| 摘要: |
| 为解决蕴含有复杂变形、船摇周期影响的测量船异常数据检测难题，提出了基于小波分析的异常数据的动态识别与修正方法。基于小波分析的多尺度、局部分析等特性，采用具有紧支撑特性的小波去噪方法，根据数据自适应选取阈值，对噪声进行抑制凸显异常，在实现噪声抑制的同时，较好地保留了局部异常特征，显著提高了异常数据的检出率。测试结果表明，该方法切实可行,效果良好。 |
| 关键词: 航天测量船 异常数据 小波分析 随机噪声 误差抑制 |
| DOI： |
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| 基金项目: |
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| Outlier data identification and error reduction for space tracking ships |
| LI Hui-fen,LIU Bing,CHEN Gui-ming,FENG Xiao-yong,DI Li-ping |
| () |
| Abstract: |
| It is difficult to detect the outlier data influenced by space tracking ship’s complicated movement such as deformation and swaying. To effectively solve the problem, dynamic outlier data identification and elimination method based on wavelet analysis is put forward. According to the features of wavelet analysis such as multi-scale representation and local analysis, compactly supported wavelet denoising method is applied, in which threshold value is selected based on data adaptability, by suppressing the noise anomaly can be easily checked out, at the same time local anomaly characteristics are better maintained, therefore anomaly detection rate is obviously improved. The test results show the proposed detection and elimination algorithm is both practical and effective. |
| Key words: space tracking ship outlier data wavelet analysis random noise error reduction |
《电讯技术》编辑部 