Encyclopedia
Please note this is a comparison between Version 1 by Xiaochen Yan and Version 3 by Peter Tang.
Unmanned无人机 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. （UAV） 在各个领域都广受欢迎，但它们的扩散也引发了对安全、公共安全和隐私的担忧。因此，无人机的检测和跟踪变得至关重要。在无人机监测技术中，适用于城市物联网（IoT）环境的技术主要包括射频（RF）、声学和视觉技术。
- anti-UAV
- UAV detection
- UAV tracking
- UAV dataset
- anti-UAV systems
1. Introduction简介
With the rapid advancement of technology, unmanned aerial vehicles (随着技术的快速发展，无人机（UAVs) have found numerous urban IoT (Internet of Things) applications in fields such as rescue operations [1], surveillance [2][3], edge computing [4][5], disaster area reconstruction [6][7], aerial base stations [8][9], intelligent transportation [10][11], wireless power transfer [12], environmental monitoring [13], and more [14][15]. According to market forecasts, the UAV market will experience a compound annual growth rate (）在救援行动[1]，监视[2,3]，边缘计算[4,5]，灾区重建[6,7]，空中基站[8,9]，智能交通[10,11]，无线电力传输[12]、环境监测[13]等[14,15]。根据市场预测，无人机市场的复合年增长率（CAGR) of ）为7.9%, expanding from USD 26.2 billion in 2022 to USD 38.3，从26年的2亿美元扩大到2022年的38亿美元[3]。然而，无人机的使用越来越多，也引发了对安全、公共安全和隐私的担忧。为了解决这些问题，研究人员一直在探索各种无人机监测技术。无人机监视可以通过四种主要技术来实现：雷达、射频 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.）、视觉和声学监视。
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 雷达监视是一种成熟且广泛采用的空域监视和侦察方法。该技术可用于检测缺乏无线电通信或完全自主的无人机。多普勒雷达是一种强大的技术，它利用多普勒效应来检测移动物体的速度。微多普勒雷达是多普勒雷达的高级版本，可以检测物体内部的速度和运动差异。微多普勒雷达是探测无人机的理想方法，因为它们的螺旋桨会产生较大的线速度差[17]。其他可能适用的雷达技术包括非视距雷达、超宽带雷达、毫米波雷达[18]、混沌单静态、双静态[19]和多静态雷达[20]。由于其雷达横截面低[21]和速度慢，用雷达探测无人机既困难又复杂[22]。实验结果表明，雷达的探测范围很少超过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 [23]。
可以拦截和分析发射射频波信号的无人机，以跟踪和定位它们[24]。在许多情况下，手动操作的无人机与地面站和GNSS（全球导航卫星系统）通信进行操作，从而可以拦截信号并获得坐标和视频馈送等信息。然而，仅依靠机载传感器进行操作的自主无人机可能不会发出射频信号，这使得定位和跟踪它们更具挑战性[17]。射频监控表现出在 (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,范围内检测和定位无人机的能力。但是，它们的性能可能会受到多径和非视距传播等因素的影响。
近年来，基于计算机视觉的方法在检测和监控无人机方面越来越受欢迎。通过利用深度学习技术，可以训练模型自动从无人机图像和视频数据集中提取外观和运动特征，从而能够使用摄像头的视频监控来识别和跟踪这些车辆[25]。此外，红外摄像机可用于在低环境光条件下检测和识别无人机[26]。通过将计算机视觉技术与红外摄像机相结合，可以在各种照明和环境条件下有效地检测和监控无人机。基于视觉的方法在区分无人机和鸟类方面遇到了挑战，特别是当无人机位于相当远的距离时。事实上，识别 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 范围以外的无人机变得极其艰巨，如果不是几乎不可能的话。
在无人机导航过程中，动力装置和螺旋桨叶片发出的声波可以使用声学麦克风进行检测，该麦克风将这些声波的压力转换为电信号。这些声波为每架无人机创建个性化的“audio fingerprint音频指纹” 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 ，从而实现个人识别。然而，由于环境噪声和声波衰减等因素，检测这些声波在实践中可能具有挑战性。深度学习模型已被应用于提高该方法在无人机检测中的有效性。通过使用声学特征数据集训练这些模型，他们可以学习区分无人机信号与噪声和其他干扰，从而提高这种方法的准确性和可靠性[27]。然而，声学监测极易受到环境噪声的影响，并且检测范围有限。根据进行的测试，无人机的最大探测范围不超过300 m.
