改进小波空域相关去噪算法的飞行数据滤波

Flight data de-noising using an improved adaptive wavelet spatial correlation algorithm

飞行数据具有非平稳的特点,传统的降噪算法难以在抑制噪声的同时保留飞行数据中的尖峰等细节信息,而空域相关滤波算法由于要设定处理相关系数的阀值,具体实现较困难。本文提出了一种改进的自适应小波系数空域相关滤波算法,依据在不同预选阀值情况下提取的信号占优小波系数个数的变动情况来停止相关性计算,并且对噪声能量占优的小波系数进行萎缩处理。采用仿真数据和实测飞行数据进行试验,结果表明,改进方法保留了信号更多的细节信息,提高了信噪比,也增强了小波空域相关滤波的实用性。

Because of the non-stationary specialties of flight data, it is difficult for the traditional de-noise algorithms to suppress noise while preserving details such as micro-pinnacles. Considering the hard realization by traditional spatial correlation coefficients algorithms, this paper proposes an improved adaptive wavelet de-noising method by spatial correlation coefficients. Quit the calculation according the variation of the numbers of the signal-dominant coefficients, and shrink the noise-dominant coefficients. Simulated data and flight data were used for numerical experiments, which show that the proposed algorithm can better retain the original features and improve the SNR, enhancing the practical applicability of the spatial correlation coefficients de-noise algorithms.