# UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey
## 1 Introduction  
With the rapid advancement of technology, unmanned aerial vehicles (UAVs) have found numerous urban IoT (Internet of Things) applications in fields such as rescue operations [1], surveillance [2, 3], edge computing [4, 5], disaster area reconstruction [6, 7],aerial base stations [8; 9], intelligent transportation [10; 11], wireless power transfer [12], environmental monitoring [13], and more [14; 15]. According to market forecasts, the UAV market will experience a compound annual growth rate (CAGR) of 7.9%, expanding from USD 26.2 billion in 2022 to USD 38.3 billion in 2027 [16]. However, the increasing use of UAVs also raises concerns about security, public safety, and privacy. To address these issues, researchers have been exploring various UAV monitoring technologies. UAV surveillance can be achieved through four primary technologies: radar, radio frequency (RF), visual, and acoustic surveillance. Radar surveillance is a well-established and widely adopted method for airspace monitoring and reconnaissance. This technology can be used to detect UAVs that lack radio communication or with full autonomy. Doppler radar is a powerful technology that uses the Doppler effect to detect the velocity of moving objects. Micro-Doppler radar is an advanced version of Doppler radar that can detect speed and motion differences inside objects. Micro-Doppler radar is an ideal method for detecting UAVs because their propellers create large linear velocity differences [17]. Other radar technologies that may be applicable include non-line-of-sight radar, ultra-wideband radar, millimeter-wave radar [18], chaotic monostatic, bi-static [19], and multi-static radars [20]. Because of their low radar cross section [21] and slow speeds, detecting UAVs with radar is difficult and complex [22]. Experimental results indicate that the detection range of radar rarely surpasses 10,000 m [23]. UAVs emitting RF wave signals can be intercepted and analyzed to track and locate them [24]. In many cases, manually operated UAVs communicate with a ground station and a GNSS (Global Navigation Satellite System) for operation, making it possible to intercept signals and obtain information such as coordinates and video feeds. However, autonomous UAVs that rely solely on onboard sensors for operation may not emit RF signals, making it more challenging to locate and track them [17]. RF surveillance exhibit the capability to detect and locate UAVs within a range of 5000 m. However, their performance can be influenced by factors such as multipath and non-line-of-sight propagation. In recent years, computer vision-based methods have become increasingly popular for detecting and monitoring UAVs. By leveraging deep learning techniques, models can be trained to automatically extract appearance and motion features from datasets of UAV images and videos, enabling the identification and tracking of these vehicles using video surveillance from cameras [25]. In addition, infrared cameras can be used to detect and identify UAVs in low ambient light conditions [26]. By combining computer vision techniques with infrared cameras, UAVs can be detected and monitored effectively in various lighting and environmental conditions. Vision-based methods encounter challenges in distinguishing UAVs from birds, particularly when the UAVs are situated at a considerable distance. In fact, identifying UAVs beyond a range of 1000 m becomes exceedingly arduous, if not nearly impossible. Sound waves emitted by the power unit and propeller blades during UAV navigation can be detected using an acoustic microphone, which converts the pressure of these sound waves into electrical signals. These sound waves create an individualized \"audio fingerprint\" for each UAV, enabling individual identification. However, detecting these sound waves can be challenging in practice due to factors such as ambient noise and sound wave attenuation. Deep learning models have been applied to improve the effectiveness of this method for UAV detection.