Bayesian Angular Superresolution Algorithm for Real-Aperture Imaging in Forward-Looking Radar

Abstract

In real aperture imaging, the limited azimuth angular resolution seriously restricts the applications of this imaging system. This report presents a maximum a posteriori (MAP) approach based on the Bayesian framework for high angular resolution of real aperture radar. First, Rayleigh statistic and the lq norm (for 0 < q ≤ 1) sparse constraint are considered to express the clutter property and target scattering coefficient distribution, respectively. Then, the MAP objective function is established according to the hypotheses above. At last, a recursive iterative strategy is developed to estimate the original target scattering coefficient distribution and clutter statistic. The comparison of simulations and experimental results are given to verify the performance of our proposed algorithm.

Keywords

• real aperture radar
• angular superresolution
• Bayesian framework
• sparse constraint
• maximum a posteriori