Segment empirical mode decomposition filtering method on distance correction signal for aerosol lidar

Abstract

Aerosol lidar has been widely used in environmental monitoring. It is necessary to correct the distance before calculating the backscatter coefficient. However, the problem in this process is that the noise is greatly amplified, especially for long-distance signals. Based on previous theoretical research and signal characteristics, a segment empirical mode decomposition (EMD) filtering method that can denoise the distance correction signal is developed. Taking measured data of aerosol lidar as an example, the distance correction signal is divided into four segments, and each segment is decomposed by EMD. For low-height signals with low noise, the signal is reconstructed as comprehensively as possible. For high-height signals with high noise, most high-frequency signals are removed. This method is feasible because the aerosol concentration decreases with the increase in height. After segment EMD filtering, four segments of signals are recombined. Compared with the classical EMD, the segmented EMD filtering method not only retains the details of the low-height signal but also removes the high-height noise as much as possible. The segmented EMD filtering method is proven to be effective for aerosol lidar that conducts profile monitoring on the ground.