Grating Lobe/Sidelobe Suppression Method for Near-Field Distributed MIMO Imaging Array

Distributed multiple-input-multiple-output(MIMO)arrays obtain a larger virtual aperture by combining subarrays at different locations. However, subarray spacing leads to sparse spatial sampling, introducing grating lobes in the imaging results. In this article, we propose a grating lobe suppression method utilizing array pattern migration and multiapodization (MA) for large-aperture distributed MIMO imaging arrays under near-field conditions. The MA processing includes applying multiple sets of compensation weights to achieve pattern migration of the imaging result and selecting the minimum value as the output. For further image quality improvement, we derive sum and difference images of the imaging results from sum and difference patterns of the array factor, and then propose a novel sidelobe suppression method utilizing weighted sum and difference (WSD) image cancellation. On this basis, we combine the two proposed methods with the backprojection (BP) algorithm through adaptive weighting techniques to eliminate the spatially variant characteristics of the array pattern in the near-field range, introducing a weighted BP (WBP) algorithm for near-field imaging. The effectiveness of the proposed methods has been verified through multiple simulations, measurements, and a publicly available dataset.