基于观测量校正的星载GNSS-R海面风速快速反演方法

谭承旦; 罗瑞丹; 李亚峰; 袁超; 杨光; 田向伟

doi:10.12265/j.gnss.2021071402

基于观测量校正的星载GNSS-R海面风速快速反演方法

doi: 10.12265/j.gnss.2021071402

谭承旦^{1, 2,},
罗瑞丹¹,
李亚峰³,
袁超^1, ,,
杨光¹,
田向伟¹

1.
中国科学院空天信息创新研究院, 北京100094
2.
中国科学院大学电子电气与通信工程学院, 北京100049
3.
北京信息科技大学自动化学院, 北京100192

基金项目: 国家自然科学基金 (41904033)；中国科学院青年创新促进会(E03314020D)

详细信息

作者简介:
谭承旦：(1996—)，男，硕士研究生，主要研究方向：星载GNSS-R理论与应用

罗瑞丹：(1987—)，女，博士，副研究员，主要研究方向：导航信号体制及同步算法研究等

李亚峰：(1985—)，男，博士，副教授，主要研究方向：惯性导航系统(INS)及其组合导航技术、GNSS-R理论与应用等

袁超：(1986—)，男，硕士，工程师，主要研究方向：卫星导航接收机及应用技术、GNSS-R应用技术与设备、GNSS电离层探测技术与设备等

通信作者:
袁超 E-mail: yuanchao100918@aircas.ac.cn

中图分类号: P228.4；TN961
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出版历程
- 收稿日期: 2021-07-14
- 录用日期: 2021-07-14
- 网络出版日期: 2021-12-29

A fast spaceborne GNSS-R sea surface wind speed retrieval method based on observation correction

1.
Aerospace Information Research Institute, University of Chinese Academy of Sciences, Beijing 100094, China
2.
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
3.
School of Automation, Beijing Information Science and Technology University, Beijing 100192, China

摘要

摘要: 快速反演(FDI)算法是一种典型的星载全球卫星导航反射技术(GNSS-R)海面风速反演方法，具有计算复杂度低、快速实时反演的特点，但是FDI算法中的反演观测量提取精度不高，导致风速反演精度低. 针对于此，提出基于观测量校正的改进FDI算法，用于实现海面风速的快速高精度反演. 该方法首先利用辅助测量信息对观测量进行校正以降低干扰因素的影响，然后基于统计分析方法对ASCAT卫星风速数据进行海面风速值提取，最后建立了海面风速与校正观测量的地球物理模型函数(GMF)关系式，实现对海面风速的反演. 与传统FDI算法相比，该方法的反演偏差值更小，均方根误差(RMSE)降低了29%.
- 全球卫星导航系统(GNSS) /
- 反射信号 /
- 海面风速 /
- 反演模型 /
- 观测量校正
Abstract: The fast-delivery inversion (FDI) algorithm is a typical spaceborne Global Navigation Satellite system Reflectometry (GNSS-R) sea surface wind speed inversion method, which has the characteristics of low computational complexity and fast processing. However, the retrieval observation extraction accuracy in FDI algorithm is low, which leads to low wind speed retrieval accuracy. In view of this, an improved FDI algorithm based on observation correction is proposed to realize the fast and high-precision retrieval of sea surface wind speed. In this method, firstly, the auxiliary measurement information is used to correct the observation to reduce the influence of interference factors, then the sea surface wind speed value is extracted from the ASCAT satellite wind speed data based on the statistical analysis method, and finally the geophysical model function (GMF) relationship between the sea surface wind speed and the corrected observation is established to realize the retrieval of sea surface wind speed. Compared with the traditional FDI algorithm, the wind speed retrieval bias of this method is smaller, and the root-mean-square error (RMSE) is reduced by 29%.
- Global Navigation Satellite System (GNSS) /
- reflected signal /
- sea surface wind speed /
- retrieval model /
- observation correction.

HTML全文

图 1 DDM与$R_{{\rm{SN}}}^0$观测量提取示意图

注：数据来源于TDS-1L1b星载数据。

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图 2 GNSS-R几何示意图

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图 3 $R_{{\rm{SN}}}^0$在不同$G_{\rm{r}}^{sp}$值下与ASCAT风速的变化关系

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图 4 $R_{{\rm{SN}}}^0$平均值与$G_{\rm{r}}^{sp}$的变化关系

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图 5 $R_{{\rm{SN}}}^0$与$R_{{\rm{SN}}}^1$的均值与标准差（误差条表示）随ASCAT风速的变化关系

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图 6 $R_{{\rm{SN}}}^\prime$与$R_{{\rm{SN}}}^1$的标准差

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图 7 风速统计值$ W\left( n \right) $与正态分布模型的概率密度函数

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图 8 不同$R_{{\rm{SN}}}^1$下正态分布模型对统计值的拟合优度

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图 9 风速真值估计值$ {\hat W_\mu } $与$R_{{\rm{SN}}}^1$的关系以及GMF关系式

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图 10 传统FDI算法与本文方法风速反演结果比对

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