Robust compressive multi-input–multi-output imaging

Canonical multi-input–multi-output (MIMO) imaging methods suffer from limited resolution, poor robustness against noise and high computational complexity, especially when the array aperture is limited (affecting angular resolution) and the number of snapshots is limited (affecting Doppler resolution). In this study, the authors discuss a new range-angle-Doppler monostatic MIMO imaging method through adaptive estimation of the generalised Cauchy prior distribution (GCD). The superiority of GCD-based model over existing ℓ_p-norm-based model (which actually assumes the prior as general Gaussian distribution) is theoretically verified through the issue of signal compressibility. The authors adapt a reweighted ℓ₂-norm iterative algorithm to solve the model. In our model the authors do not pre-define the scaling factor of the prior distribution and, during the iteration, the authors use a novel ´quantile-of-OS´ method to adaptively estimate the scaling parameter of the prior distribution, enhancing the robustness of the method. Simulation results verify the image quality and speed advantages of the proposed method.