## 1 Introduction  
With the advent of commercial Unmanned Aerial Vehicles (UAVs), drone detection has become more of a necessity in recent years. The remote operative capabilities of UAVs pose threats to security, privacy, and other potential legal issues. Additionally, the number of applications for UAVs has also increased, spanning recreational, commercial, and military uses. Nowadays, state-of-the-art drones even possess wireless networking capabilities. Most modern drones are equipped with video cameras and can even deliver small packages. The relatively cheap costs of UAVs paired with remote operability allows anyone to become a pilot with low risk to their health. If used incorrectly, many hazards may be overlooked and the degree of risk involved increases. Although guidelines have been created by the FAA for remote pilots, the ease of access to these systems can prove to be dangerous. People with malicious intentions pay no heed to the warnings and rules created by the FAA. For example, in 2018, hundreds of flights were canceled at Gatwick Airport due to a drone sighting close to the runway, causing flight delays of two days [1]. Methods for detecting drones in the past include acoustic, optical, and radar methods. RCS-based radar methods, such as high-resolution range profiles and synthetic aperture radar, can be difficult when considering the close RCS values of birds and drones. To avoid this, we investigated the benefits of using micro-Doppler signatures from targets using a CW radar system. In addition to detecting the flying target, it is possible to discern the type of target (bird or drone) based on their micro-motions such as propeller blade spinning or bird wing flapping. Using the short-time Fourier transform (STFT), we generated time-frequency plots called spectrograms to observe the individual micro-Doppler characteristics. The classification efforts involved the overall Doppler bandwidth spread, base velocity, and cycle frequency. Fortunately, the Federal Aviation Administration (FAA) created the Part 107 regulations for commercial small UAVs in June 2016. These regulations require UAV pilots to register their UAVs if they weigh over 250 g [2]. In addition, the pilot must carry a small UAV operator license, which can be obtained after passing the Part 107 exam. This exam covers a wide range of information regarding UAV safety. Some topics include UAV limitations, flight operations, airspace classification and operating requirements, flight restrictions, weather awareness, emergency procedures, radio communication procedures, and more. State-of-the-art techniques for detecting drones include acoustic [3, 4], optical camera [5, 6], lidar [7, 8], and radar [9, 10, 11]. Despite the wide range of sensing practices, there are several trade-offs associated with each method. In Table 1, the advantages and disadvantages of sensing drones with acoustics, cameras, lidar, and radar sensors are listed. Some of the advantages of acoustics include no line-of-sight (LOS) requirements, effective performance in low-visibility conditions, and relatively low implementation costs. However, in noisy conditions, such as windy gusts, microphones will not be able to distinguish or detect drones. Using cameras for detecting targets is advantageous due to their high accuracy and easy operability. Although it is easy to detect flying targets, it is difficult to classify the subject further. There is also a high cost associated with using and maintaining cameras. Lidar sensing is an accurate method for detecting targets, but the cons greatly outweigh the pros. They are expensive, have a narrow LOS, and are heavily weather-dependent. Finally, radar sensing provides an effective method for detection due to the low cost of implementation and robustness of operability. The biggest weakness of using radar is that such targets tend to have a low radar cross section (RCS) or visibility, which can hinder their ability to detect them.