面向陆表的星载GNSS-R DDM波形分类

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。

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

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.