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

TL;DR

This paper introduces HOT-ICA, a novel tensor ICA method that leverages tensor categories for improved separation of respiration and heartbeat signals in MIMO radar systems, demonstrating robustness in challenging environments.

Contribution

The paper presents HOT-ICA, a new tensor ICA approach that fully utilizes tensor axial categorization, enhancing signal separation in MIMO radar bio-signal detection.

Findings

Effective separation of bio-signals in obstacle environments

HOT-ICA outperforms conventional tensor ICA methods

Maintains tensor categorization for high-dimensional data

Abstract

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 that makes effective use of the signal categories represented by the axes of a separating tensor. Conventional tensor ICAs, such as multilinear ICA (MICA) based on Tucker decomposition, do not fully utilize the high dimensionality of tensors because the matricization in MICA nullifies the tensor axial categorization. In this paper, we deal with multiple-target signal separation in a multiple-input multiple-output (MIMO) radar system to detect respiration and heartbeat. HOT-ICA realizes high robustness in learning by incorporating path information, i.e., the physical-measurement categories on which transmitting/receiving antennas were used. In numerical-physical experiments, our HOT-ICA system effectively separate the bio-signals successfully even in an obstacle-affecting environment, which is usually a difficult task. The results demonstrate the significance of the HOT-ICA, which keeps the tensor categorization unchanged for full utilization of the high-dimensionality of the separation tensor.