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This paper proposes a novel method of independent component analysis (ICA),which we name higher-order tensor ICA (HOT-ICA). HOT-ICA is a tensor ICA thatmakes effective use of the signal categories represented by the axes of aseparating tensor. Conventional tensor ICAs, such as multilinear ICA (MICA)based on Tucker decomposition, do not fully utilize the high dimensionality oftensors because the matricization in MICA nullifies the tensor axialcategorization. In this paper, we deal with multiple-target signal separationin a multiple-input multiple-output (MIMO) radar system to detect respirationand heartbeat. HOT-ICA realizes high robustness in learning by incorporatingpath information, i.e., the physical-measurement categories on whichtransmitting/receiving antennas were used. In numerical-physical experiments,our HOT-ICA system effectively separate the bio-signals successfully even in anobstacle-affecting environment, which is usually a difficult task. The resultsdemonstrate the significance of the HOT-ICA, which keeps the tensorcategorization unchanged for full utilization of the high-dimensionality of theseparation tensor.

Higher-order tensor independent component analysis to realize MIMO remote sensing of respiration and heartbeat signals