Abstract
In order to reduce random errors of the lidar signal inversion, a low-pass parabolic fast Fourier transform filter (PFFTF) was introduced for noise elimination. A compact airborne Raman lidar system was studied, which applied PFFTF to process lidar signals. Mathematics and simulations of PFFTF along with low pass filters, sliding mean filter (SMF), median filter (MF), empirical mode decomposition (EMD) and wavelet transform (WT) were studied, and the practical engineering value of PFFTF for lidar signal processing has been verified. The method has been tested on real lidar signal from Wyoming Cloud Lidar (WCL). Results show that PFFTF has advantages over the other methods. It keeps the high frequency components well and reduces much of the random noise simultaneously for lidar signal processing.
Highlights
Lidar is an active remote sensing instrument, which measures the backscattering signals by emitting laser pulses towards atmosphere or targets
A low-pass parabolic fast Fourier transform filter was reported for lidar signal processing
Following minimum mean square error (MSE) criterion, relations between pass frequency & stop frequency of parabolic fast Fourier transform filter (PFFTF) and sampling frequency were studied by simulative analysis
Summary
Lidar is an active remote sensing instrument, which measures the backscattering signals by emitting laser pulses towards atmosphere or targets It is widely used in atmospheric remote sensing, such as Sensors 2015, 15 detection of atmospheric aerosols, clouds, atmospheric boundary layer, temperature, visibility, and wind [1,2,3,4]. Long-time averaging is an effective method for ground-based lidar systems. This is not an option for airborne and space borne systems due to the requirement of higher horizontal resolution. A low-pass parabolic fast Fourier transform filter (PFFTF) was introduced, which can be operated well for lidar signal denoising in real-time monitoring.
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