Inverse synthetic aperture radar autofocus imaging of block structure targets with sparse aperture

Abstract

Inverse synthetic aperture radar (ISAR) autofocus imaging performance is challenged by the residual phase errors that arise from the traditional motion compensation methods with sparse aperture. Moreover, sparse reconstruction algorithms usually ignore the block sparsity in the ISAR image, which cannot recover structured information of the block structure target completely. An imaging method based on joint minimum Tsallis entropy and pattern-coupled sparse Bayesian learning algorithm is proposed to achieve ISAR autofocus imaging of block structure targets with sparse aperture. A pattern-coupled hierarchical complex Gaussian prior is utilized to characterize the correlation among ISAR image pixels in the complex domain. Such a prior model has the potential to encourage block-sparse patterns and achieve ISAR sparse aperture imaging of block structure targets without knowing the block partition prior information. The residual phase errors are estimated based on the minimum Tsallis entropy criterion during the sparse reconstruction of ISAR image to achieve ISAR autofocus imaging, which has the advantage of improving the computational efficiency compared with minimum Shannon entropy criterion. The superiority of the proposed algorithm is verified by the experimental results based on both simulated and measured data.

Imaging and detection method for low signal-to-noise ratio airborne synthetic aperture ladar signals

“…For high-reflectivity cooperative targets at a distance of 1.6 km, they achieved an imaging result of 1 m width at a resolution better than 3.3 cm. In the imaging process, they used the phase gradient autofocus (PGA) 4,5 algorithm of microwave synthetic aperture radar to suppress the impact of vibration.…”