Extended Target Localization in Distributed MIMO Radars Employing One-Bit ADCs

Low-complexity distributed MIMO radars can be achieved by implementing one-bit quantization in each receiver, significantly reducing data volume and hardware cost. Previous research typically assumes point targets, where each target contains a single scatterer, thus limiting their applicability to extended target scenarios. In this study, we propose a sparse reconstruction method tailored for locating extended targets in distributed MIMO radars with one-bit ADCs. The proposed method relies on the sparse Bayesian learning framework, simultaneously addressing the non-linear quantization effect and the pattern-coupled block sparse structure resulting from extended targets. Our numerical experiments demonstrate that our method achieves high accuracy in localizing extended targets compared to existing methods.