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.

Conventional radar-based people counting systems are designed mainly for dense spatial distributions in a small region of interest (ROI). Therefore, a system with only conventional energy-based features, which are effective for a small ROI with a limited spatial distribution of individuals, generally fails to cope with the diverse and complex spatial distributions that arise from the freer movements of individuals as the ROI widens. To address this problem, a novel approach that achieves robust people counting in both wide and small ROIs is presented in this study. The proposed technique incorporates modified CLEAN-based features in the range domain and energy-based features in the frequency domain to efficiently address both dense and dispersed distributions of individuals. Subsequently, principal component analysis and an appropriate normalization of the proposed features are performed for improving the people counting system further. Based on several experiments in practical environments with wide ROIs and severe multipath effects, we observed that the proposed approach yields significantly improved performance compared with traditional people counting systems.

Synthetic aperture radar (SAR) systems, which operate under a slant-viewing geometry, inevitably entail shadow regions in the resulting radar image. Such shadow profiles contain backprojected signatures of an object’s configuration as with target profiles; however, they are rarely utilized in current SAR-based recognition techniques. A major challenge in leveraging shadow information together lies in the intrinsic limitation of current single-pathway approaches, in which the target and shadow cannot be addressed simultaneously because of their incompatible domain properties. Hence, we herein propose novel solutions that enable the successful fusion of target and shadow regions within SAR for the first time. First, we devise new image preprocessing techniques specifically customized for shadows to compensate for their unique domain characteristics, which are distinct from the target. Second, we introduce a parallelized SAR processing mechanism such that a network can independently extract features oriented toward each conflicting modality. Third, adaptive fusion strategies are proposed for the optimal integration of features from each region while considering their relative significance layer by layer. Extensive experiments on public benchmark datasets demonstrate that the proposed framework allows a network to effectively employ shadow signatures and targets, thereby outperforming previous methods significantly for all setups.

With the development of deep learning (DL) frameworks in the field of pattern recognition, DL-based algorithms have outperformed handcrafted feature (HF)-based ones in various applications. However, there still exist several challenges in applying the DL framework to a radar-based people counting (RPC) task: The powerful representation capacity of a deep neural network (DNN) learns not only the desired human-induced components but also unwanted nuisance factors, and available data for RPC is usually insufficient to train a huge-sized DNN, leading to an increased possibility of overfitting. To tackle this problem, we propose novel solutions for the successful application of the DL framework to the RPC task from various perspectives. First, we newly formulate the preprocessing pipelines to transform the raw received radar echoes into a better-matched form for a DNN. Second, we devise a novel backbone architecture that reflects the spatiotemporal characteristics of the radar signals, while relieving the burden on training through a parameter efficient design. Finally, an unsupervised pretraining process and a newly defined loss function are proposed for further stabilized network convergence. Several experimental results using real measured data show that the proposed scheme enables an effective utilization of DL for RPC, achieving a significant performance improvement compared to conventional RPC methods.

Impulse-radio ultra-wideband (IR-UWB) radar devices can be used to monitor vital signs, such as respiratory and cardiac rates. For this purpose, the phase of an echo signal received from the chest or back of a human is usually used; subsequently, respiratory rate estimation (RRE) and cardiac rate estimation (CRE) can be achieved by estimating two fundamental frequencies corresponding to the respiratory and cardiac rates, respectively. However, the interference caused by undesirable terms would inevitably be induced in an echo signal; thus, the cardiac rate information contained in the phase of the received signal is often smeared and destroyed. Among several sources of interference such as high-order harmonics of respiratory rate, random body movement of an individual, and system noise, the body movement of an individual is the most critical for reliable CRE, making it a very challenging task. To address this problem, we herein propose a new framework comprising four steps: 1) RRE via differentiation and spectrum analysis for an unwrapped phase of received signal, 2) coarse estimation of cardiac rate candidates, 3) selection of three promising candidates for the desired cardiac rate based on a histogram of several candidates, and 4) determination of the desired cardiac rate via a fuzzy logic rule-based method. In experiments using 7.29 GHz IR-UWB radar hardware, we observed that our proposed framework is capable of performing accurate and robust real-time CRE even in the presence of body movement.