Multisensor inverse synthetic aperture radar imaging and phase adjustment based on combination of sparsity and total variation

Compared with conventional single-sensor inverse synthetic aperture radar (ISAR), multisensor ISAR technique can reduce the integration time and improve the resolution of the image effectively. One solution is investigated for multisensor ISAR imaging and phase adjustment with sparse measurements via the compressed sensing (CS) framework. In most previous research of CS-based radar imaging, the target is modeled as a few strong scatterers that randomly distributed in the imaging plane and only the image domain sparsity assumption is used in image reconstruction, which will result in the degradation of image quality, especially when the measurements are limited. In practical ISAR imaging, some strong scatterers facing the radar always form flat regions in high-resolution radar imaging. Therefore, the dependence and redundancy of these scatterers can also be exploited in target reconstruction. We utilize this information and propose a multiplatform ISAR imaging method that combines the image domain sparsity and edge-preserving total variation to improve the image quality with sparse measurements. Meanwhile, an iterative minimization approach is also used to process the phase adjustment with the discontinuous echo data. Experimental results show the effectiveness of the proposed method. (C) 2018 Society of Photo Optical Instrumentation Engineers (SPIE)