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.

In the case of a stepped-frequency waveform (SFW) inverse synthetic aperture radar (ISAR) system, the translational motion (TM) of a target can be usually divided into two parts: 1) target motion within a pulse repetition interval, called the inter-pulse translational motion (IPTM) and 2) target motion between bursts, called the inter-burst translational motion (IBTM). The former induces severe blurring in the ISAR images as well as range-compressed data (i.e., range profile), and the latter also causes dramatic degradation of the ISAR image quality. In this paper, a novel framework for high-resolution gapped SFW (GSFW) ISAR imaging of high-speed maneuvering target is proposed. The main novelty of the proposed method is twofold: 1) accurate TM parameter estimation in conjunction with a compressive sensing theory using a newly devised cost function and particle swarm optimization and 2) compensation for both the IPTM and IBTM phase errors simultaneously even with the GSFW data set. Simulation results using ideal point scatterers show that the proposed method is capable of precise reconstruction of ISAR image and accurate TM parameter estimation. Experimental results using real measured data verify the robustness and the effectiveness of the proposed method.

Ground moving target imaging (GMTIm) is considered one of the most important applications of synthetic aperture radar (SAR). Phase modulation from a moving target’s higher-order movements severely degrades the focusing quality of SAR images, because the conventional SAR-GMTIm algorithm assumes a constant target velocity in high-resolution GMTIm with single channel SAR. To solve this problem, a novel SAR-GMTIm algorithm in the compressive sensing (CS) framework is proposed to obtain high-resolution SAR images with highly focused responses and accurate re-location. After taking direct action on data in the defocused region of interest (ROI) from the entire image scene, CS theory is used to decompose an SAR-GMTIm signal into a set of the polynomial basis functions to remove the various phase errors related to higher-order movements. A modified orthogonal matching pursuit (MOMP)-type basis function-searching scheme is adopted to determine the motion parameter and reconstruct the sensing dictionary matrix. We can generate a refocused image of SAR-GMTIm from the complete SAR-GMTIm signal recovered using the proposed method. Finally, simulated and real measured SAR data are used to validate the effectiveness and superiority of the proposed method for SAR-GMTIm.

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.