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An EMD-based denoising method for lidar signal

机译:一种基于EMD的LIDAR信号的去噪方法

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Lidar echo signal is a typical non-steady-state, non-stationary signal, and difficult to be dealt with by the traditional filtering methods. As a new signal processing theory proposed in recent years, Empirical Mode Decomposition method can adaptively divide the lidar echo signal into different intrinsic mode function (IMF) components according to different time scale, and noise mainly concentrates in the high-frequency component. However, when filtered with simply removing high frequency component, the useful signal will be possible to be reduced. In this paper, a new method which combines Empirical Mode Decomposition (EMD) with Savitzky-Golay filter is proposed. With experiments, it is indicated that our approach not only removes the noise component effectively but also maintains the useful signal, then will improve the accuracy in the next phase of data processing.
机译:LIDAR回波信号是典型的非稳态,非稳定性信号,难以通过传统的过滤方法处理。作为近年来提出的新信号处理理论,经验模式分解方法可以根据不同的时间尺度将LIDAR回波信号自适应地将LIDAR回波信号分成不同的内在模式功能(IMF)组件,并且噪声主要集中在高频分量中。然而,当用简单地消除高频分量过滤时,将可以减少有用信号。本文提出了一种将经验模式分解(EMD)与Savitzky-Golay滤波器结合的新方法。通过实验,表示我们的方法不仅有效地消除噪声分量,而且还保持了有用的信号,然后将提高数据处理的下一阶段的准确性。

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