Scopus
🔓 Açık Erişim YÖKSİS Eşleşti
Comparison of ML algorithms to distinguish between human or human-like targets using the HOG features of range-time and range-Doppler images in through-the-wall applications
Turkish Journal of Electrical Engineering and Computer Sciences · Ocak 2022
YÖKSİS Kayıtları
Comparison of ML algorithms to distinguish between human or human-like targets using the HOG features of range-time and range-Doppler images in through-the-wall applications
Turkish Journal of Electrical Engineering and Computer Sciences · 2022 SCI-Expanded
DOKTOR ÖĞRETİM ÜYESİ YUNUS EMRE ACAR →
Comparison of ML algorithms to distinguish between human or human-like targets using the HOG features of range-time and range-Doppler images in through-the-wall applications
Turkish Journal of Electrical Engineering and Computer Sciences · 2022 SCI-Expanded
PROFESÖR İSMAİL SARITAŞ →
Makale Bilgileri
DergiTurkish Journal of Electrical Engineering and Computer Sciences
Yayın TarihiOcak 2022
Cilt / Sayfa30 · 2086-2096
Scopus ID2-s2.0-85142295148
Erişim🔓 Açık Erişim
Özet
When detecting the human targets behind walls, false detections occur for many systematic and environmental reasons. Identifying and eliminating these false detections is of great importance for many applications. This study investigates the potential of machine learning (ML) algorithms to distinguish between the human and human-like targets behind walls. For this purpose, a stepped-frequency continuous-wave (SFCW) radar has been set up. Experiments have been carried out with real human targets and moving plates imitating a regular breath of a healthy human. Unlike conventional methods, human and human-like returns are classified using range-Doppler images containing range and Doppler information. Then, the histogram of oriented gradients (HOG) features of the range-Doppler images are extracted, and the number of these features is reduced by principal component analysis (PCA). Finally, popular ML algorithms are executed to distinguish the human and human-like returns. The performances of the ML algorithms are compared for both range-time and range-Doppler images with or without HOG features. Experiments have indicated that the HOG features of the range-Doppler profiles provide the best results with the support vector machine (SVM) classifier with an accuracy of 93.57%.
Yazarlar (3)
1
Yunus Emre Acar
2
Ismail Saritas
3
Ercan Yaldiz
Anahtar Kelimeler
HOG feature
human detection
machine learning
radar
through-the-wall
Kurumlar
Konya Technical University
Konya Turkey
Selçuk Üniversitesi
Selçuklu Turkey
Metrikler
1
Atıf
3
Yazar
5
Anahtar Kelime