## 1 Introduction  
In recent years, due to technological development and cost reduction, unmanned aerial vehicles (UAVs) have experienced exponential growth, making them devices that are now accessible for the personal use of any citizen. The increasing popularity of UAVs can also be attributed to their great versatility, as they offer a wide range of applications, from recreational and informal uses to professional use in tasks such as search and rescue operations, fire control, and other natural disaster operations, as described by [PERSON] et al., who analyzed the growing use of UAVs in various civilian applications and the associated challenges [1]. Also, there are more applications. For instance, [PERSON] and [PERSON] designed UAVs for Package Delivery and Network Coverage tasks [2]. Others focused on the monitoring and assessment of agriculture, such as [PERSON] and [PERSON], who developed a system using multiple UAVs with a distributed control algorithm for system performance in the agricultural environment [3]. However, this rise in the use of UAVs has also raised significant security concerns, as UAVs can be used maliciously. In recent years, the main issues that can arise concerning the use of UAVs have been analyzed. For instance, the use of UAVs for surveillance purposes can pose risks to individual and intellectual privacy. Some of the malicious uses of UAVs are the espionage of essential and famous individuals and industrial espionage against companies, as explained by [PERSON] et al., who analyzed the perceptions of forty-three security specialists on the risks and threats to privacy associated with the use of drones for surveillance [4]. Furthermore, there are other threats associated with UAV use, such as smuggling or even terrorist attacks, as indicated by [PERSON] et al., who analyzed the emerging threats of drone use in various attacks and countermeasures to prevent them [5]. For this reason, in the face of this emerging threat, the detection and neutralization of UAVs have become a priority for governments and security organizations worldwide. The detection of UAVs is a complex challenge due to the diversity in their size, shape, and modes of operation; each UAV can have a different appearance and methods of functioning and communication, making it challenging to develop a valid and effective method for detecting any type of UAV. For this reason, [PERSON] et al. provided a detailed review and critical analysis of existing techniques for the detection of unauthorized UAVs, such as acoustic detection, detection through computer vision techniques, detection based on RF signals, or the use of radar, pointing out their possible advantages and limitations [6]. Each method has its advantages and disadvantages in detection, and most involve using neural networks to identify the presence of UAVs based on the features extracted by each technique. Regarding acoustic detection, this method is based on the identification of sound characteristics produced by UAVs when they are in operation, mainly due to the propellers' movement and the motors' noise. [PERSON] et al. developed a detection algorithm using two arrays of four microphones arranged in a tetrahedral shape which could detect and locate amateur UAVs utilizing a time delay of arrival (TDOA) algorithm and a support vector machine (SVM) as a neural network [7]. Acoustic methods are instrumental in environments where UAVs are not visible, although their effectiveness can be affected by background noise and weather conditions. Radar detection involves emitting signals that bounce off the UAV and return to the receiver to estimate the UAV's distance, speed, or shape. In [8], [PERSON] et al. implemented a detection and classification method based on radar cross-section (RCS) signatures. At the same time, [PERSON] et al. developed a system to detect micro-UAVs using a frequency-modulated continuous-wave (FMCW) radar along with a constant false alarm rate (CFAR) detector with adaptive thresholding to detect the presence of UAVs [9].