Improved specular point prediction precision using gradient descent algorithm

doi:10.1016/j.asr.2019.12.016

NSSC OpenIR

	Improved specular point prediction precision using gradient descent algorithm
Alternative Title	WOS:000517656200004;20200608133834
	Tian, Yusen 1,2,3; Xia, Junming 2,3; Sun, Yueqiang 1,2,3; Wang, Xianyi 2,3; Du, Qifei 2,3; Bai, Weihua 2,3; Wang, Dongwei 1,2,3; Cai, Yuerong 2,3; Wu, Chunjun 1,2,3; Li, Fu 2,3; Qiao, Hao 2,3
Source Publication	ADVANCES IN SPACE RESEARCH
	2020
Volume	65 Issue:6 Pages:1568-1579
DOI	10.1016/j.asr.2019.12.016
ISSN	0273-1177
Language	英语
Keyword	GNSS-R Specular point Gradient descent algorithm Self-adaptive learning rate REFLECTED GPS SIGNALS WIND-SPEED REFLECTOMETRY PARIS
Abstract	Global Navigation Satellite Systems Reflectometry (GNSS-R) utilizes GNSS signals reflected off the Earth surface for remote sensing applications. Due to weak power of reflected signals, GNSS-R receiver needs to track reflected signals by open loop. The first step is to calculate the position of specular point. The specular point position error of the existing algorithm-Quasi-Spherical Earth (QSE) Approach-is about 3 km which may cause troubles in data post-processing. In this paper, gradient descent algorithm is applied to calculate position of specular point and the calculation is based on World Geodetic System 1984 (WGS 84) ellipsoid in geodetic coordinate. The benefit of this coordinate is that it is easy to investigate the effect of real surface's altitude. Learning rate-the key parameter of the algorithm-is adaptively adjusted according to initial error, latitude and gradient descent rate. With self-adaptive learning rate strategy, the algorithm converges fast. Through simulation and test on Global Navigation Satellite System Occultation Sounder II (GNOS II), the performances of the algorithm are validated. The specular point position error of the proposed algorithm is about 10 m. The speed of the proposed algorithm is competitive compared with the existing algorithm. The test on GNOS II shows that the proposed algorithm has good real-time performance. (C) 2019 Published by Elsevier Ltd on behalf of COSPAR.
Indexed By	SCI ; EI
Citation statistics
Document Type	期刊论文
Identifier	http://ir.nssc.ac.cn/handle/122/7679
Collection	中国科学院国家空间科学中心
Affiliation	1.Chinese Acad Sci, Natl Space Sci Ctr, 1 Nanertiao, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Beijing Key Lab Space Environm Explorat, Beijing 100190, Peoples R China 4.Chinese Acad Sci, Key Lab Sci & Technol Space Environm Situat Aware, Beijing 100190, Peoples R China
Recommended Citation GB/T 7714	Tian, Yusen,Xia, Junming,Sun, Yueqiang,et al. Improved specular point prediction precision using gradient descent algorithm[J]. ADVANCES IN SPACE RESEARCH,2020,65(6):1568-1579.
APA	Tian, Yusen.,Xia, Junming.,Sun, Yueqiang.,Wang, Xianyi.,Du, Qifei.,...&Qiao, Hao.(2020).Improved specular point prediction precision using gradient descent algorithm.ADVANCES IN SPACE RESEARCH,65(6),1568-1579.
MLA	Tian, Yusen,et al."Improved specular point prediction precision using gradient descent algorithm".ADVANCES IN SPACE RESEARCH 65.6(2020):1568-1579.

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