Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.13091/4951
Title: | Classification of Human Target Movements Behind Walls Using Multi-Channel Range-Doppler Images | Authors: | Acar, Y.E. Ucar, K. Saritas, I. Yaldiz, E. |
Keywords: | Histogram of oriented gradients (HOG) Human activity classification Radar Range-Doppler images Through-the- wall (TTW) Uniform Linear Array (ULA) Deep learning Doppler effect Image classification Learning systems Network architecture Radar imaging Activity classifications Doppler images Histogram of oriented gradient Histogram of oriented gradients Human activities Human activity classification Range doppler Range-dopple image Through the wall Through-the- wall Uniform linear array Uniform linear arrays Convolutional neural networks |
Publisher: | Springer | Abstract: | Range-Doppler images represent one of the most informative radar data forms, providing range and frequency information. This study explores the performance of machine learning and deep learning techniques in classifying human activities behind walls using Range-Doppler images. Therefore, we input the HOG features of Range-Doppler images into various machine-learning approaches. Although the HOG feature enhances the performance of machine learning methods, we observe the superior performance of Convolutional Neural Network (CNN) architectures in a more complex scenario in which the number of target activity classes is higher. To obtain sufficient data for the CNN architecture, we combine the Uniform Linear Array (ULA) and stepped-frequency continuous-wave (SFCW) structures, enabling the acquisition of multi-channel data. The experimental results demonstrate both the improvement of the machine learning accuracy from 95.33% to 98.67% through the HOG + Range-Doppler approach and an approximately 6% enhancement in CNN performance achieved through the SFCW-ULA combination. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. | URI: | https://doi.org/10.1007/s11042-023-17759-8 https://hdl.handle.net/20.500.13091/4951 |
ISSN: | 1380-7501 |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections |
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