Improved phase gradient autofocus algorithm based on segments of variable lengths and minimum entropy phase correction

In this paper, an improved phase gradient autofocus (PGA) Algorithm motion compensation (MOCO) approaches is proposed for the unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) imagery. The approach is implemented in two-steps. The first step determines the length of segments depending on number of good quality scatterers and motion errors obtained from navigation data. In the second step, a novel minimum-entropy phase correction based on the Discrete Cosine Transform (DCT) coefficients is proposed. In this approach, transform phase error estimates by PGA to DCT-coefficient. The entropy of a focused image is utilized as the optimization function of the DCT coefficients to improve the final images quality. Finally, real-data experiments show that the proposed approach is appropriate for highly precise imaging of UAV SAR.