Atmospheric radar signal processing using principal component analysis
Principal Component Analysis (PCA) is a simple non-parametric method for extracting relevant information from high-dimensional data sets. In this paper, we analyze the data collected from the Indian MST (Mesosphere, Stratosphere, Troposphere) radar at Gadanki (13.5N 13.5  N , 79.2E 79.2  E ) using PCA. We tested the PCA for various simulated signals like narrowband, wideband and exponential signals which may contain more than one frequency both in absence and presence of noise. For the simulated data, it is observed that PCA works for low SNR, i.e. it successfully detects the frequency in the highly noise-corrupted signal also. Finally, we applied PCA to the radar data for estimating the power spectrum and thus in turn estimating the Doppler frequency components. We estimate the zonal (U), meridional (V), wind speed (W) etc. from the Doppler frequencies. Compared with existing algorithms, PCA works well at higher altitudes and results have been validated using the GPS sonde data.
principal component analysis gps sonde mst radar wavelet-based denoising cepstral thresholding