High-Resolution Radar Imaging in Complex Environments Based on Bayesian Learning With Mixture Models

We address the problem of high-resolution radar imaging in complex environments in a Bayesian framework. We perform model order selection and sparse weights estimation via the maximum a posterior-expectation maximization technique in a statistical model, where the noise obeys Gaussian mixture distribution; and the weights are governed by the sparsity-promoting Gamma-Gaussian hierarchical prior. The proposed method has closed-form solution and can be implemented efficiently by matrix operation. Experiments has shown that accounting for the noise with Gaussian mixture model instead of single Gaussian greatly improves the performance, and the proposed method provides an effective way of high-resolution radar imaging in complex environments such as barrage jamming and micro-Doppler interference.