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, a UAV's range-Doppler signature depends on its movement mechanism. This is why a classifier's accuracy would be degraded if the effect of the mechanical control system of UAVs wasn't taken into consideration, which may lead to a non-unique signature of a UAV while in-flight. In this paper, a full-wave electromagnetic CAD tool is used to investigate the effect of the control systems of two quadcopters, a hexacopter, and a helicopter UAVs on their range-Doppler signatures. A Mechanical Control-Based Machine Learning (MCML) algorithm is introduced to classify the four UAVs. Different Machine Learning (ML) algorithms were applied to the generated datasets that considered the mechanical control information of UAVs. The Convolutional Neural Networks (CNN) algorithms provided robust performance reaching an accuracy of higher than 90%.

The detection and classification of Unmanned Aerial Vehicles (UAVs) are disturbing challenges within contemporary radar systems, wherein the physical characteristics of UAVs, including their size and Radar Cross Section (RCS), exert a substantial influence on radar’s detection capabilities. Smaller UAVs, characterized by reduced RCS values, often escape radar detection. In response to these challenges, this study introduces an efficient radar signal processing technique based on beamforming, termed Range-Doppler Integration while Steering (RDIwS). RDIwS significantly enhances the Signal-to-Noise Ratio (SNR) associated with UAVs, resulting in an increased detection probability and classification accuracy for these UAVs. Importantly, the RDIwS approach demonstrates superior performance to traditional Multiple-Input Multiple-Output (MIMO) methods and established beamforming-based techniques, showcasing its potential to significantly advance UAV detection and classification across various operational contexts. For four targets located at different angles and distances scenario, and at -30 dB SNR and false alarm probability of 10 ^-5 , the RDIwS beamforming-based method achieved a detection probability of 75% compared to 5% for steering-only beamforming, and no detection for MIMO radar case.

In the present investigation, the impacts of antenna field of view (FOV) on the accuracy of machine learning (ML) models utilized for the classification of 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 the relative angle between the UAVs and the antenna was kept at 0 $^{\circ}$ , yielding an accuracy of 96.5%. In contrast to the common practice, the trained ML model was subsequently subjected to testing at varied relative angles, spanning from 20 $^{\circ}$ to 90 $^{\circ}$ . Observational outcomes delineate a decrease in ML classification accuracy with an increase in the relative angle between UAVs and the antenna. Next, this study investigates the impact of utilizing multiple-input-multiple-output (MIMO) radar system for classification. The results indicate enhancement of UAV detection and classification efficacy when contrasted with a single-input-single-output (SISO) radar system, at 80 $^{\circ}$ , an accuracy of 60% for the MIMO antenna compared to 13% for the SISO antenna. To corroborate the results obtained by utilizing full-wave electromagnetic simulation software, a series of experimental laboratory measurements were undertaken. The empirical measurements were found to yield comparable ML accuracy, at 80 $^{\circ}$ , an accuracy of 38% for the MIMO antenna compared to 1% for the SISO antenna. This work underscores the paramount versatility achieved through invoking full-wave electromagnetic simulators alongside ML algorithms and the respective possible impact on standard practices when it comes to developing next-generation MIMO radar systems for UAV classification.

Unmanned Aerial Vehicles (UAVs) pose significant security challenges due to their widespread use in various malicious activities, including terrorist attacks and wartime operations where explosives are attached to them. Conventional radar-based detection and classification methods often rely on range-Doppler signatures, which may lead to misclassification, especially in identifying UAVs carrying explosive payloads. To address this challenge, this paper proposes an ISAR-based classification algorithm. To develop and validate the proposed algorithm, a quadcopter is modeled using radar digital twins to generate comprehensive datasets. Initially, a Convolutional Neural Network (CNN) classifier is trained on a dataset comprising range-Doppler information, aiming to distinguish between a commercial quadcopter and the same quadcopter when it's modified to carry explosives. However, the model fails to accurately classify instances where the quadcopter is carrying explosives based solely on range-Doppler data. Subsequently, the CNN model is retrained using a dataset containing ISAR images for both scenarios. When applied to a separate dataset featuring ISAR images of a quadcopter carrying explosives, the model demonstrates improved accuracy in classification. Real-measurements further validate these findings, confirming the effectiveness of the proposed ISAR-based classification approach in enhancing radar security against UAV-borne threats. This research presents valuable insights for the future development of robust countermeasures to address the evolving challenges posed by UAVs in security-sensitive environments.

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 and classification methodologies, radar technology stands as a cornerstone due to its versatility and reliability. This paper presents a comprehensive primer written specifically for researchers starting on investigations into UAV detection and classification, with a distinct emphasis on the integration of full-wave electromagnetic computer-aided design (EM CAD) tools. Commencing with an elucidation of radar’s pivotal role within the UAV detection paradigm, this primer systematically navigates through fundamental Frequency-Modulated Continuous-Wave (FMCW) radar principles, elucidating their intricate interplay with UAV characteristics and signatures. Methodologies pertaining to signal processing, detection, and tracking are examined, with particular emphasis placed on the pivotal role of full-wave EM CAD tools in system design and optimization. Through an exposition of relevant case studies and applications, this paper underscores successful implementations of radar-based UAV detection and classification systems while elucidating encountered challenges and insights obtained. Anticipating future trajectories, the paper contemplates emerging trends and potential research directions, accentuating the indispensable nature of full-wave EM CAD tools in propelling radar techniques forward. In essence, this primer serves as an indispensable roadmap, empowering researchers to navigate the complex terrain of radar-based UAV detection and classification, thereby fostering advancements in aerial surveillance and security systems.

Unmanned aerial vehicles (UAVs), commonly known as drones, have emerged as versatile tools with a wide range of applications across various fields. UAV technology has rapidly evolved, leading to its adoption in civilian and commercial sectors. UAVs offer numerous benefits, including cost-effectiveness, flexibility, and accessibility, making them invaluable assets in various industries such as precision agriculture, environmental monitoring, disaster response, and infrastructure inspection. They provide real-time aerial data and imagery, enabling farmers to optimize crop management, conservationists to monitor ecosystems, and emergency responders to assess disaster-affected areas [1] . Additionally, UAVs play a crucial role in contactless vital sign monitoring [2] and in entertainment and filmmaking, revolutionizing aerial cinematography and photography [3] . With their versatility and accessibility, UAVs continue to drive innovation and efficiency in industries worldwide. However, the increased accessibility and availability of UAVs have also raised concerns about their potential use in illegal activities and terrorist attacks [4] , [5] , [6] , [7] . This highlights the importance of identifying and classifying drones for safety and security purposes.