Detection of Minerals and Hidden Objects in Airports Using Millimeter-Wave Radar (MMW) A MATLAB and AI-Based Approach

الملخص

Exposing hidden objects and metals to airport safety has become a serious challenge, especially with the increasing development of smuggling techniques and the limitations of traditional safety methods such as X-rays and metal detectors. The study looked at the use of millimeter wave radar (MMW) in conjunction with artificial intelligence (AI) to detect hidden objects in the airport environment in real time. MMW radar, which is known for its ability to penetrate various materials, is used to take radar signals from various hidden objects including metals, metals and non-metallic elements. Captured radar data is processed and analyzed using MATABs, particularly with AI-based models, particularly convolutional neural networks (CNNs) and support vector machines (SVMs) to classify and identify these objects based on their unique radar signatures. This study provides a detailed methodology for data collection, pre-processing, feature extraction, and AI model training, and evaluates the performance of the proposed system in terms of detection accuracy, accuracy, and memory. These results reflect the ability of MMW radar and artificial intelligence devices to significantly improve airport security and provide a non-invasive, fast and reliable way to detect hidden objects, bridging the limitations of existing security screening technologies. Future work will focus on improving model performance, expanding datasets, and integrating real-time processing capabilities for large-scale deployment in operational airport environments.