Inverse synthetic aperture radar image reconstruction with heavily corrupted data based on heavy-tailed Levy model

Inverse synthetic aperture radar (ISAR) is a powerful radar-processing technique that uses target's motion to generate images on the range-Doppler plane. In the defense industry, ISAR imaging of moving targets is an important tool for automatic target recognition. We focus on the problem of ISAR imaging at low signal-to-noise ratio (SNR). The nonsubsampled directional filter bank (NSDFB) is a very useful tool in analyzing the directional information in two-dimensional signals. This paper presents an ISAR Imaging algorithm using NSDFB coefficients modeling. Bayesian maximum a posteriori is used where the heavy-tailed Levy model is assumed for estimating an ISAR image at low SNR. We applied NSDFB transform to the ISAR image and developed a simulation procedure to describe the characteristics of the algorithm. Both simulated and real ISAR data have been tested. The proposed algorithm maintains a balance among noise suppression, feature preservation, and computational time. Finally, the experiments show that the proposed method outperforms others in terms of visual evaluation and image assessment parameters. (C) 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)