Sparse iterative covariance-based estimation approach for processing atmospheric radar data - C, Raju; T, Sreenivasulu Reddy (International Journal of Engineering and Technology, 2018)
The Doppler estimation is an important problem for Mesosphere–Stratosphere–Troposphere (MST) Radar data for detection and estimation of the weather parameters like turbulence intensity, mean radial velocity, humidity, temperature, wind speed. For Doppler estimation, one has to compute the Power Spectral Density (PSD). Various parametric and nonparametric methods have been developed. Recently, a new category of spectrum estimation method called Sparse Iterative Covariance Based Estimation (SPICE) is also developed. SPICE is a robust, user parameter-free, high resolution, iterative and globally convergent estimation algorithm. In this paper, the simple gradient approach is used for minimization of the weighted covariance estimation analyzing the data collected from the Indian MST radar at Gadanki (13.5°N, 79.2°E). The same method is applied for radar data to estimate the power spectrum and Doppler frequency. The zonal (U), meridional (V), wind speed (W) are calculated and the results have been validated using simultaneous Global Positioning System (GPS) Sonde data.