Experiment design-based regularization technique for radar imagery

One of the critical elements in the radar imagery in scattering natural media (turbulent atmosphere) is the problem of developing the system-oriented statistical signal processing techniques exploiting in some optimal way all the available "degrees of freedom" of the image formation method. Control of these degrees of freedom is the hope for solving many existing algorithm design and system-level problems that cause the ill-posed nature of the radar image formation and restoration tasks. We address a new approach to radar imaging (RI) problems stated and treated as ill-posed problems of restoring the extended object power scattering function by processing the data signals distorted in a stochastic measurement channel. We exploit the idea of combining/fusing the experiment design and descriptive regularization theory methods for inverse problems solution. The developed RI algorithms are illustrated with the simulation results.