我所郏东耀老师课题组完成的论文《A multi-target physiological signal detection method for UWB radar based on Kalman tracking and dual-branch network》于2025-11在《Machine Vision and Applications》正式发表。论文摘要如下：
Abstract: This paper introduces a novel non-contact physiological signal detection method using Ultra-Wideband (UWB) radar, integrating Kalman tracking and time-frequency deep learning to mitigate range gate jitter and noise in multi-target scenarios. A dynamic threshold adaptive peak detection method, combined with multi-Kalman filtering, ensures robust target acquisition. A deep learning model with frequency domain enhancement improves respiratory rate (RR) and heart rate (HR) detection accuracy. The framework employs multi-task learning, optimizing RR regression, HR classification, enhancing both interpretability and robustness. Experiments at 3 ms show a range detection error below 0.05 ms, with root mean square errors of 0.009 Hz and 0.04 Hz for RR and RR, respectively, over 60% more accurate than traditional fast Fourier transform methods. The approach is computationally efficient and highly applicable in telemedicine, smart home monitoring, and related fields.
引用信息：Ziqi Li, Dongyao Jia, Zihao He, Nengkai Wu. A multi-target physiological signal detection method for UWB radar based on Kalman tracking and dual-branch network. Machine Vision and Applications, 2025-11-01. DOI: 10.1007/s00138-025-01754-0