基于PCA算法的星载GNSS-R海面目标反演

提出使用主成分分析(principal components analysis,PCA)抑制时延多普勒图(delay Doppler map, DDM)中的海杂波，提高海面目标反演精度。以挪威Snøhvit采气平台作为海面目标，采用2016-11-13的DDM数据进行目标反演。结果显示，未使用PCA抑制海杂波前，反演位置平均误差为17.65 km；抑制海杂波后，反演位置平均误差为11.42 km，位置精度提升35.30%。

Inversion of Sea Surface Target of Spaceborne GNSS-RBased on PCA Algorithm

To suppress the sea clutter in delay Doppler map (DDM) and improve the accuracy of sea surface target inversion, we propose principal components analysis (PCA). In this paper, the NorwegianSnøhvit gas platform is used as the sea surface target, and the DDM data on November 13, 2016, are used for target inversion. Before using PCA to suppress sea clutter, the average error of inversion position is 17.65 km, the average error of inversion position after suppressing sea clutter is 11.42 km, and the position accuracy is improved by 35.30%.