High-Resolution Sparse Subband Imaging Based on Bayesian Learning With Hierarchical Priors

To obtain higher range resolution without incurring significant hardware costs, this paper proposes a novel method for high-resolution sparse subband imaging based on Bayesian learning. The signal model is derived and a probabilistic model is constructed. In particular, hierarchical sparse-promoting priors are imposed on the distribution of scattering centers, which is conjugate to the likelihood function. Then, a closed-form solution is derived based on the MAP-expectation-maximization framework. A multilevel dictionary which automatically adjusts the distance between adjacent atoms is adopted to achieve refined estimation with moderate computational burden. Finally, a coherent processing method is addressed. Experimental results have demonstrated the effectiveness of the proposed method in low signal-to-noise ratio and complex target scenarios.