KEYWORDS: Radar, Radar sensor technology, Education and training, 3D modeling, Radar signal processing, Data modeling, Signal detection, Detection and tracking algorithms, 3D acquisition, Sensors
With the exacerbation of global population aging, the issue of falls among the elderly is increasingly drawing attention from various sectors of society. Consequently, the development of an effective human fall detection system is crucial for timely identification of fall incidents and provision of emergency assistance. This paper proposes a fall detection method based on non-contact sensors, aiming to achieve real-time monitoring and accurate identification of fall events through advanced sensor technology and data processing algorithms. Initially, this study employs low-frequency ultra-wideband radar (UWB) as the primary tool for data acquisition, followed by the utilization of deep learning techniques to extract key dynamic features from radar data, thus providing a reliable foundation for fall event identification. Furthermore, this paper designs and implements a fall recognition model based on Convolutional Neural Networks (CNN), which, through training on a large amount of radar image data, learns to distinguish patterns between normal activities and falling behavior. The results demonstrate that the proposed method can effectively detect falls under various common daily activities, with a recognition rate as high as 99.338%.
The low-frequency Synthetic Aperture Radar (SAR) can penetrate foliage and detect the targets concealed under the foliage, while the high-frequency SAR cannot. The low-frequency SAR image and high-frequency SAR image cannot be directly compared to detect the foliage-penetrating (FOPEN) targets due to their distinct statistical properties. This paper presents an FOPEN target detection method based on bi-frequency SAR and conditional generative adversarial networks (CGAN). The high-frequency SAR image is translated into low-frequency one by the CGAN. The direct comparison between the real and generated low-frequency SAR images is used to detect the FOPEN targets. Experiments on P- and Ku-band SAR images show that our method performs better than the double-parameter constant false alarm rate detection (DP-CFAR) using the single P-band SAR image.
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