Regularized doppler radar imaging for target identification in atmospheric clutter

We develop a method for the formation of Doppler radar images with enhanced features. This problem, when studied as an adaptive spectral estimation problem, is particularly ill-posed because of the small number of data. Our approach is based on a regularized estimation of depth-frequency images which combines a high-resolution Fourier model of the observations with prior information about the nature of the features of interest. We first derive an appropriate model and a regularized criterion for meteorological clutter restoration before addressing the extension to the identification of spot-like targets superimposed to this clutter. We also adapt quasi-Newton algorithms based on Half-Quadratic regularization [1, 2] for the computation of the solution. The practical interest of our approach is validated on simulated and real data.