基于小波变换的飞行数据清洗

Flight Data Cleaning Based on Wavelet Transforms

飞行数据因为野点和噪声的存在给其进一步处理和利用造成了困难。提出了一种基于小波变换残差直方图分析的野点识别方法,能在时间域内精确定位野点,并具有识别少量成片野点的能力。根据飞行数据噪声的特点及去噪要求,在去噪的过程中引入边缘检测,提出了分二进小波尺度乘积和小波阈值收缩两个步骤进行去噪的方法,从而在去噪的同时很好地保留了序列极值点的特性。实验结果表明本文所提方法对飞行数据中存在的质量问题具有较好的清洗效果,野点识别准确,去噪效果良好,并且对类似其它数据的处理也有一定的应用参考价值。

Outliers and noise will cause difficulties during processing and using flight data. This paper proposes an outlier detection method based on histogram analysis of wavelet transform residuals, which can locate outliers in time- domain precisely, and can recognize little outliers in succession. Then according to the characteristics of flight data noise and its denoising demand, edge detection is introduced and a two - step denoising method including dyadic wavelet coefficients product and wavelet shrinkage is put forward, which can keep the characteristic of extremum points very well. Finally the experiment shows that the method presented in this paper is effective on flight data cleaning, with which outliers can be recognized exactly and denoising effect is good. The method can also be used for reference in processing other similar data.