基于自适应多尺度形态滤波与EMD的激光雷达回波信号去噪方法

在使用经验模式分解(Empirical Mode Decomposition,EMD)对激光雷达回波信号进行去噪处理时,由于信号含有脉冲及间歇等间断事件而产生模态混叠,导致不能很好地分解出有用信号成分,影响去噪效果。针对这一问题,提出了一种形态滤波与EMD相结合的组合算法。首先,使用自适应多尺度形态滤波器作为前置单元,对信号进行初步处理,剔除信号中的间断事件干扰。之后,应用EMD对处理过的信号去噪。采用仿真数据及真实激光雷达回波数据进行了去噪实验。实验结果表明,文中算法相比于直接EMD去噪,在仿真试验中信噪比提高了8.89 dB,均方根误差降低了0.0514;在真实回波数据去噪实验中,6 km以后平均信噪比提高了3.356 4 dB。该组合算法有效地抑制了模态混叠现象,具有良好的去噪效果及应用前景。

Lidar backscattering signal denoising method based on adaptive multi-scale morphological filtering and EMD

Becauce of interruption contained in a Lidar echo signal, mode mixing is often generated when using EMD (Empirical Mode Decomposition) to denoise such a signal. It lead to that it can't remove the noise from useful signal easily, and make the denoising effect so worse. In order to solve this problem, a combinational algorithm was presented which combine the morphological filtering and EMD together. Firstly, an adaptive multi-scale morphological filter was used to dispose the signal and remove the interruption, as a preliminary treatment, then used EMD for denoising. At last, a simulated signal denoising experiment and a real Lidar echo signal denoising experiment were done, the results showed that SNR increased by 8.89 dB and RMSE reduced by 0.051 4 compared with using EMD to denoise directly in the former experiment, the mean-SNR after 6 km increased by 3.356 4 dB in the later. This combinational algorithm can restrain mode mixing effectively, and has a better denoising effect and application prospects.