained from either modality individually. The specific implementation explored herein uses the interpolated ISR density field as initial guess to the combined inverse problem, which is subsequently solved using maximum entropy regularization. Simulations involving a dense meridional network of GNSS receivers near the Poker Flat ISR demonstrate the potential of this approach to resolve sub‐beam structure in ISR measurements. Several future directions are outlined, including (1) data fusion using lower level (lag product) ISR data, (2) consideration of the different temporal sampling rates, (3) application of physics‐based regularization, (4) consideration of nonoptimal observing geometries, and (5) use of an ISR simulation framework for optimal experiment design.
影响因子：
2.2
作者：
Chiang, YW;Borbat, PP;Freed, JH
通讯作者：
Freed, JH