## 1 Introduction  
The proliferation of small unmanned aerial systems is becoming increasingly evident in both commercial and personal markets, with these systems playing pivotal roles in various sectors, including agriculture, logistics, and construction. The integration of artificial intelligence into drones has been particularly impactful, enhancing their efficiency through autonomous flight, real-time data analysis, and route optimization. As these technologies advance, small drones are becoming more accessible to individual users due to their increasingly diverse functionalities and affordability. Consequently, the use of drones is now prevalent in various fields beyond recreational and photographic applications, including public sector operations such as environmental monitoring and disaster response, with further expansion in their scope of use anticipated [1, 2]. However, the increasing use of drones has also heightened the need for safety measures and regulations. Governments worldwide are enforcing stricter regulations on drones, such as the establishment of no-fly zones and registration systems, to address concerns over potential misuse. The malicious use of drones, such as crossing national borders or targeting military installations, poses significant risks, including difficulties in target identification, military failures, and civilian casualties. Such misuse can constitute a violation of international law and humanitarian principles, particularly in densely populated areas or restricted zones. Furthermore, drones are increasingly being exploited for illegal activities such as smuggling and terrorism [3; 4]. In addition, UAVs are playing a pivotal role in package distribution, where effective detection technologies ensure accurate delivery in urban and remote areas. As UAVs continue to be deployed in these fields, the demand for reliable detection technologies is expected to grow, making the development of efficient and robust detection systems a critical area of research. Consequently, the development of aerial surveillance systems has become essential for detecting and preventing drone-related threats. These systems aim to identify and respond promptly to potential misuse, thereby protecting civilian facilities and military installations while minimizing damage. Technological approaches to drone surveillance involve various sensor-based methods. Radar, acoustic, and vision sensors are commonly employed as primary surveillance instruments, each with its own advantages and limitations. In military and security applications, radar systems are used to detect and track the flight paths of planes. Radar can cover large areas and detect invisible objects at long distances, making it an effective surveillance tool. However, the accuracy of radar can be compromised by interference from other electromagnetic signals, and radar may struggle to detect small drones with low radar cross-sections (RCSs) [5; 6]. To address these issues, extensive research has been conducted. One such study, by [PERSON] et al. [7], proposed integrating features such as micro-Doppler effects caused by drone movements and rotations, RCS variations reflecting the size and shape of objects, and motion analysis to improve situational awareness. This fusion technology has contributed to reducing false detections and improving drone identification performance. In addition, [PERSON] et al. [8] used a linear frequency-modulated continuous wave radar that uses high-frequency bandwidths to provide high-resolution range information at short distances and reduce noise through frequency modulation, thereby enabling precise detection of small drones. In a similar vein, [PERSON] et al. [9] analyzed the Doppler signal-to-clutter ratio generated by drone movements to differentiate weak drone signals from environmental noise. This approach improved detection accuracy in complex environments. However, when small drones fly close to the ground, their low altitude can cause radar signals to scatter or be absorbed by surrounding terrain and obstacles.