Compressive Sampling of Polarimetric Doppler Weather Radar Processing via Inverse Fast Fourier Transform

Polarimetric Doppler frequency-modulated continuous-wave weather radar produces a tremendous amount of data during the observation of atmospheric conditions. Using conventional signal processing method, the beat signals are sampled at Nyquist rate, so that the range signal can be reconstructed perfectly. We propose compressive sampling (CS) technique to sample and reduce the data simultaneously by exploring the sparsity of the beat signal in transform domain. Reducing the number of the beat signal and the transform coefficients are natural choices, because sparse signals will be obtained by inverse Fourier transforming the beat signals. The proposed techniques are evaluated by constructing the plan position indicator of reflectivity and mean Doppler velocity measurements from real weather polarimetric data. Compared to the conventional method, the CS polarimetric Doppler processing significantly reduce the number of the data while pertaining important weather information. We show that the proposed method works properly to quantify and classify the precipitation, mainly when the number of the samples is above 25% of its original.