ISAR Image Reconstruction with Heavily Corrupted Data Based on Normal Inverse Gaussian Model

Abstract

Inverse synthetic aperture radar (ISAR) is a potent radar system which generates two-dimensional signal on the range-Doppler domain by using target’s motion. ISAR imaging of targets is an important tool for automatic target recognition and classification in the defense and aerospace industry. In this paper, we focus upon the problem of ISAR imaging at low signal to noise ratio (SNR). Nonsubsampled directional filter bank (NSDFB) is a very useful tool in studying the directional features in two-dimensional signals. This paper offers a novel ISAR imaging approach by using NSDFB coefficients modeling. Bayesian maximum a posteriori is used where normal inverse Gaussian model is presumed for estimating ISAR image at low SNR. We applied NSDFB transform to ISAR image and implemented procedure to describe the characteristics of the algorithm. Both simulated and real ISAR data have been tested. The proposed technique keeps a balance between feature preservation and noise suppression. Finally, experimental results show that the offered technique outperforms other in terms of visual assessment and image evaluation parameters.