面向陆表的星载GNSS-R DDM波形分类

doi:10.13474/j.cnki.11-2246.2019.0249

测绘通报 ›› 2019, Vol. 0 ›› Issue (8): 44-47.doi: 10.13474/j.cnki.11-2246.2019.0249

面向陆表的星载GNSS-R DDM波形分类

涂晋升¹, 张瑞^1,2, 洪学宝³, 汉牟田³

1. 西南交通大学地球科学与环境工程学院, 四川成都 611756;
2. 西南交通大学高速铁路运营安全空间信息技术国家地方联合工程实验室, 四川成都 611756;
3. 北京航空航天大学电子信息工程学院, 北京 100191

收稿日期:2018-11-29 修回日期:2019-02-11 出版日期:2019-08-25 发布日期:2019-09-06
通讯作者: 张瑞。E-mail:zhangrui@swjtu.edu.cn E-mail:zhangrui@swjtu.edu.cn
作者简介:涂晋升(1994-),男,硕士生,研究方向为GNSS反射信号遥感。E-mail:tjsheng94@163.com
基金资助:
国家重点研发计划（2017YFB0502700）；国家自然科学基金面上项目（41474003）；国家自然科学基金青年基金（41601503）；中国铁路总公司科技研究开发计划重点课题（2016T002-E）；四川省科技计划（2018JY0564）

A Space-borne GNSS-R DDM waveform classification method for land surface

TU Jinsheng¹, ZHANG Rui^1,2, HONG Xuebao³, HAN Mutian³

1. Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China;
2. State-Province Joint Engineering Laboratory of Spatial Information Technology of High-Speed Rail Safety, Southwest Jiaotong University, Chengdu 611756, China;
3. School of Electronic and Information Engineering, Beihang University, Beijing 100191, China

Received:2018-11-29 Revised:2019-02-11 Online:2019-08-25 Published:2019-09-06

摘要/Abstract

摘要： GNSS-R信号在面向复杂场景的陆表遥感应用中存在信噪比（SNR）低和有效信息难以辨别提取的问题，严重制约了其在陆表遥感领域的应用拓展。为从海量低信噪比的星载GNSS-R陆表数据中快速区分杂波信号和有效信息，本文通过统计归纳分析，基于星载时延多普勒图（DDM）相关峰的显著程度评定，提出了一种DDM波形分类方法。随后，利用该方法对UK TechDemoSat-1（TDS-1）星载陆表观测数据进行了波形分类。最后，比较了分类后波形对应的SNR情况，同时结合典型地物类型对分类结果进行了相关性分析，证实了波形分类方法的可行性与有效性。

关键词: 星载GNSS-R, 时延多普勒图(DDM), DDM波形分类, UK TechDemoSat-1(TDS-1), 典型地物类型

Abstract: In the GNSS-R based land surface remote sensing application for the complex scenarios, there are limitations, such as the low signal-to-noise ratio (SNR) and the difficulty for effective information identification. This problem seriously restricts the GNSS-R application in land surface remote sensing. In order to quickly distinguish clutter signals and effective information from massive space-borne GNSS-R land surface data of low SNR, a new method is proposed for the DDM waveform classification based on statistical inductive analysis and the significant level of the space-borne Delay Doppler Map (DDM) correlation peak. Subsequently, this method is utilized to waveform classification the land surface observation data of UK TechDemoSat-1 (TDS-1) satellite. Finally, related comparative analysis for the SNR of the waveform after classification, and the correlation analysis between the classification results and various typical land surface types was accomplished, which demonstrated the feasibility and effectiveness of the proposed waveform classification method.

Key words: space-borne GNSS-R, delay Doppler map, DDM waveform classification, UK TechDemoSat-1 (TDS-1), typical land surface types

中图分类号:

P237

涂晋升, 张瑞, 洪学宝, 汉牟田. 面向陆表的星载GNSS-R DDM波形分类[J]. 测绘通报, 2019, 0(8): 44-47.

TU Jinsheng, ZHANG Rui, HONG Xuebao, HAN Mutian. A Space-borne GNSS-R DDM waveform classification method for land surface[J]. 测绘通报, 2019, 0(8): 44-47.

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面向陆表的星载GNSS-R DDM波形分类

A Space-borne GNSS-R DDM waveform classification method for land surface

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