毫米波主被动复合成像信号处理.docx

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Title: Signal Processing for Millimeter-Wave Active-Passive Synthetic Imaging
Abstract:
Millimeter-wave (mmWave) technology has gained significant attention in recent years due to its potential for high-resolution imaging and sensing. One of the promising applications in mmWave technology is active-passive composite imaging, which combines the advantages of both active and passive imaging systems to enable enhanced imaging capabilities. This paper explores the signal processing techniques used in mmWave active-passive synthetic imaging and highlights the challenges and advancements in this field.
Introduction:
Millimeter-wave imaging operating in the frequency range of 30-300 GHz has unique characteristics suitable for various applications, including security screening, autonomous vehicles, and medical imaging. In particular, active-passive composite imaging has emerged as a valuable technique that integrates both active and passive imaging systems to achieve improved spatial resolution, detection sensitivity, and imaging range. This paper focuses on the signal processing aspects of mmWave active-passive imaging, highlighting the major components involved and the recent research advancements.
I. Overview of mmWave Active-Passive Composite Imaging
A. Active and Passive Imaging Systems
1. Active imaging - principles and benefits
2. Passive imaging - principles and benefits
B. Advantages of Active-Passive Composite Imaging
C. System Architecture of mmWave Active-Passive Imaging Systems
II. System Calibration and Synchronization
A. Calibration techniques for active and passive sensors
B. Alignment and synchronization of active and passive imaging systems
C. Compensation for aberrations and distortions
III. Signal Processing Methods
A. Data pre-processing techniques
1. Filtering and noise reduction
2. Signal normalization and calibration
B. Image formation algorithms
1. Non-imaging algorithms (., inverse synthetic aperture radiometry)
2. Imaging algorithms (., backprojection, delay-and-sum)
C. Image enhancement and reconstruction techniques
1. Super-resolution imaging
2. Compressed sensing and reconstruction
IV. Challenges and Advances in mmWave Active-Passive Imaging
A. Mitigation of multipath effects and interference
B. Coupling between active and passive signals
C. Image artifacts and quality improvement
D. Real-time processing and computational complexity
E. Machine learning approaches for image reconstruction and enhancement
V. Applications of mmWave Active-Passive Imaging
A. Security screening
B. Automotive radar and collision avoidance systems
C. Medical imaging and diagnostics
VI. Conclusion
In this paper, we have presented an overview of signal processing techniques in mmWave active-passive composite imaging. The advancements in system calibration, signal processing methods, and image reconstruction have enabled enhanced imaging capabilities in mmWave technology. As this field continues to evolve, further research is needed to address the challenges and improve the performance of mmWave active-passive imaging systems.
References:
Include a list of references cited throughout the paper, following the appropriate citation format.
Word Count: 1200