Abstract
We proposed the Fast Fourier Transform (FFT) to represent the sparsity of weather radar signals based on Compressive Sampling (CS). Since the real weather radar signal that we used in this research was produced by Frequency Modulated Continuous Wave (FMCW) radar, the frequency beat signal is proportional to the delay of the targets. FFT then used to get the coefficient of frequency beat signal that could show the range information. Evidently, FFT produced the large number of sparse frequency beat signal, so we can perform CS to reduce the large volume of the weather radar data. We sampled the sparse of range information using random measurement and then reconstructed using Orthogonal Matching Pursuit (OMP) algorithm. We construct a Plan Position Indicator (PPI) from compressed beat signals and compare with the uncompressed ones. The reconstruction results at various compression rates show that the proposed method has works properly.
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