Range-Doppler images are widely used to classify different types of Unmanned Air Vehicles (UAVs) because each UAV has a unique range-Doppler signature. However, UAV's signature depends on its movement mechanism. This is why classifier's accuracy would be degraded if the effect mechanical control system UAVs wasn't taken into consideration, which may lead non-unique while in-flight. In this paper, full-wave electromagnetic CAD tool investigate systems two quadcopters, hexacopter, and...

The detection and classification of Unmanned Aerial Vehicles (UAVs) are disturbing challenges within contemporary radar systems, wherein the physical characteristics UAVs, including their size Radar Cross Section (RCS), exert a substantial influence on radar's capabilities. Smaller characterized by reduced RCS values, often escape detection. In response to these challenges, this study introduces an efficient signal processing technique based beamforming, termed Range-Doppler Integration...

In the present investigation, impacts of antenna field view (FOV) on accuracy machine learning (ML) models utilized for classification various unmanned air vehicle (UAV) types were systematically explored using full-wave electromagnetic simulation software. Initially, similar to many state-of-the-art works, an ML algorithm was meticulously trained under a particular condition where relative angle between UAVs and kept at 0 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML"...

The potential dangers of the unauthorized use Unmanned Air Vehicles (UAVs) have made remote detection and classification crucial. Radar systems are preferred as they operate in all weather situations during any time. Identification UAVs threats is aided by knowledge number detected their directions. In this paper, a digital twin Multiple-Input Multiple-Output (MIMO) radar used to detect CAD replicas various UAVs, enable simultaneous classification. Rather than resorting complex measurement...

Using micro-doppler signatures is an effective way to classify different types of UAVs, as well other targets like birds. To generate these datasets, researchers used conduct campaigns for radar drones’ measurements. However, measurements are limited the available drones, parameters, targets’ range, and environment taken in. In this paper, a new method simulating datasets introduced, uses full-wave electromagnetic CAD tools. Radar simulations five real drones presented. method, can simulate...

Detection and classification of Unmanned Air Vehicles (UAV s) at a distance have become important because the potential threats illegal usage them. Radar systems are preferred for UAV s detection their advantages over other UAVs systems. In this paper, an investigation effect antenna Field View (FoV) on Machine Learning (ML) accuracy is conducted. A full-wave Electromagnetic (EM) CAD tool used to generate required datasets investigation. Five were in work, fixed-wing, helicopter, two...

In this study, the efficacy of range-Doppler imaging is explored for detection and classification Unmanned Air Vehicles (UAVs), with attention to radar system's operating frequency bandwidth. The investigation employs full-wave Electromagnetic (EM) CAD software scrutinize influence varied radars, spanning different bands, on precision images a rotating blade. Notably, mmWave distinguished by their expansive bandwidth, demonstrate superior accuracy compared other examined systems. Building...

Unmanned Aerial Vehicles (UAVs) represent a rapidly increasing technology with profound implications for various domains, including surveillance, security, and commercial applications. Among the number of detection classification methodologies, radar stands as cornerstone due to its versatility reliability. This paper presents comprehensive primer written specifically researchers starting on investigations into UAV classification, distinct emphasis integration full-wave electromagnetic...

&lt;p&gt;Using micro-doppler signatures is an effective way to classify different types of UAVs, as well other airborne objects such birds. To generate for drones, radar measurements are needed; however, these limited the available parameters, targets’ range, and environments in which conducted. In this paper, a new method generating signature datasets introduced. The uses full-wave electromagnetic simulation software. Using method, drones’ can be generated using types, sizes, drone...

&lt;p&gt;Range-Doppler images are widely used to classify different types of UAVs because each UAV has a unique range-doppler signature. However, drone's signature depends on its movement mechanism. This is why the classifier accuracy would be degraded if effect mechanical control system wasn't taken into consideration, which may lead non-unique drone while in-flight. In this paper, full-wave electromagnetic CAD tool investigate systems quadcopter and hexacopter their signatures. A...

&lt;p&gt;Using micro-doppler signatures is an effective way to classify different types of UAVs, as well other airborne objects such birds. To generate for drones, radar measurements are needed; however, these limited the available parameters, targets’ range, and environments in which conducted. In this paper, a new method generating signature datasets introduced. The uses full-wave electromagnetic simulation software. Using method, drones’ can be generated using types, sizes, drone...

&lt;p&gt;Range-Doppler images are widely used to classify different types of UAVs because each UAV has a unique range-doppler signature. However, drone's signature depends on its movement mechanism. This is why the classifier accuracy would be degraded if effect mechanical control system wasn't taken into consideration, which may lead non-unique drone while in-flight. In this paper, full-wave electromagnetic CAD tool investigate systems quadcopter and hexacopter their signatures. A...