Higher-order tensor independent component analysis to realize 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). 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.

Complex-valued reservoir computing for aspect classification and slope-angle estimation with low computational cost and high resolution in interferometric synthetic aperture radar

Synthetic aperture radar (SAR) is widely used for ground surface classification since it utilizes information on vegetation and soil unavailable in optical observation. Image classification often employs convolutional neural networks. However, they have serious problems such as long learning time and resolution degradation in their convolution and pooling processes. In this paper, we propose complex-valued reservoir computing (CVRC) to deal with complex-valued images in interferometric SAR (InSAR). We classify InSAR image data by using CVRC successfully with a higher resolution and a lower computational cost, i.e., one-hundredth learning time and one-fifth classification time, than convolutional neural networks. We also conduct experiments on slope angle estimation. CVRC is found applicable to quantitative tasks dealing with continuous values as well as discrete classification tasks with a higher accuracy.