In this paper, we attempt to generate high resolution profile of flying drone targets. To this purpose, multi-bands radar signals are adopted to carry out inverse synthetic aperture radar (ISAR) imaging of moving drones. After theoretical analysis is performed, experimental results are acquired through field tests. demonstrate that high resolution ISAR imaging provide effective measures to detect and classify the class of multiple target drones.

To replace the field-programmable gate array semiconductors used in the defense satellite industry, we conducted a technical investigation of application-specific integrated circuit (ASIC) semiconductors. The analysis results show that ASIC semiconductors have the heat dissipation, reliability, and radiation resistance capabilities required in a space environment and excellent performance indicators related to the resolution of synthetic aperture radar payloads, such as operating frequency. Research and development of mission-customized ASIC semiconductors that fit the requirements for NSP(new-space paradigm) should be preceded to expanse bases of the space industry, national security capabilities for next-generation space deveolpment also to secure competitiveness of technology and domestic supply chain as space parts itselfs.

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

Anti-drone has been one of major research topics in recent years and among others, the radar detection is considered as the most promising technique. However, the use of conventional radar detection may not be effective due to the low level radar cross sections (RCS) of the commercial drones. In this paper, inverse synthetic aperture (ISAR) technique has been employed to implement a drone detection system in urban area. To this purpose, a pulse radar system is set up on the ground to track flying drones. A full aperture coherent processing technique is employed to increase the observation time and achieve sufficient resolutions to detect and classify the structure of the multi-rotor platform. We present the results and verify the feasibility of the proposed technique is verified by producing high resolution ISAR images through experimental field test in urban area.

Increasing concerns over the use of drones for military or hostile purposes have intensified the demand for effective detection and countermeasures. Although conventional acoustic, thermal, and optical sensors have been extensively explored, active radar systems are particularly promising due to their allweather capability and simultaneous range-velocity measurements. However, the low radar cross-section (RCS) of small drones remains a critical obstacle for reliable detection and identification. Although recent advancements in ultra-wideband (UWB) and high-power frequency-modulated continuous wave (FMCW) radars have improved sensitivity, they often fail to distinguish drones from other aerial objects, leading to false alarms and classification errors. A primary source of these errors is the high-frequency micro-Doppler signature generated by the rapidly rotating propellers, which corrupts the phase information required for high-resolution imaging. To address this problem, we propose a method to isolate and suppress these micro-Doppler artifacts, thereby enabling robust phase compensation for inverse synthetic aperture radar (ISAR) processing. The proposed processing framework integrates three key stages: (A) a Chebyshev polynomial-based filter to suppress propeller-induced spectral components, (B) spectral-domain averaging to enhance the signal from the drone body, and (C) the phase gradient autofocus (PGA) algorithm to correct the residual phase errors. The proposed methodology was analyzed theoretically using simplified target models and subsequently validated using experimental data obtained from real-world drone flights. By improving the clarity and contrast of drone ISAR images, the proposed method provides a practical processing framework for mitigating micro-Doppler-induced degradation in maneuvering drone scenarios.

This study presents an effective moving target indication (MTI) algorithm using single-channel Synthetic Aperture Radar (SAR) Single Look Complex (SLC) data, which is able to consecutively perform moving target detection and velocity estimation. Using different ICEYE, Umbra and Unmanned Aerial Vehicle (UAV) SAR images, this study generates two sub-aperture SAR image pairs from each SLC image and measures sub-aperture coherence. While estimating coherence, azimuth misregistration is added in order to better identify moving targets from background clutters. Moreover, moving target velocity is consecutively estimated using target motion SAR phase refocusing, which both effectively removes motion-induced SAR phase distortion and estimates its corresponding target velocity. When evaluated using real-time vessel in-situ data, the proposed MTI algorithm detects moving vessels with 87.1 % of overall accuracy and estimates their velocities with 0.59 m/s of average offset. Detailed applications of the proposed single-channel SAR MTI algorithm on (i) identifying over-speeding vessels, (ii) vehicle traffic monitoring and (iii) aircraft motion analysis are presented, demonstrating the effectiveness of the MTI algorithm on military target reconnaissance and dark vessel monitoring.