## 2 Related Works  
Several techniques have been developed to detect and recognize drones, including visual [24], audio [25], WiFi [26; 27], infrared camera [28], and radar [29]. Drone audio detection relies on detecting propeller sounds and separating them from the background noise. A high-resolution daylight camera and a low-resolution infrared camera were used for visual assessment [30]. Good weather conditions and a reasonable distance between drone targets and cameras are still required for visual assessment. Fixed visual detection methods cannot estimate the continuance track of drones. Infrared cameras detect heat sources on drones such as batteries, motors, and motor driver boards. Airborne vehicles can be detected more easily by mmWave radar, which has been the most-popular form of detection for military troops for a long time. However, traditional military radars are designed to recognize large targets and have trouble detecting small drones. Furthermore, the target discrimination may not be straightforward. The extremely short wavelength of mmWave radar systems makes them highly sensitive to the small features of drones, providing very precise velocity resolution, and allowing them to penetrate certain materials to detect concealed hazardous targets [30]. This subsection discusses various recent drone classifications using machine learning and deep learning models. The radar cross-section (RCS) signatures of different drones with different frequency levels have been discussed in several studies, including [2; 31]. The method proposed in [2] relied on converting the RCS into images and then using a CNN to perform drone classification, which required much computation. As a result, they introduced a weight-optimization model that reduces the computational overhead, resulting in improved long short-term memory (LSTM) networks. The authors showed how a database of mmWave radar RCS signatures can be utilized to recognize and categorize drones in [31]. They demonstrated RCS measurements at 28 GHz for a carbon-fiber drone model. The measurements were collected in an anechoic chamber and provided significant information regarding the RCS signature of the drone. The authors aided the RCS-based detection probability and range accuracy by performing simulations in metropolitan environments. The drones were placed at different distances ranging from 30 m to 90 m, and the RCS signatures used for detection and classification were developed by trial and error. The authors proposed a novel drone-localization and activity-classification method using vertically oriented mmWave radar antennas to measure the elevation angle of the drone from the ground station in [32]. The measured radial distance and elevation angle were used to estimate the height of the drone and the horizontal distance from the radar station. A machine learning model was used to classify the drone's activity based on micro-Doppler signatures extracted from radar measurements taken in an outdoor environment. The system architecture and performance of the FAROS-E 77 GHz radar at the University of St Andrews were reported in [33] for detecting and classifying drones.. The goal of the system was to demonstrate that a highly reliable drone-classification sensor could be used for security surveillance in a small, low-cost, and portable package. To enable robust micro-Doppler signature analysis and classification, the low phase noise and coherent architecture take advantage of the high Doppler sensitivity available at mmWave frequencies. Even when a drone hovered in a stationary manner, the classification algorithm was able to classify its presence. In [34], the authors employed a vector network analyzer that functioned as a continuous wave radar with a carrier frequency of 6 GHz to gather Doppler patterns from test data and then recognize the motions using a CNN.