EMD去噪法在FMCW雷达信号处理中的应用

发布时间：2018-05-10 08:59

本文选题：经验模态分解 + 自相关　；参考：《科学技术与工程》2014年27期

【摘要】：针对调频连续波(frequency modulated continuous wave,FMCW)雷达信号在传播过程中会受到各种噪声影响的情况,提出了一种新型的基于自相关函数能量特性的经验模态分解(empirical mode decomposition,EMD)降噪算法。该算法首先对差频信号进行EMD分解,得到包含原信号不同尺度信息的本征模态函数(intrinsic mode function,IMF)分量;其次对每一IMF分量进行无偏自相关运算,并计算其能量;第三,根据IMF分量的自相关函数能量变化曲线,确定出有用信号贡献率最大的IMF分量;最后进行EMD重构。实验结果表明:该方法在各种信噪比条件下,能够准确判断出有用信号主导的IMF分量,对差频信号具有良好的去噪效果,同时该方法具有自适应性,不受主观因素的影响,适合应用在FMCW雷达系统中。
[Abstract]:A novel empirical mode decomposition (EMD) denoising algorithm based on the energy characteristics of autocorrelation function is proposed for frequency-modulated continuous wave FMCW radar signal which is affected by various noises during its propagation. The algorithm firstly decomposes the difference frequency signal by EMD, and obtains intrinsic mode function intrinsics mode function IMF component which contains different scale information of the original signal. Secondly, unbiased autocorrelation operation is performed for each IMF component and its energy is calculated. According to the energy change curve of autocorrelation function of IMF component, the IMF component with the largest contribution rate of useful signal is determined. Finally, the EMD reconstruction is carried out. The experimental results show that the method can accurately judge the IMF component dominated by the useful signal under various SNR conditions, and has a good denoising effect on the differential frequency signal. At the same time, the method is self-adaptive and independent of subjective factors. It is suitable for FMCW radar system.
【作者单位】：中北大学电子测试技术国家重点实验室;
【基金】：国家自然科学基金科学仪器基础专项(61227003);国家自然科学基金面上项目(61171179) 山西省经济和信息化委员会节能减排专项资助
【分类号】：TN957.51

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