Table 。
表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].
提供了上述监测技术的全面概述。重要的是要承认，所提供的检测距离来自现有的文献和系统，并且可能会根据无人机类型、硬件参数和相关算法等因素而表现出变化。为了提高无人机识别的有效性，无人机检测系统通常利用上述两种到三种技术的组合[22]。
Table表 1. Comparison of UAV surveillance technologies.
无人机监视技术的比较。
| Surveillance Technology
监控技术 |
Localization/Tracking Method
定位/跟踪方法 |
Detection Range
检测范围 |
Challenges
挑战 |
|---|---|---|---|
|
Radar雷达 |
]. 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.过去，无人机监控系统主要部署在机场和军事基地等关键的军事和民用设施中。然而，随着无人机的日益普及，对无人机监控系统的需求已扩展到更广泛的环境，包括建筑工地、社区、购物中心、学校和其他地点。这创造了对更具成本效益、可扩展性和响应能力更强的无人机监控系统的需求。为了应对这一挑战，研究人员一直在探索使用低成本和无源传感器检测无人机的方法[28]。软件定义无线电的发展大大降低了射频检测的成本，使其更容易被更广泛的用户使用。近年来，神经网络增强的射频检测、基于视觉的检测和基于声学的检测已成为城市环境中通用无人机监测系统的有前景的选择。随着轻量级模型的发展，已经有一些模型可以用很少的计算资源获得可接受的结果，这使得使用边缘计算进行无人机检测成为可能。虽然雷达监视在探测飞机方面非常有效，但由于与该技术相关的高成本和辐射，其使用仅限于特定位置。因此，它可能不适合在城市地区检测非法无人机。
2. UAV Detection and Identification无人机检测与识别
In this section, the 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. The researchers will explore these methods from various perspectives, including traditional methods, deep learning methods, and the available public and semi-public datasets. Additionally, the 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 在本节中，我们将对基于射频、基于声学和基于视觉的方法进行全面的分析和比较，用于检测和识别城市物联网环境中的无人机。我们将从各种角度探讨这些方法，包括传统方法、深度学习方法以及可用的公共和半公共数据集。此外，我们将讨论与识别物联网环境中的无人机入侵相关的具体挑战，并回顾最近专注于利用边缘设备进行无人机检测的研究。表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 提供了各种无人机检测方法的性能比较，以及从文献研究中获得的数据。基于深度学习的视觉方法即使在未专门针对无人机优化的公共数据集上进行实验时也表现出良好的准确性。然而，最近的文献研究表明，在专门的无人机数据集上进行微调、优化和训练后，准确率可以超过90%, indicating a high level of competitiveness.，表明具有很高的竞争力。Table表 2. Comparison of different UAV detection methods.
不同无人机检测方法的比较。
|
Category类别 |
Method方法 |
Accuracy准确性 |
||||
|---|---|---|---|---|---|---|
Doppler-based tracking | 基于多普勒的跟踪 |
|||||
|
Traditional RF-based Detection传统的基于射频的检测 | ||||||
|
10,000 m米 |
Low radar cross section低雷达横截面 |
|||||
100% |
Delay-based localization基于延迟的本地化 |
integrated bispectra (AIB) |
||||
|
Low speed and altitude低速和低海拔 |
||||||
] |
98% |
Acoustic声 |
Vision | |||
|
square平方积分双光谱 integrated bispectra (SIB) [31 |
基于 TDOA\AOA-based localization 的本地化 |
]（SIB） [31] 0–300 m米 |
96% |
High ambient noise高环境噪音 |
视觉 |
|
|
Motion-based tracking基于运动的跟踪 |
100–1000m 米 |
97.85% |
Confuse with birds与鸟类混淆 |
|||
|
Indistingushable small objects不可分割的小物件 |
||||||
|
93.75% |
RF射频 |
|||||
|
Power spectral entropy (PSE) [32] |
基于 RSS\AOA-based localization 的本地化 |
5000 |
83.85%m米 |
Ambient RF noise环境射频噪声 |
||
|
Multipath多路径 |
||||||
|
RF-based Detection Using Deep Learning |
Y. Mo et al. [33] |
Non-line of sight非视线 |
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
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, thwe 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.
