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

Abstract

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.