Intensive Review of Drones Detection and Tracking: Linear Kalman Filter Versus Nonlinear Regression, an Analysis Case

Zitar, Raed Abu; Mohsen, Amani; Seghrouchni, Amal ElFallah; Barbaresco, Frédéric; Al-Dmour, Nidal A.

doi:10.1007/s11831-023-09894-0

Cited by 24 publications

(6 citation statements)

References 64 publications

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“…Both of these trends are presented in Table 1, along with examples illustrating the scope of the literature reviews performed. Animal behaviour research [23] Disaster management [24] Drone operation Detection [25][26][27] Social acceptance [28,29] Routing and scheduling problem [30][31][32][33][34] Sensors 2024, 24, 1205 3 of 29…”

Section: Research Trend Scope Of the Analysed Literature Publicationsmentioning

confidence: 99%

Risks of Drone Use in Light of Literature Studies

Tubis,

Poturaj,

Dereń

et al. 2024

Sensors

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This article aims to present the results of a bibliometric analysis of relevant literature and discuss the main research streams related to the topic of risks in drone applications. The methodology of the conducted research consisted of five procedural steps, including the planning of the research, conducting a systematic review of the literature, proposing a classification framework corresponding to contemporary research trends related to the risk of drone applications, and compiling the characteristics of the publications assigned to each of the highlighted thematic groups. This systematic literature review used the PRISMA method. A total of 257 documents comprising articles and conference proceedings were analysed. On this basis, eight thematic categories related to the use of drones and the risks associated with their operation were distinguished. Due to the high content within two of these categories, a further division into subcategories was proposed to illustrate the research topics better. The conducted investigation made it possible to identify the current research trends related to the risk of drone use and pointed out the existing research gaps, both in the area of risk assessment methodology and in its application areas. The results obtained from the analysis can provide interesting material for both industry and academia.

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Section: Research Trend Scope Of the Analysed Literature Publicationsmentioning

confidence: 99%

Risks of Drone Use in Light of Literature Studies

Tubis,

Poturaj,

Dereń

et al. 2024

Sensors

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“…Results showed a 0.4 improvement in object detection over other conventional kinematic models that do not account for significant trajectory changes. Zitar et al (2023) provided an extensive review of objects and drone detection and tracking methods [96]. The article presents state-of-the-art methods used in drone detection and tracking, offering critical analysis and comparisons based on recent research material.…”

Section: Sensor Fusionmentioning

confidence: 99%

Counter Drone Technology: A Review

Gonzalez-Jorge,

Aldao,

Fontenla-Carrera

et al. 2024

Preprint

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Unmanned Aerial Vehicles (UAVs), commonly known as drones, have experienced a significant surge in both civilian and military applications, owing to their versatility and cost-effectiveness. However, a negligent use or the operation with harmful intent of these aircraft poses a potential risk to public safety and the privacy of individuals. In response to this threat, counter-drone systems have been developed in recent years, including technologies to detect, position, and neutralize these aircraft. This paper presents a review of different existing commercial counter-drone systems, the technologies behind detection, tracking, and classification, as well as soft and hard kill mitigation.

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“…While the Kalman filter is widely used in UAV tracking tasks due to its low computational cost, achieving high accuracy results in practical applications is often challenging [112]. Recently, DCF methods and Siamese tracking algorithms have shown excellent performance in object tracking, thanks to their use of end-to-end offline learning.…”

Section: Deep Siamese Networkmentioning

confidence: 99%

UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey

Yan,

Fu,

Lin

et al. 2023

Applied Sciences

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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. In this article, we provide a comprehensive review of passive UAV surveillance technologies, encompassing RF-based, acoustic-based, and vision-based methods for UAV detection, localization, and tracking. Our research reveals that certain lightweight UAV depth detection models have been effectively downsized for deployment on edge devices, facilitating the integration of edge computing and deep learning. In the city-wide anti-UAV, the integration of numerous urban infrastructure monitoring facilities presents a challenge in achieving a centralized computing center due to the large volume of data. To address this, calculations can be performed on edge devices, enabling faster UAV detection. Currently, there is a wide range of anti-UAV systems that have been deployed in both commercial and military sectors to address the challenges posed by UAVs. In this article, we provide an overview of the existing military and commercial anti-UAV systems. Furthermore, we propose several suggestions for developing general-purpose UAV-monitoring systems tailored for urban environments. These suggestions encompass considering the specific requirements of the application scenario, integrating detection and tracking mechanisms with appropriate countermeasures, designing for scalability and modularity, and leveraging advanced data analytics and machine learning techniques. To promote further research in the field of UAV-monitoring systems, we have compiled publicly available datasets comprising visual, acoustic, and radio frequency data. These datasets can be employed to evaluate the effectiveness of various UAV-monitoring techniques and algorithms. All of the datasets mentioned are linked in the text or in the references. Most of these datasets have been validated in multiple studies, and researchers can find more specific information in the corresponding papers or documents. By presenting this comprehensive overview and providing valuable insights, we aim to advance the development of UAV surveillance technologies, address the challenges posed by UAV proliferation, and foster innovation in the field of UAV monitoring and security.

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Intensive Review of Drones Detection and Tracking: Linear Kalman Filter Versus Nonlinear Regression, an Analysis Case

Cited by 24 publications

References 64 publications

Risks of Drone Use in Light of Literature Studies

Risks of Drone Use in Light of Literature Studies

Counter Drone Technology: A Review

UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey

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