Ki-Bong Kang

The non-contact respiration rate measurement (nRRM) method allows a system to monitor the breathing patterns of an individual without physical contact, which is crucial for regular health monitoring. Current nRRM approaches primarily depend on detecting minor variations in RGB profiles reflected from a camera to remotely extract respiration signals. However, these methods require continuous pixel-level tracking, which restricts their use on individuals in quasi-stationary sitting positions. To address this limitation, we propose a radio-frequency (RF)-Vital model, which leverages RF signals to extend the applicability of nRRM methods to individuals who exhibit global motions (GMs) and even walk around. The core idea of the RF-Vital model lies in the unique characteristics of RF signals: the RF signals received from a moving individual capture both their respiratory motions (RMs) and GMs through linear superposition, while simultaneously providing the reflections of GM alone. To fully utilize such unique properties, we introduce a new RF modality that allows stable inclusion of micro-level respiration signatures, even when GMs are present. Additionally, we optimize the RF-Vital model using a novel multi-task adversarial learning framework combined with a new loss function, which facilitates the direct mapping of the desired RMs as well as the self-supervised removal of GMs, thereby effectively filtering out RMs from mixtures of GMs and RMs. The proposed RF-vital model was evaluated using newly published datasets. It demonstrated state-of-the-art performance in static conditions and achieved the significant milestone of enabling nRRM under moving conditions.

한국전자파학회논문지 (The Journal of Korean Institute of Electromagnetic Engineering and Science)

한국전자파학회논문지 (The Journal of Korean Institute of Electromagnetic Engineering and Science)

In this paper, we introduce an improved algorithm for practical inverse synthetic aperture radar (ISAR) imaging, by {\color{black}utilizing} the advantages of prominent point processing (PPP) and integrated cubic phase function (ICPF) in an efficient way. {\color{black}The} proposed ICPF-PPP algorithm achieves a significant improvement compared to the conventional range-Doppler processing for ISAR imaging of a nonuniformly rotating target in terms of both image quality and computational efficiency. The experimental results using real data sets show that the proposed autofocus chain is highly efficient in forming ISAR images of a nonuniformly rotating target.

In this article, we propose a new signal-decomposition algorithm composed of three steps: principal components analysis to yield high-resolution range profiles with improved signal-to-noise ratio; estimation of whitening and mixing matrices using independent component analysis in distributed radar network; and signal decomposition to obtain inverse synthetic aperture radar (ISAR) images that correspond to rigid body and fast-rotating parts by using estimated two matrices regardless of complicated range migration. The proposed method efficiently removes image blur caused by micro-Doppler (MD) signals of rotating parts and reduces the sensitivity to noise. In simulations, our proposed method could perform accurate and robust removal of the MD signatures to obtain a focused ISAR image of an aircraft with fast-rotating parts.

Most of the investigations on the micro-Doppler (MD) effect caused by a small unmanned aerial vehicle (UAV) have been conducted using joint time–frequency (JTF) images rather than the Doppler spectrum. On the other hand, several researchers utilized the Doppler spectrum instead of JTF images to observe the MD signature of a small UAV, and found the relationship between the spectral distribution of a small UAV and its physical specifications. However, the studies using the Doppler spectrum still lack concrete and theoretical foundations of the MD effects of a small UAV, focusing mainly on phenomena identified by measurement data. In this article, we establish the theoretical foundation connecting the MD signatures and motion dynamics of small UAVs based on the Doppler spectrum, and analyze their spectral distribution using simulations and measured data. In addition, experimental analysis is conducted using the data measured from various types of small UAVs considering the translational motion and aspect change. In contrast to already existing investigations, we completely explain and predict the changes on the Doppler spectrum relative to the physical specifications of a small UAV (e.g., blade length and rotor rotation rate). In particular, we show that the Doppler spectrum, compared to the JTF images, is a considerably simple and useful tool for analyzing the MD effects of small flying UAVs. The analysis results reveal that the MD features obtained from the measured echoes of small UAVs have considerable potential for detection and classification of small UAVs.

한국전자파학회논문지 (The Journal of Korean Institute of Electromagnetic Engineering and Science)

한국전자파학회논문지 (The Journal of Korean Institute of Electromagnetic Engineering and Science)