Leveraging Complex-Valued Federated Learning for Accurate and Privacy-Respectful Threat Detection Based on Millimeter-Wave Imaging

Leveraging Complex-Valued Federated Learning for Accurate and Privacy-Respectful Threat Detection Based on Millimeter-Wave Imaging

Millimeter-wave (MMW) radar systems are widely recognized for their affordability, safety, and effectiveness in concealed object (CO) detection. This paper introduces a novel high-resolution pseudo-image-based CO detection framework that leverages complex-valued convolutional neural networks (CV-CNN...

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Bibliographic Details
Main Authors: Hadi Mahdipour, Jaime Laviada, Fernando Las-Heras Andres, Mehdi Sookhak
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Complex-valued convolutional neural networks (CV-CNNs)
complex-valued federated learning (CV-FL)
concealed object (CO) detection
decision-level fusion (DLF)
millimeter-wave (MMW) radar systems
real-valued convolutional neural networks (RV-CNNs)
Online Access:https://ieeexplore.ieee.org/document/11015809/
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https://ieeexplore.ieee.org/document/11015809/

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