Higher-Order Tensor Independent Component Analysis for MIMO Remote Sensing of Respiration and Heartbeat Signals

This paper proposes a novel method of independent component analysis (ICA), which we name higher-order tensor ICA (HOT-ICA). We newly develop a robust microwave multiple-input multiple-output (MIMO) radar system, in which HOT-ICA performs separation of multiple-target signals to detect respiration and heartbeat. In comparison with millimeter waves, microwaves spread wider with diffraction and propagate even in an environment with obstacles to reach targets. However, it often requires more powerful signal separation because of its lower resolution. HOT-ICA realizes high robustness in self-organization of a separation tensor by utilizing channel information, i.e., the information of physical-measurement circumstances concerning, e.g., which transmitting/receiving antennas are used. In numerical and living-human experiments, our HOT-ICA system effectively separates the bio-signals successfully even in an obstacle-affecting environment, which has been a difficult task. The results demonstrate the significance of HOT-ICA in remote sensing. It fully utilizes the high dimensionality of the separation tensor by keeping the tensor structure unchanged to take advantage of the measurement-circumstances information.