PCA和KLE在高采样率GPS定位中的应用

为抑制多路径误差和随机噪声以提高定位精度，引入主成分分析（PCA）和KLE变换方法对定位坐标序列随机噪声水平进行评价、提取和消除多天坐标序列中的多路径误差。对比分析结果表明，主成分系数比值能有效地反映出强随机噪声对某天定位坐标序列的影响，PCA方法能有效地提取和消除多天定位坐标序列中的多路径误差，显著提高定位精度。KLE变换对随机噪声污染不敏感，对多路径误差的滤波能力较PCA略弱，但其对随机噪声以及局部异常具有较好的抑制作用。

APPLICATION OF PCA AND KLE TO HIGH-RATE POSITIONING

Multipath error and random noise are two important error sources in high rate GPS positioning. In order to characterize and mitigate these errors, and improve the accuracy of high rate GPS positioning, the Principal Component Analysis (PCA) and Karhunen loeve Expansion (KLE) are introduced to evaluate the random noise level of the time series of coordinate on some days, extract and mitigate the multipath error from coordinates time series of multiple days. The data processing, comparison and analysis based on real data show that the principal component coefficients ratio of PCA can effectively reflect the random noise level of the time series of coordinates on certain day, meanwhile, the multipath error of the time series of coordinates on many days can be extracted and mitigated with introduction of PCA. Compared to PCA, the KLE approach performs slightly weaker on accurate improvement,but the random noise and local anomaly can be suppressed by KLE better since it is not sensitive to the random noise.