Noisy Tensor Completion for Sparse-Aperture Microwave Imaging in Distributed MIMO Radar Networks

Distributed multiple-input multiple-output (MIMO) radar networks operating at millimeter-wave frequencies enable high-resolution microwave imaging but face fundamental limitations in antenna array synthesis. Sparse virtual apertures reduce hardware complexity yet introduce severe grating lobes and sidelobe artifacts, which degrade image fidelity via aliasing in the electromagnetic (EM) spatial frequency domain. To address these antenna array synthesis challenges, we propose a noisy tensor completion framework exploiting joint low-rank and sparsity constraints inherent in radar scattering. Our method reorganizes radar echoes into high-dimensional tensors with dispersed missing elements, explicitly modeling the sparse array sampling process. A key innovation is an adaptive singular-value reweighting scheme that preserves dominant EM scattering components while suppressing noise-corrupted interference. The resulting optimization is solved via an alternating direction method of multipliers (ADMM) algorithm. Extensive EM simulations and experimental validation using a prototype W-band (77 GHz) distributed MIMO radar system demonstrate superior artifact suppression and target reconstruction over state-of-the-art methods. This establishes a robust imaging solution for sparse-aperture systems by directly addressing antenna array pattern limitations through tensor-based aperture synthesis.