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Multipolarization Through-Wall Radar Imaging Using Low-Rank and Jointly-Sparse Representations.

Van Ha Tang, Abdesselam Bouzerdoum, Son Lam Phung

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |January 19, 2018
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new low-rank and sparse imaging model for through-the-wall radar imaging (TWRI). It effectively removes wall clutter and reconstructs clear images, even with reduced data acquisition.

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    Area of Science:

    • Radar Imaging
    • Signal Processing
    • Electromagnetics

    Background:

    • Through-the-wall radar imaging (TWRI) faces challenges with wall clutter.
    • Existing methods often separate wall clutter mitigation from image formation.
    • Compressed sensing (CS) offers potential for faster TWRI data acquisition.

    Purpose of the Study:

    • To develop a joint model for wall clutter mitigation and image formation in multichannel TWRI.
    • To leverage the inherent structures of through-wall radar signals for improved imaging.
    • To address limitations of independent processing in existing TWRI techniques.

    Main Methods:

    • A novel low-rank and sparse imaging model is proposed.
    • The model exploits low-rank structure of wall reflections and jointly-sparse structure of polarization images.
    • A proximal gradient-based iterative algorithm is used to solve the optimization problem.

    Main Results:

    • The proposed model effectively mitigates wall clutter in multichannel TWRI.
    • Clear multichannel images of indoor targets are reconstructed.
    • The method demonstrates high performance even with significantly reduced measurements (compressive sensing).

    Conclusions:

    • The joint low-rank and sparse imaging model offers a significant advancement in TWRI.
    • Simultaneous wall clutter removal and image reconstruction are achieved.
    • The approach enhances stationary target detection and imaging quality in challenging TWRI scenarios.