Enhancement of lidar backscatters signal-to-noise ratio using empirical mode decomposition method

Lidar is being widely used to monitor meteorological parameters and atmospheric constituents. Applications include meteorology, environmental pollution, atmospheric dynamics and global climate change. Signal processing for lidar applications involve highly nonlinear models and consequently nonlinear filtering. In this paper, we applied a new method, empirical mode decomposition to the lidar signal processing. The denoising approach is done by removal of the proper intrinsic mode functions. The data from the simulation and measurements are analyzed to evaluate this method comparing with the traditional low-pass filter and the multi-pulse averaging. Results show that it is effective and superior to the band-pass filter and the averaging method. The denoising method also allows less averaging laser shots which is important for the real-time monitoring and for the low cost laser transmitter.