TY - JOUR
T1 - Autonomous Human and Animal Classification Using Synthetic 2D Tensor Data Based on Dual-Receiver mmWave Radar System
AU - Darlis, Arsyad R.
AU - Ibrahim, Nur
AU - Subiantoro, Aries
AU - Yusivar, Feri
AU - Albaqami, Nasser Nammas
AU - Prabuwono, Anton Satria
AU - Kusumoputro, Benyamin
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2023
Y1 - 2023
N2 - Human and animal classification under the rubble is necessary to successfully rescue survivors post-disaster, and interference from other animals is becoming an issue in noncontact monitoring with radar. Many animals in indoor and outdoor environments have characteristics similar to humans, where they are easily mistaken for human targets, which would trigger a false alarm. A novel human and animal classification through single-receiver and dual-receiver mmWave radar at 77 GHz is presented. The system uses feedback signal responses from targets with dual-receiver mmWave radar and classifies human and animal features using a convolution neural network (CNN) based on synthetic 2D Tensor data. The performance is compared to the classification result using a deep learning and backpropagation neural network (BPNN) from a single mmWave radar dataset under the measurement distance and number of objects. Our experimental results showed that using dual receivers was very useful and performed well in classifying humans and animals, with an average accuracy rate of 99% in the classification of 2 and 3 classes, 83% in 6 classes, and 68% in 10 classes. The performance showed an excellent result considering that this system's application avoids false alarms in human rescue applications during post-disaster periods and exceeds the accuracy of several previous studies.
AB - Human and animal classification under the rubble is necessary to successfully rescue survivors post-disaster, and interference from other animals is becoming an issue in noncontact monitoring with radar. Many animals in indoor and outdoor environments have characteristics similar to humans, where they are easily mistaken for human targets, which would trigger a false alarm. A novel human and animal classification through single-receiver and dual-receiver mmWave radar at 77 GHz is presented. The system uses feedback signal responses from targets with dual-receiver mmWave radar and classifies human and animal features using a convolution neural network (CNN) based on synthetic 2D Tensor data. The performance is compared to the classification result using a deep learning and backpropagation neural network (BPNN) from a single mmWave radar dataset under the measurement distance and number of objects. Our experimental results showed that using dual receivers was very useful and performed well in classifying humans and animals, with an average accuracy rate of 99% in the classification of 2 and 3 classes, 83% in 6 classes, and 68% in 10 classes. The performance showed an excellent result considering that this system's application avoids false alarms in human rescue applications during post-disaster periods and exceeds the accuracy of several previous studies.
KW - Deep learning
KW - dual-receiver
KW - human and animal classification
KW - mmWave radar
KW - synthetic 2D Tensor
UR - http://www.scopus.com/inward/record.url?scp=85166311130&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2023.3299325
DO - 10.1109/ACCESS.2023.3299325
M3 - Article
AN - SCOPUS:85166311130
SN - 2169-3536
VL - 11
SP - 80284
EP - 80296
JO - IEEE Access
JF - IEEE Access
ER -