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Related Concept Videos

Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

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Related Experiment Video

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Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
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Published on: February 12, 2014

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

Yi Li, Weijie Xia, Lingzhi Zhu

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |July 13, 2026
    PubMed
    Summary

    This study introduces a novel tensor completion framework to enhance high-resolution microwave imaging in distributed MIMO radar systems. The method effectively suppresses artifacts caused by sparse apertures, improving target reconstruction accuracy.

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    Published on: November 14, 2025

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    Last Updated: Jul 15, 2026

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    Clinical Imaging of Microwave Mammography
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    Clinical Imaging of Microwave Mammography

    Published on: November 14, 2025

    Area of Science:

    • Electromagnetic imaging
    • Radar systems engineering
    • Signal processing

    Background:

    • Distributed MIMO radar at millimeter-wave frequencies offers high-resolution imaging capabilities.
    • Sparse virtual apertures are employed to reduce hardware complexity but introduce artifacts like grating lobes and sidelobes.
    • These artifacts cause aliasing in the spatial frequency domain, degrading image quality.

    Purpose of the Study:

    • To address antenna array synthesis limitations in sparse-aperture distributed MIMO radar systems.
    • To develop a robust imaging solution that suppresses artifacts and improves target reconstruction.
    • To leverage joint low-rank and sparsity constraints for enhanced radar imaging.

    Main Methods:

    • A noisy tensor completion framework is proposed, reorganizing radar echoes into high-dimensional tensors.
    • Joint low-rank and sparsity constraints inherent in radar scattering are exploited.
    • An adaptive singular-value reweighting scheme and an alternating direction method of multipliers (ADMM) algorithm are utilized.

    Main Results:

    • The proposed method demonstrates superior artifact suppression compared to state-of-the-art techniques.
    • Experimental validation using a W-band distributed MIMO radar system confirms improved target reconstruction.
    • The framework effectively preserves dominant electromagnetic scattering components while mitigating noise.

    Conclusions:

    • The tensor completion framework provides a robust solution for sparse-aperture distributed MIMO radar imaging.
    • This approach directly addresses antenna array pattern limitations through tensor-based aperture synthesis.
    • The findings enable higher fidelity microwave imaging in systems with reduced hardware complexity.