# Drone and Controller Detection and Localization: Trends and Challenges
## 1 Introduction  
In recent years, there has been a significant advancement in unmanned aerial vehicles (UAVs). UAVs are widely used for commercial, civilian, and military applications due to their low cost, spatiotemporal coverage, and remote sensing capability. They have been specifically popular for collecting information in remote and inaccessible areas, such as military surveillance and search and rescue in floods or earthquakes [1, 2, 3]. An aircraft without onboard human command and control is called a UAV, also called a drone. Command and control are achieved autonomously by the embedded autopilot or remotely by the operators through a ground station [1]. Moreover, autonomous and remote controls can be integrated as a single UAV control mechanism. Over the years, the technology and features of UAVs have improved tremendously to address the varying requirements of different applications. In addition, ongoing research has been successful in finding ways to improve the performance of the UAV. Various designs and features that support their assigned missions in different fields and sectors have been proposed, such as shape structures, take-off, and landing techniques [2, 4]. Surveillance applications use UAV technology to be integrated as a standalone, connected platform for information gathering. The human detection system in [4] was achieved through input from thermal images and videos from a thermal camera connected to a UAV. These images and videos are categorized by reference to a thermal dataset in the system and are processed by sequence operations to achieve the final result. In the military, some geographic areas are difficult to reach for monitoring and detecting unwanted signals or entities. The proposed system in [5] overcomes this demand. Moreover, smart farming utilizes UAV technology for real-time monitoring and data acquisition of crop parameters, e.g., plant height, presence of weeds, or fungus. [PERSON] et al. [1] discussed different types of UAVs and explored multiple applications of UAVs in precision agriculture, crop health, and growth monitoring. Moreover, this work reviews data acquisition technologies and aerial image processing methods. Further, civil engineering utilizes UAV technologies for seismic risk assessment, transportation management, disaster response, construction management, surveying and mapping, and flood monitoring and evaluation [2]. To summarize, UAV technologies have numerous features that enable their usage in multiple sectors and applications. The benefits of such technologies include: (1) reducing human risk, (2) lower energy consumption, (3) a lower cost, (4) flexibility, and (5) accuracy of data collection. The advances in electronics and sensor technology have widened the scope of UAV applications for their likely invaluable inclusion in police, fire brigades, and disaster management operations. However, in recent years, UAVs have been used to perform malicious actions, such as drug smuggling, intelligence gathering, and suicide attacks [2; 3; 5]. UAVs also pose a threat to surpassing restricted government or military sites. In addition, with the prevalence of smaller UAVs, concerns over public privacy are rising. All these threats warrant an urgent need for research into UAV detection methods. It becomes strategic to detect and localize UAVs to prevent such malicious actions. Recently, various detection algorithms have been researched, such as active radar probes, acoustic recognition, infrared spectrum identification, visual recognition, and radio frequency (RF) signal detection [1; 2; 3; 4; 5; 6; 7]. This study aims to provide a detailed literature review of these detection methods, identify their strengths, explore various applications where they were used, and compare these methods for the major relevant studies in the open literature. Our study scope includes the detection and localization of multirotor and other UAV types. The study also reviews the techniques for the UAV controller localization of the detected drones.