引用本文: 王坚, 高井祥, 苗李莉. 强污染单历元GPS形变信号的提取和粗差识别[J]. 武汉大学学报 ( 信息科学版), 2004, 29(5): 416-419.
WANG Jian, GAO Jingxiang, MIAO Lili. Strong Contaminated Single Epoch GPS Deformation Signals Extracting and Gross Error Detection[J]. Geomatics and Information Science of Wuhan University, 2004, 29(5): 416-419.
 Citation: WANG Jian, GAO Jingxiang, MIAO Lili. Strong Contaminated Single Epoch GPS Deformation Signals Extracting and Gross Error Detection[J]. Geomatics and Information Science of Wuhan University, 2004, 29(5): 416-419.

## Strong Contaminated Single Epoch GPS Deformation Signals Extracting and Gross Error Detection

• 摘要: 阐述了小波分析技术用于强污染GPS单历元形变信号处理的基本原理及其实现方法。以含少量粗差的低信噪比形变信号为例,研究了基于Mallat分解和合成算法,分离信号趋势项并进行粗差识别的技术。采用小波软阈值降噪的方法去除强污染数据中的随机噪声,降噪效果远好于中值滤波。最后对识别的粗差信息进行线性修复,获得了令人满意的形变信号。

Abstract: Wavelet theory is efficient as an adequate tool for analyzing single epoch GPS deformation signal. In this paper, wavelet analysis technique on gross error detection and recovery is advanced. Criteria of wavelet function choosing and MALLAT decomposition levels decision are discussed. An effective deformation signal extracting method is proposed, that is wavelet noise reduction technique considering gross error recovery, which combines wavelet multi-resolution gross error detection results. Time position recognizing of gross errors and their repairing performance are realized. In the experiment, compactly supported orthogonal wavelet with short support block is more efficient than the longer one when discerning gross errors, which can obtain more finely analyses. And the shape of discerned gross error of short support wavelet is simpler than that of the longer one. Meanwhile, the time scale is easier to identify.

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