High-Resolution Radar Imaging with Unknown Noise

This paper addresses the problem of high-resolution radar imaging in complex environments with unknown noise in a Bayesian framework. In the new statistical model, the noise obeys Gaussian mixture distribution, and the weights are governed by the sparsity-promoting Gamma-Gaussian hierarchical prior. Then, the weights are estimated via the maximum a posterior-expectation maximization (MAP-EM) technique. Experiments have shown that the proposed method provides an effective way of high-resolution radar imaging in complex environments such as barrage jamming.