Bayesian Azimuth Angular Superresolution Algorithm for Forward-looking Scanning Radar

In real aperture radar, the limited azimuth angular resolution determined by the physical size of the antenna seriously restricts the applications of this type of imaging system. This paper presents a deconvolution algorithm based on Maximum a posteriori (MAP) criterion to realize azimuth angular superresolution for real-beam radar. Firstly, the received signal of real beam radar in azimuth dimension is modeled as the convolution of antenna pattern and target scattering. Then, the MAP objective function is built in terms of the assumption that the noise obeys Gaussian distribution and the target scattering characteristic is described by Lognormal distribution. Finally, the iterative expression is developed to estimate the original target scattering coefficient distribution. The comparison of simulation results between the proposed algorithm and other common algorithms is given to verify the performance of our proposed algorithm.