Bayesian Azimuth Super-resolution of Sea-surface Target in Forward-looking Imaging

Aiming at the problem that real-aperture scanning radar in forward-looking imaging has low azimuth angular resolution for sea-surface targe, this paper presents an angular super-resolution method based on the maximum a posteriori (MAP) criterion. Firstly, sea-surface clutter can be well fitted with Weibull distribution, so this paper derives a Weibull-based maximum likelihood(ML) estimation method based on Newton-Raphson iteration to effectively improve the azimuthal resolution. However, the Weibull-based ML method has limited robustness of noise suppression and randomly converges to the local optimal solution under low signal to clutter ratio. Therefore, this paper adds a sparse distribution as the prior distribution of sea-surface target, which can be regarded as a constraint term so that proposed MAP estimation method can not only obtain the better property of noise suppression, but also well converge to the global optimum. Finally, the simulation results are given to verify the performance of proposed method.