**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 [1–3].

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.

**Citation:** Yousaf, J.; Zia, H.; Alhalabi, M.; Yaghi, M.; Basmaji, T.; Shehhi, E.A.; Gad, A.; Alkhedher, M.; Ghazal, M. Drone and Controller Detection and Localization: Trends and Challenges. *Appl. Sci.* **2022**, *12*, 12612. https://doi.org/10.3390/ app122412612

Academic Editors: Luis Gracia and Carlos Perez-Vidal

Received: 16 September 2022 Accepted: 5 December 2022 Published: 9 December 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Tsouros 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 [1–7]. 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. This review aims to survey the quickly evolving field, record what is notable and popular within this sector, and provide recommendations for future investigators. Table 1 summarizes the covered topics in different sections of this study.

**Table 1.** Summary of reviewed topics for drones and their controller detection.


The rest of the study is organized as follows: Section 2 details the architecture of UAVs and associated security concerns with drones. A comprehensive review of UAV detection technologies is outlined in Section 3. The studies about drone controller localization are reviewed in Section 4. Lastly, Section 5 concludes the findings of the study.

#### **2. UAV Architecture and Security Concerns**

#### *2.1. UAV Architecture*

UAVs have multiple subsystems integrated to perform various operations, such as launch, fly, operate, process, transmit, and receive commands from remote or ground stations [3,5]. Four main UAV subsystems should be considered: (1) a power unit, (2) a communication module, (3) the main computing device, and (4) a sensor board. The power unit is designed to provide a longer lifetime for UAV operation without charging it [4]. The high-level architecture of the UAV system is illustrated in Figure 1, including UAV's main computer processes commands based on the collected data from other subsystems or components (GPS, sensors, gyroscopes, accelerometers, antennas, receivers, etc.). These

data or commands are transferred through a communication link between the UAV and the ground control station (GCS). This communication is mainly monitored to detect UAVs based on RF and radar-based technologies (details in Sections 3.1 and 3.2).

**Figure 1.** High-level architecture of a UAV.

A brief description of the major components of UAE architecture is as follows:


#### *2.2. Security Concerns*

Regarding UAV security, two main topics are discussed in the literature: the security and safety of UAVs and the potential misuse of UAVs against critical infrastructures and privacy-related issues.

Threats to UAV security are well-researched concerning targeting its hardware, software, and communication module. In [6], threats to various components of the UAV system

are discussed. GCS's physical, network, and cloud security have been highlighted as vulnerabilities that can be exploited. Moreover, threats exist against the UAV communication according to the communication medium technology or type (WiFi, cellular network, GPS, and other RF solutions). The possible common attacks are eavesdropping, jamming, replay, denial of service, hijacking, etc. Other threats include mission disruption and itinerary tracking [7,8].

Despite promising application benefits using UAVs, threats also exist by their prevalence in the public domain. UAV security threats and incidents are mainly caused by privacy violations of sensitive sites, airplane flight disruption, damage and explosion in targeted areas, and sensitive data leakage through eavesdropping [7,8].

#### **3. UAV Detection Methods**

As mentioned in Section 2, there are many sectors in which UAVs have been explored and adopted, utilizing their practical and advanced features. The continuous development and improvement of UAV's main systems and components, i.e., flight controller, sensors, gyroscopes, cameras, GPS, etc., increased the demand and reliance on UAVs for accomplishing different civilian and military missions. Moreover, they are widely available in the market at a reasonable cost compared to other solutions.

Research has been dedicated to designing, developing, and implementing systems for detecting malicious UAVs. Techniques of these systems are classified into passive and active. RF-based, acoustic, and vision-based techniques are among the passive technologies, whereas radar-based techniques are defined as active technologies. These technologies vary in operational conditions, covering range, consistency, accuracy, and many other parameters. This section focuses on UAV detection technologies and discusses the general framework and related work.
