This paper introduces a novel ML based approach to channel identification for time variant SIMO (Single Input Multiple Output) systems fed by a stochastic process. We focus on the particular case where the unknowns are represented by the channels phases, that find applications in RADAR interferometry. Starting from the rigorous formulation of the ML estimator, we derive an approximation that makes use of mixers and FIR filters only. The computational efficiency and the robustness versus model errors of the resulting estimator make it suitable for its implementation is an adaptive framework. An application in topography reconstruction from real SAR (Synthetic Aperture Radar) data is presented.
['Andrea Monti Guarnieri', 'Stefano Tebaldini']
Channel Phase Estimate in Time Variant SIMO Systems