# UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey
## 1 Introduction
By training these models with datasets of acoustic signatures, they can learn to distinguish UAV signals from noise and other interference, enhancing the accuracy and reliability of this approach [27]. Nevertheless, acoustic monitoring is highly susceptible to ambient noise and possesses a constrained detection range. As per the conducted tests, the maximum detection range for UAVs does not exceed 300 m.  
Table 1 provides a comprehensive overview of the aforementioned monitoring techniques. It is crucial to acknowledge that the detection distances presented are derived from existing literature and systems, and may exhibit variations based on factors such as UAV type, hardware parameters, and associated algorithms. To improve the effectiveness of  
UAV recognition, UAV detection systems typically utilize a combination of two to three of the aforementioned technologies [22]. In the past, UAV-monitoring systems were primarily deployed in critical military and civilian facilities such as airports and military bases. However, with the increasing popularity of UAVs, the need for UAV-monitoring systems has expanded to a wider range of settings, including construction sites, communities, shopping malls, schools, and other locations. This has created a demand for UAV-monitoring systems that are more cost-effective, scalable, and responsive. To meet this challenge, researchers have been exploring ways to detect UAVs using lower-cost and passive sensors [28]. The development of software-defined radio has greatly reduced the cost of RF detection, making it more accessible to a broader range of users. In recent years, neural network-enhanced RF-based detection, visual-based detection, and acoustic-based detection have emerged as promising options for general UAV-monitoring systems in urban environments. With the development of lightweight models, there are already some models that can obtain acceptable results with very little computing resources, which makes it possible to use edge computing for UAV detection. While radar surveillance is highly effective in detecting aircraft, its use is limited to specific locations due to the high cost and radiation associated with the technology. As a result, it may not be suitable for detecting illegal UAVs in urban areas. Our findings indicate that within the research community, there is a notable divergence in focus regarding anti-UAV research. While some researchers prioritize the development of low-cost and lightweight anti-UAV solutions, the majority of researchers show a greater interest in enhancing the effectiveness of UAV detection and tracking. Our survey aims to address this disparity and raise awareness among researchers regarding the broader anti-UAV requirements in urban environments. We believe there is a pressing need for further exploration of low-cost and passive sensor-based anti-UAV systems. By directing attention towards these areas, we hope to foster increased research and innovation in developing comprehensive anti-UAV solutions that are both cost-effective and capable of meeting the specific challenges posed by urban environments. This paper provides a comprehensive review of passive UAV surveillance technologies, encompassing RF-based, acoustic-based, and vision-based methods for UAV detection, localization, and tracking. Our research reveals that certain lightweight UAV depth detection models have been effectively downsized for deployment on edge devices, facilitating the integration of edge computing and deep learning. In the city-wide anti-UAV, the integration of numerous urban infrastructure monitoring facilities presents a challenge in achieving a centralized computing center due to the large volume of data. To address this, calculations can be performed on edge devices, enabling faster UAV detection. Currently, there is a wide range of anti-UAV systems that have been deployed in both commercial and military sectors to address the challenges posed by UAVs. In this article, we provide an overview of the existing military and commercial anti-UAV systems.