TY - JOUR
T1 - SimHumalator
T2 - An open-source end-to-end radar simulator for human activity recognition
AU - Vishwakarma, Shelly
AU - Li, Wenda
AU - Tang, Chong
AU - Woodbridge, Karl
AU - Adve, Raviraj
AU - Chetty, Kevin
PY - 2022/3/1
Y1 - 2022/3/1
N2 - Radio-frequency-based noncooperative monitoring of humans has numerous applications ranging from law enforcement to ubiquitous sensing applications such as ambient assisted living and biomedical applications for nonintrusively monitoring patients. Large training datasets, almost unlimited memory capacity, and ever-increasing processing speeds of computers could drive forward the data-driven deep-learning-focused research in the abovementioned applications. However, generating and labeling large volumes of high-quality, diverse radar datasets is an onerous task. Furthermore, unlike the fields of vision and image processing, the radar community has limited access to databases that contain large volumes of experimental data. Therefore, in this article, we present an open-source motion capture data-driven simulation tool, SimHumalator, that can generate large volumes of human micro-Doppler radar data in passive WiFi scenarios. The simulator integrates IEEE 802.11 WiFi Standards (IEEE 802.11 g, n, and ad) compliant transmissions with the human animation data to generate the micro-Doppler features that incorporate the diversity of human motion characteristics and the sensor parameters. The simulated signatures have been validated with experimental data gathered using an in-house-built hardware prototype. This article describes simulation methodology in detail and provides case studies on the feasibility of using simulated micro-Doppler spectrograms for data augmentation tasks.
AB - Radio-frequency-based noncooperative monitoring of humans has numerous applications ranging from law enforcement to ubiquitous sensing applications such as ambient assisted living and biomedical applications for nonintrusively monitoring patients. Large training datasets, almost unlimited memory capacity, and ever-increasing processing speeds of computers could drive forward the data-driven deep-learning-focused research in the abovementioned applications. However, generating and labeling large volumes of high-quality, diverse radar datasets is an onerous task. Furthermore, unlike the fields of vision and image processing, the radar community has limited access to databases that contain large volumes of experimental data. Therefore, in this article, we present an open-source motion capture data-driven simulation tool, SimHumalator, that can generate large volumes of human micro-Doppler radar data in passive WiFi scenarios. The simulator integrates IEEE 802.11 WiFi Standards (IEEE 802.11 g, n, and ad) compliant transmissions with the human animation data to generate the micro-Doppler features that incorporate the diversity of human motion characteristics and the sensor parameters. The simulated signatures have been validated with experimental data gathered using an in-house-built hardware prototype. This article describes simulation methodology in detail and provides case studies on the feasibility of using simulated micro-Doppler spectrograms for data augmentation tasks.
U2 - 10.1109/maes.2021.3138948
DO - 10.1109/maes.2021.3138948
M3 - Article
SN - 0885-8985
VL - 37
SP - 6
EP - 22
JO - IEEE Aerospace and Electronic Systems Magazine
JF - IEEE Aerospace and Electronic Systems Magazine
IS - 3
ER -