Weighted Minimum Entropy Autofocus Algorithm for ISAR Imaging

A novel method of Weighted Minimum Entropy Autofocus (WMEA) algorithm for ISAR imaging is proposed where the objective function of weighed entropy is constructed and then solved with iterative technique to seek for phase errors. Based on Weighted Least-Squares (WLS) principle, the algorithm proposed is very robust by exploting the difference of phase error variations in range cells. Contrary to traditional Minimum Entropy Autofocus (MEA) algorithm, the convergence rate could be greatly promoted by weighted minimum entropy autofocus algorithm. Besides, with this method clutter and noise could be suppressed efficiently to achieve better performance. The experimental results using both simulated data and measured data confirm the validation of the proposed algorithm.