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Resolution Enhancement for Millimeter-Wave Radar ROI Image with Bayesian Compressive Sensing.
Pengfei Xie1, Jianxin Wu1, Lei Zhang1
1School of Electronics and Communication Engineering, Sun Yat-Sen University, Shenzhen 518107, China.
Sensors (Basel, Switzerland)
|August 12, 2022
Summary
This study enhances millimeter-wave (MMW) imaging resolution for security systems using Bayesian compressive sensing (BCS). The method improves detection and recognition of suspicious targets without increased computational cost.
Area of Science:
- Applied Physics
- Signal Processing
- Imaging Science
Background:
- Millimeter-wave (MMW) imaging is crucial for security systems, but conventional synthetic aperture radar (SAR) algorithms offer limited resolution.
- Image resolution directly impacts the performance of suspicious target detection and recognition in MMW security applications.
Purpose of the Study:
- To enhance the resolution of Region of Interest (ROI) images in active MMW imaging.
- To improve the detection and recognition capabilities of MMW security systems.
Main Methods:
- Utilized super-resolution (SR) imaging techniques.
- Implemented Bayesian compressive sensing (BCS) for resolution enhancement.
- Employed fast Fourier transform (FFT) for computational efficiency.
Main Results:
- Successfully exploited the spatial sparsity of MMW ROI images.
- Achieved significant resolution enhancement in MMW ROI images.
- Demonstrated effectiveness through both simulated and measured experiments.
Conclusions:
- The proposed BCS-based SR imaging scheme effectively improves MMW ROI image resolution.
- This advancement offers enhanced capabilities for suspicious target detection and recognition in security systems.
- The method provides resolution enhancement without incurring additional computational costs.
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