In recent years, there has been a significant amount of literature on anti-UAV systems, and several anti-UAV systems have been used commercially and militarily.

One such system is DedroneTracker [113], a multi-sensor platform (RF, PTZ camera, radar) with countermeasure capabilities (jamming) released by Dedrone. The system can be extended and customized to meet specific field requirements and can automatically capture a portfolio of forensic data, including UAV make, model, time and length of UAV activity, and video verification. Dedrone also offers RF sensors and jammers, with a detection range of up to 1.5 km (up to 5 km in special cases) and a maximum jamming range of about 2 km. Droneshield [114] is another system that provides anti-UAV defense solutions, offering a range of standalone portable products as well as rapidly deployable fixed-site solutions. It employs a variety of surveillance technologies, including radar, audio, video, and radio frequency, to detect UAVs and provide effective countermeasures. Droneshield’s jammer can immediately cease video transmission back to the UAS operator and allow the UAV to respond to a live vertical controlled landing or return to the operator controller or starting point. Another RF-based UAV detection system is ARDRONIS [115], developed by Rohde and Schwarz. ARDRONIS performs detection for frequency-spread spectrum (FHSS) signals and WLAN signals, with a detection range of up to 7 km for commercial off-the-shelf remote signals and up to 5 km for UAVs such as the DJI Phantom 4 under ideal conditions. The AUDS [116] system, on the other hand, uses radar technology, video, and thermal imaging to detect and track UAVs and has directional radio frequency inhibition capabilities. With its Air Security Radar, the range of detection can be up to 10 km. The ORELIA Drone-Detector system [117] consists of an acoustic sensor and software for protected object monitoring, target tracing, and sensor adjustment. The detection range is about 100 m, and multiple acoustic detectors can be installed to detect UAVs in all directions. Falcon Shield [118] is a rapidly deployable, scalable, and modular system that combines electro-optics, electronic surveillance, and radar sensors. ELTA Systems [119] is another solution that combines radar, RF, and photoelectric sensors to detect and track UAVs more than 5 km away and to take soft and hard kill measures against them.

Based on our observation, the more mature anti-UAV systems are currently mainly focused on military applications, and these systems are often implemented using a combination of multiple sensors. RF monitoring, radar, and vision sensors are the three most commonly used types of multi-sensors. However, radar is not suitable for use in certain locations, such as urban environments, due to its strong radiation characteristics [22]. In urban environments, RF surveillance emerges as a crucial method for locating UAV pilots, while visual surveillance plays a pivotal role in capturing UAV flight videos as evidence of intrusions. This combination of RF and visual surveillance techniques provides a highly competitive solution for UAV detection in urban settings. Although the acoustic-based method has shortcomings such as small detection range and being easily affected by noise, it can also provide an effective supplement for UAV detection under certain conditions, such as poor visual conditions and complex electromagnetic environments.

Inspired by the above anti-UAV systems, we propose several suggestions for developing effective and scalable general anti-UAV systems. First, it is essential to consider the specific needs and requirements of the application scenario when selecting and combining sensors. Indeed, various environmental factors can impact the effectiveness of different surveillance methods in urban environments. For instance, visual surveillance may face challenges in locations with low visibility caused by heavy fog, sand, or dust. Similarly, complex electromagnetic environments can negatively affect the performance of RF surveillance. Additionally, acoustic surveillance may not be suitable in areas with strong winds or high levels of ambient noise. It is essential to consider these factors when selecting the most appropriate surveillance method for UAV detection, ensuring optimal performance in diverse urban scenarios.

Second, the detection and tracking components of the system should be integrated with appropriate countermeasure capabilities. One of the most prevalent countermeasure equipment against intruding UAVs is the use of RF jamming guns. These devices are designed to disrupt the communication and control signals of the UAVs, forcing them to either land or return to their point of origin. RF jamming guns are widely employed as a common UAV countermeasure. Another commonly utilized countermeasure involves deploying UAVs to capture intruding UAVs. This approach involves using specially designed UAVs equipped with nets, cables, or other mechanisms to physically intercept and capture the unauthorized UAVs. Both RF jamming guns and UAV capture methods serve as effective countermeasures in mitigating the risks and potential threats posed by unauthorized UAV activities.

Third, the system should be designed to be scalable and modular, allowing for easy deployment and adaptation to changing conditions. The design concept put forth by Dedrone Company offers valuable inspiration. It emphasizes the importance of creating scalable anti-UAV systems with a platform at the core. Such a design enables easier integration and maintenance of sensors in the future. By adopting a scalable approach, anti-UAV systems can adapt to evolving threats and technological advancements, ensuring flexibility and efficiency in the long run.

Fourth, data analytics and machine learning techniques can be employed to enhance the accuracy and efficiency of UAV detection and tracking. Indeed, machine learning techniques have already found extensive application in the field of UAV detection. The continuous advancements in machine learning algorithms and the availability of more comprehensive datasets are key factors in enhancing UAV detection capabilities. By leveraging more efficient learning methods and utilizing diverse and representative datasets, the accuracy and effectiveness of UAV detection systems can be significantly improved. This ongoing development in machine learning holds promise for further advancements in UAV detection technology.

Fifth, although accuracy is an important evaluation indicator, lightweight anti-UAV systems that sacrifice part of the accuracy seem to be more in demand in some non-important scenarios. In mobile scenarios or situations where budget constraints exist, lightweight anti-UAV systems that can operate on portable devices offer a more suitable solution. These systems, designed to be lightweight and portable, can be easily deployed and utilized in various environments. They provide flexibility and cost-effectiveness, making them a practical choice for scenarios where mobility and budget considerations are important factors. By leveraging lightweight anti-UAV systems, organizations can enhance their capabilities for UAV detection and mitigation while maintaining operational efficiency.

Furthermore, compatibility with existing sensors, such as ubiquitous video surveillance equipment, could significantly reduce the cost of anti-UAV systems. This suggestion of leveraging existing video surveillance equipment, inspired by Dedrone’s products, is indeed valuable. By ensuring compatibility and utilization of the already deployed video surveillance infrastructure, the deployment costs of anti-UAV systems can be significantly reduced. This approach aligns with the concept of using edge computing to assist in UAV detection, as previously discussed. By calibrating the physical locations of these monitoring devices, it becomes possible to detect and track illegally intruding UAVs effectively. This application scenario showcases the potential for cost-effective and efficient UAV detection by leveraging existing resources and edge computing capabilities.