Compressive sensing for direct millimeter-wave holographic imaging.

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.