BSBL-based multiband fusion ISAR imaging

Abstract

Multiband fusion imaging can effectively improve the range resolution of inverse synthetic aperture radar (ISAR) imaging. In this study, the block sparse Bayesian learning (BSBL) method is applied to multiband fusion imaging to achieve high-resolution ISAR imaging of a block-structured target. The BSBL method is suitable for the ISAR imaging of numerous and continuous scatterers because it considers the block structure characteristics of the signal. The validity of the proposed method is verified by the simulation and real-data experimental results.

Keywords

• Bayes methods
• radar imaging
• image resolution
• image fusion
• synthetic aperture radar
• learning (artificial intelligence)
• BSBL-based multiband fusion ISAR imaging
• multiband fusion imaging
• range resolution
• inverse synthetic aperture radar imaging
• block sparse Bayesian learning method
• high-resolution ISAR imaging
• block-structured target
• BSBL method
• block structure characteristics