Minimum Entropy via Subspace for ISAR Autofocus

• Published in: IEEE geoscience and remote sensing letters Vol. 7; no. 1; pp. 205 - 209
• Main Authors: Cao, Pan, Xing, Mengdao, Sun, Guangcai, Li, Yachao, Bao, Zheng
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2010; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Array manifold; autofocus; Convergence; Cost function; Echoes; Entropy; Feasibility; Image quality; Inverse synthetic aperture radar; inverse synthetic aperture radar (ISAR); minimum entropy; Motion compensation; Optimization; Optimization methods; Phase error; Phase estimation; Radar scattering; Robustness; Signal processing algorithms; signal subspace; Subspaces; Theoretical analysis
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2009.2031658

In this letter, a novel approach to autofocus for inverse synthetic aperture radar (ISAR) imaging called minimum entropy via subspace autofocus is presented. This scheme uses the weighted signal subspace to express the phase errors left in the echoes after range-bin alignment and estimates the optimal weights sequentially via an optimization algorithm based on an entropy minimization principle, and its robustness and convergence can be ensured by the optimization method. Both the theoretical analysis and processing results of the real ISAR data have confirmed the feasibility of this new scheme.