TY - GEN
T1 - End-to-End Through-Wall Human Localization Network Using Raw Radar ADC Data
AU - Wang, Wei
AU - Du, Naike
AU - Guo, Yuchao
AU - Sun, Chao
AU - Liu, Jingyang
AU - Song, Rencheng
AU - Ye, Xiuzhu
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - The radar signal processing algorithm is one of the core processes in through-wall radar human localization technology. However, traditional algorithms often struggle to adaptively handle low signal-to-noise ratio (SNR) echo signals in challenging and dynamic through-wall application environments. These environments, characterized by complex and varying conditions such as multi-path reflections and signal attenuation through different wall materials, exacerbate the difficulties in accurately localizing human targets. In this paper, we introduce a novel end-to-end through-wall radar human localization network, which directly processes raw radar Analog-to-Digital Converter (ADC) signals without any preprocessing. By bypassing traditional signal processing stages, our approach leverages the raw data to capture more comprehensive information from the environment, allowing for more robust and adaptable localization. To achieve this, we replace the conventional radar signal processing flow with the proposed DFT-based adaptive feature extraction (DAFE) module. This module employs learnable parameterized 3D complex convolution layers, which are specifically designed to extract superior feature representations from ADC signals. The ability to learn implicit features from raw data enables our network to overcome the limitations of traditional preprocessing methods, which often fail to adequately capture the nuances of low-SNR conditions. We rigorously trained and validated our proposed method on extensive data collected in real-world through-wall scenarios. The experimental results confirm the effectiveness and superiority of our approach, demonstrating its potential to significantly improve human localization accuracy in challenging environments.
AB - The radar signal processing algorithm is one of the core processes in through-wall radar human localization technology. However, traditional algorithms often struggle to adaptively handle low signal-to-noise ratio (SNR) echo signals in challenging and dynamic through-wall application environments. These environments, characterized by complex and varying conditions such as multi-path reflections and signal attenuation through different wall materials, exacerbate the difficulties in accurately localizing human targets. In this paper, we introduce a novel end-to-end through-wall radar human localization network, which directly processes raw radar Analog-to-Digital Converter (ADC) signals without any preprocessing. By bypassing traditional signal processing stages, our approach leverages the raw data to capture more comprehensive information from the environment, allowing for more robust and adaptable localization. To achieve this, we replace the conventional radar signal processing flow with the proposed DFT-based adaptive feature extraction (DAFE) module. This module employs learnable parameterized 3D complex convolution layers, which are specifically designed to extract superior feature representations from ADC signals. The ability to learn implicit features from raw data enables our network to overcome the limitations of traditional preprocessing methods, which often fail to adequately capture the nuances of low-SNR conditions. We rigorously trained and validated our proposed method on extensive data collected in real-world through-wall scenarios. The experimental results confirm the effectiveness and superiority of our approach, demonstrating its potential to significantly improve human localization accuracy in challenging environments.
KW - end-to-end neural network
KW - human localization
KW - raw ADC data
KW - Through-wall radar
UR - http://www.scopus.com/inward/record.url?scp=85213363746&partnerID=8YFLogxK
U2 - 10.1109/IST63414.2024.10759150
DO - 10.1109/IST63414.2024.10759150
M3 - Conference contribution
AN - SCOPUS:85213363746
T3 - IST 2024 - IEEE International Conference on Imaging Systems and Techniques, Proceedings
BT - IST 2024 - IEEE International Conference on Imaging Systems and Techniques, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE International Conference on Imaging Systems and Techniques, IST 2024
Y2 - 14 October 2024 through 16 October 2024
ER -