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
Issue Date: 2023
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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