Improved specular point prediction precision using gradient descent algorithm

Tian, Yusen; Xia, Junming; Sun, Yueqiang; Wang, Xianyi; Du, Qingyun; Bai, Weihua; Wang, Dongwei; Cai, Yuerong; Wu, Cindy; Li, Fu; Qiao, Hao

doi:10.1016/j.asr.2019.12.016

Cited by 4 publications

(1 citation statement)

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“…In order to improve specular point prediction precision and reduce the calculation time, an improved algorithm named the self-adaptive learning rate gradient descent (SAL-RGD) [25] has been especially proposed for the GNSS-R module in the SIGRS. On the one hand, the SALRGD calculates the specular point on the ellipsoidal surface, which makes SALRGD fit the ellipsoid better than the QSE.…”

Section: Gnss Ro Modulementioning

confidence: 99%

An Innovative Signal Processing Scheme for Spaceborne Integrated GNSS Remote Sensors

et al. 2023

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The vigorous development of the global navigation satellite system (GNSS) has led to a boom in GNSS radio occultation (GNSS RO) and GNSS reflectometry (GNSS-R) techniques. Consequently, we have proposed an innovative signal processing scheme for spaceborne integrated GNSS remote sensors (SIGRS), combining a GNSS RO and a GNSS-R module. In the SIGRS, the GNSS-R module shares one precise orbit determination (POD) module with the GNSS RO module, and the GNSS-R module first achieves compatibility with GPS, BDS, and Galileo. Moreover, the programmable non-uniform delay resolution was introduced and first used by the SIGRS to generate the output DDM, which achieves a high delay resolution in the DDM central region around the specular point to improve the accuracy of basic observables but requires fewer delay bins than the conventional DDM with uniform delay resolution. The SIGRS has been successfully used to design the GNOS II onboard the Chinese FY-3E satellite, and the results of in-orbit operation validate the performance of the SIGRS, which means the SIGRS is an economically and technically efficient design and has become the first successful signal processing scheme for spaceborne integrated GNSS remote sensors around the world.

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Section: Gnss Ro Modulementioning

confidence: 99%