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针对靶场测量设备种类多,外测数据中存在各种不确定因素的误差,且难以用统一的误差模型来描述,提出了一种基于希尔伯特-黄变换的滤波去噪方法。通过经验模式分解将外测数据自适应地分解成一组内蕴模态函数(IMF),然后对内蕴模态函数进行希尔伯特频谱分析,采用基于自适应阈值的消噪方法对模态函数进行消噪,最后对消噪后的模态函数重构,得到去噪后的外测数据。数据结果分析证明,该方法最大限度地抑制了测量数据中的噪声,特别是对于外测数据中的瞬时强噪声干扰剔除效果非常有效,在高精度的外测数据处理中具有较好的实用性。
Aiming at the variety of measuring equipment and the errors of various uncertainties in the measured data, it is hard to describe with a uniform error model. A filter denoising method based on Hilbert-Huang transform is proposed. By experiential mode decomposition, the measured data is adaptively decomposed into a set of intrinsic mode functions (IMFs), and then the intrinsic mode function is analyzed by Hilbert spectrum. The adaptive threshold-based de-noising method is applied to modal Function to denoise, and finally reconstruct the modal function after denoising to get the denoised external measurement data. Data analysis shows that this method can restrain the noise in measurement data to a great extent, especially for the detection of instantaneous strong noise in the measured data. It has good practicability in high-precision external measurement data processing .