Bayesian angular super-resolution for sea-surface target in forward-looking scanning radar

Abstract

As the traditional imaging dead zone, how to achieve high angular resolution in forward-looking region is always a big challenge in radar imaging field. In recent years, many approaches are proposed to solve this problem. However, most of the proposed forward-looking imaging approaches fail to work in the complex sea-surface situation. In this study, a Bayesian deconvolution method aimed at the sea-surface target is presented, which relies on the convolution model between the target distribution and the antenna pattern of scanning radar. In view of the situation of sea surface, Rayleigh distribution is adopted to describe the statistical property of sea-surface clutter, and lognormal distribution is employed to represent the prior distribution of sea-surface target. Then, the optimisation theory is utilised to obtain the iterative estimated solution. The simulation results are given to verify the superior performance of the presented method.

Keywords

• radar clutter
• deconvolution
• bayes methods
• image sensors
• iterative methods
• image resolution
• radar imaging
• radar detection
• bayesian angular super-resolution
• sea-surface target
• scanning radar
• traditional imaging dead zone
• high angular resolution
• radar imaging field
• imaging approaches
• complex sea-surface situation
• bayesian deconvolution method
• target distribution
• sea surface
• sea-surface clutter