A Bayesian Angular Superresolution Method With Lognormal Constraint for Sea-Surface Target

Abstract

Maximum a posteriori (MAP) approach, based on Bayesian criterion, is proposed to overcome the low azimuth resolution in real-aperture imaging. The essence of this approach is to use the statistical characteristics of the imaging background and target to invert the real target scene. This paper presents a deconvolution method based on Maximum a posteriori (MAP) criterion, which combines the Rayleigh distribution and Lognormal distribution, to realize high angular resolution for sea-surface target. Firstly, Rayleigh distribution is considered to express the statistical properties of sea clutter. Moreover, the Lognormal distribution is employed to represent the statistical properties of target as prior information. The reason is that Lognormal distribution can be approximatively regarded as a combined constraint term. Finally, the optimization theory is utilized to obtain the iterative estimated solution. The processed results of simulation and measured data are given to verify the performance of proposed algorithm.

Keywords

• Radar imaging
• angular superresolution
• lognormal restrict
• sea-surface target