A sparse bayesian approach to multistatic radar imaging

We tackle the problem of multistatic radar image formation by simultaneously exploiting the sparsity and covariance structure of radar images measured by a local GSM distribution of wavelet coefficients. Our aim is to gauge the extent to which such local statistical information can be leveraged in addition to the commonly used l₁ sparsity constraint. Though we assume knowledge of the covariance structure of the source image, this provides a benchmark for subsequent relaxation of this assumption and its generalization to more complex probabilistic models of scene structure.