RaLU-Net: Deep Unfolded Radar Localization of Humans for Precise Multi-Person Non-Contact Vital Signs Monitoring

The rising demand for multi-person non-contact vital signs monitoring (NCVSM) in healthcare highlights the potential of radar technology, especially in cluttered environments. Single-input multiple-output frequency-modulated continuous- wave (FMCW) radars enable multi-object localization, which is crucial for multi-person NCVSM. However, detecting and positioning humans in crowded scenarios is challenging due to resolution limitations. This work first proposes an iterative method for multi-human localization exploiting joint sparsity and cardiopulmonary properties. Then, the method is unfolded into a neural network that preserves the data’s unique features to further enhance accuracy and reduce computational cost. Simulations containing real-world data show the network’s superior performance in detecting and positioning multiple adjacent humans, outperforming existing techniques via key metrics. This approach can be integrated into advanced NCVSM systems where accuracy and computational efficiency are paramount.