UAV Detection and Tracking in Urban Environments: Comparison
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Unmanned aerial vehicles (UAVs) have gained significant popularity across various domains, but their proliferation also raises concerns about security, public safety, and privacy. Consequently, the detection and tracking of UAVs have become crucial. Among the UAV-monitoring technologies, those suitable for urban Internet-of-Things (IoT) environments primarily include radio frequency (RF), acoustic, and visual technologies. 

  • anti-UAV
  • UAV detection
  • UAV tracking
  • UAV dataset
  • anti-UAV systems

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] [1], surveillance[2,3] [2][3], edge computing[4,5] [4][5], disaster area reconstruction [6,7][6][7], aerial base stations [8[8][9],9], intelligent transportation [10,11][10][11], wireless power transfer [12], environmental monitoring [13], and more [14,15][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 mono-static, 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. 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].
Table 1.
Comparison of UAV surveillance technologies.
Surveillance Technology Localization/Tracking Method Detection Range
Category

Category

 Method

Method

 Accuracy

Accuracy

Challenges

Radar

Doppler-based tracking

10,000 m

Traditional RF-based Detection

Fractal dimension (FD) [29]

100%

Low radar cross section

Delay-based localization

Low speed and altitude

axially integrated bispectra (AIB) [30]

98%

Acoustic

square integrated bispectra (SIB) [31]

TDOA\AOA-based localization

0–300 m

High ambient noise

96%

Vision

Signal Spectrum (SFS) [32]

Motion-based tracking

97.85%

100–1000m

Confuse with birds

Indistingushable small objects

Wavelet energy entropy (WEE)[32] [32]

93.75%

RF

Power spectral entropy (PSE) [32]

RSS\AOA-based localization

83.85%

5000m

Ambient RF noise

Multipath

RF-based Detection Using Deep Learning

Y. Mo et al. [

Non-line of sight
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.

2.  UAV Detection and Identification

In this section, wthe researchers will conduct a comprehensive analysis and comparison of RF-based, acoustic-based, and vision-based methods for the detection and identification of UAVs in urban IoT environments. WeThe researchers  will explore these methods from various perspectives, including traditional methods, deep learning methods, and the available public and semi-public datasets. Additionally, wethe researchers will discuss the specific challenges associated with identifying UAV intrusions in IoT environments and review recent research focused on leveraging edge devices for UAV detection. Table 2 provides a performance comparison of various UAV detection methods, with the data obtained from literature research. The vision method based on deep learning demonstrates good accuracy even when experiments are conducted on public datasets that are not specifically optimized for UAVs. However, recent studies in the literature have shown that after fine-tuning, optimization, and training on specialized UAV datasets, the accuracy rate can exceed 90%, indicating a high level of competitiveness.
Table 2.
Comparison of different UAV detection methods.

33

]

99%

C. J. Swinney et al. [34]

100%

S. Lu et al. [35]

98%

Z. He et al. [36]

90.2%

T. Li et al. [37]

98.57%

Traditional Acoustic-based Detection

Mel Frequency Cepstrum Coefficient (MFCC)

97%

Acoustic-based Detection Using Deep Learning

S. Al-Emadi et al. [27]

96.38%

Q. Dong et al. [38]

99%

İ Aydın et al. [39]

98.78%

Vision-based Detection Using Deep Learning

RCNN

58.50%

SPPNet

59.20%

Fast RCNN

70.00%

Faster RCNN

73.20%

YOLOv3

63.40%

SSD

76.80%

RetinaNet

59.10%

Traditional RF methods involving RF feature extraction have made progress in recent years. The RF approach based on deep learning uses technology to convert RF signals into images, facilitating feature extraction using deep networks. Traditional acoustic methods mainly rely on Mel-Frequency cepstrum coefficient (MFCC) feature extraction, often supplemented by linear predictive cepstrum coefficient (LPCC). Acoustic methods based on deep learning convert sound signals into spectrograms and extract relevant features. In recent years, vision-based detection methods, especially those based on deep learning, have gained prominence. The models listed in the study use public datasets that are not specifically optimized for UAV detection. However, when these models are optimized and trained on specialized UAV datasets, detection accuracy can often exceed 90%. The effectiveness of UAV detection methods depends on factors such as dataset quality, training techniques, and optimization for specific use cases.

3. UAV Localization and Tracking

UAV localization and tracking play a crucial role in anti-UAV research. This section provides a review of UAV localization and UAV tracking methods. UAV localization research primarily concentrates on utilizing RF and acoustic-based methods. These methods are preferred due to their ability to accurately determine the location of UAVs. Regarding UAV tracking, wthe researchers categorize the studies into filter-based approaches and deep Siamese networks approaches. Filter-based methods utilize various filters for tracking UAVs, while deep Siamese networks approaches leverage deep learning techniques for tracking. The advancements made in these research areas are thoroughly discussed, highlighting the progress and innovations achieved in UAV tracking.

3.1. UAV Localization

RF-based positioning technology has become increasingly mature, leading to numerous studies on RF-based UAV positioning. RF sensors are the only technology that can locate both the UAV and the pilot. On the other hand, acoustic-based UAV positioning technology is feasible for short-distance detection [22]. Although acoustic-based UAV localization technology started later than other methods, it has achieved remarkable results in recent years. In contrast, vision-based UAV tracking technology has its advantages, but due to the rapid movement of UAVs, the effect of visual positioning technology is not ideal.

3.2. UAV Tracking

UAV tracking is a type of object tracking task that involves estimating the state and trajectory of the UAV. Object tracking (OT) is a fundamental open problem that has been extensively studied in the past decade. Two prominent paradigms for OT are Filters-based methods and deep Siamese Networks (SNs) [102][40].

4. Anti-UAV System

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][41], 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][42] 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][43], 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][44] 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][45] 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][46] is a rapidly deployable, scalable, and modular system that combines electro-optics, electronic surveillance, and radar sensors. ELTA Systems [119][47] 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 ourthe 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, wthe researchers 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.

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