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Compressive sensing for direct millimeter-wave holographic imaging.

Lingbo Qiao, Yingxin Wang, Zongjun Shen

    Applied Optics
    |May 14, 2015
    PubMed
    Summary

    Compressive sensing (CS) enhances millimeter-wave (MMW) holographic imaging by enabling faster data acquisition and reducing hardware costs. This method allows for high-quality MMW image recovery using significantly fewer measurements than traditional techniques.

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    Area of Science:

    • Applied Physics
    • Electromagnetics
    • Signal Processing

    Background:

    • Millimeter-wave (MMW) holographic imaging is crucial for security surveillance, offering amplitude and phase data.
    • Existing MMW systems face challenges with high costs and lengthy data acquisition times.
    • Compressive sensing (CS) offers a potential solution for sparse sampling to overcome these limitations.

    Purpose of the Study:

    • To extend compressive sensing (CS) techniques to direct millimeter-wave (MMW) holographic imaging.
    • To reduce the number of antenna units and data acquisition time in MMW imaging systems.
    • To evaluate the effectiveness of CS for near-field MMW imaging and complex targets.

    Main Methods:

    • Derived an exact formula for direct MMW holographic reconstruction based on scalar diffraction theory.
    • Introduced CS reconstruction strategies for complex-valued MMW images using the derived formula.
    • Evaluated three sparsity bases (total variance, wavelet, curvelet) and discussed various sampling patterns for different MMW imaging system configurations.

    Main Results:

    • Demonstrated the feasibility of recovering MMW images using CS from sub-Nyquist rate measurements (1/2 or 1/4).
    • Validated the CS approach through both numerical simulations and experimental results.
    • Showcased the applicability of CS for near-field imaging and complex targets.

    Conclusions:

    • Compressive sensing significantly reduces data acquisition time and hardware requirements for MMW holographic imaging.
    • The developed CS strategies are effective for reconstructing MMW images from sparse measurements.
    • This research paves the way for more practical and cost-effective MMW security surveillance systems.