Abstract
A multiple measurement vector (MMV) model blocks sparse signal recovery. ISAR imaging algorithm is proposed to improve ISAR imaging quality. Firstly, the sparse imaging model is built, and block sparse signal recovery algorithm-based MMV model is applied to ISAR imaging. Then, a negative exponential function is proposed to approximately block L0 norm. The optimization solution of smoothed function is obtained by constructing a decreasing sequence. Finally, the correction steps are added to ensure the optimal solution of the block sparse signal along the fastest descent direction. Several simulations and real data simulation experiments verify the proposed algorithm has advantages in imaging time and quality.
Highlights
Due to the characteristics of long distance, all-weather and all-weather, the inverse synthetic aperture radar (ISAR) imaging technology has been widely used in military, civil, and other fields [1, 2]
It has been used in ISAR imaging [7, 8], MIMO radar signal processing [9, 10], and radar parameter estimation [11,12,13,14]
Because the actual contour of the target in sky imaging background is smaller than imaging area, the scatters of the target have sparse structure compared with the imaging area. e traditional sparse ISAR imaging algorithm mainly considers the recovery of individual scatters
Summary
Due to the characteristics of long distance, all-weather and all-weather, the inverse synthetic aperture radar (ISAR) imaging technology has been widely used in military, civil, and other fields [1, 2]. E strong scatterers of ISAR target can occupy many resolution cells, which have usually clusters or blocks in the imaging region In this case, the common sparse reconstruction algorithm cannot completely describe the characteristic of target. Most of the applications of sparse signal recovery algorithm in ISAR imaging are based on the single measurement vector (SMV) model, in which the ISAR echo signals are divided according to the distance unit and the image can be obtained by combining the reconstructed result of each distance unit. E compressive sensing multiple measurement vector model can repeat observations of the information, and the MMV model can obtain better performance and improve the sparse signal reconstruction efficiency compared with that of the SMV model. In order to improve ISAR imaging quality, a two-dimensional sparse signal reconstruction algorithm of ISAR imaging based on MMV model is proposed.
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