Advances and Challenges in Drone Detection and Classification Techniques: A State-of-the-Art Review

Seidaliyeva, Ulzhalgas; Ilipbayeva, Lyazzat; Taissariyeva, Kyrmyzy; Smailov, Nurzhigit; Matson, Eric T.

doi:10.3390/s24010125

Cited by 15 publications

(4 citation statements)

References 77 publications

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“…In recent years, there has been a surge in research efforts in the field of UAVs, with various areas of study being explored. Antennas [2,3], aircraft detection [4,5], control [6], and trajectories [7] have emerged as the most prominent research directions [8]. Moreover, research on human-UAV interaction [9], swarm behavior [10], environmental sensing [11], safety and reliability [12], and application-specific development [13] has also garnered significant attention [14].…”

Section: Introductionmentioning

confidence: 99%

Design and Optimization of UAV Aerial Recovery System Based on Cable-Driven Parallel Robot

Wu,

Sun,

Yue

et al. 2024

Biomimetics

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Aerial recovery and redeployment can effectively increase the operating radius and the endurance of unmanned aerial vehicles (UAVs). However, the challenge lies in the effect of the aerodynamic force on the recovery system, and the existing road-based and sea-based UAV recovery methods are no longer applicable. Inspired by the predatory behavior of net-casting spiders, this study introduces a cable-driven parallel robot (CDPR) for UAV aerial recovery, which utilizes an end-effector camera to detect the UAV’s flight trajectory, and the CDPR dynamically adjusts its spatial position to intercept and recover the UAV. This paper establishes a comprehensive cable model, simultaneously considering the elasticity, mass, and aerodynamic force, and the static equilibrium equation for the CDPR is derived. The effects of the aerodynamic force and cable tension on the spatial configuration of the cable are analyzed. Numerical computations yield the CDPR’s end-effector position error and cable-driven power consumption at discrete spatial points, and the results show that the position error decreases but the power consumption increases with the increase in the cable tension lower limit (CTLL). To improve the comprehensive performance of the recovery system, a multi-objective optimization method is proposed, considering the error distribution, power consumption distribution, and safety distance. The optimized CTLL and interception space position coordinates are determined through simulation, and comparative analysis with the initial condition indicates an 83% reduction in error, a 62.3% decrease in power consumption, and a 1.2 m increase in safety distance. This paper proposes a new design for a UAV aerial recovery system, and the analysis lays the groundwork for future research.

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Section: Introductionmentioning

confidence: 99%

Design and Optimization of UAV Aerial Recovery System Based on Cable-Driven Parallel Robot

Wu,

Sun,

Yue

et al. 2024

Biomimetics

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“…In accordance, there are many drone-related literature reviews in the field, which serve in turn as informative backgrounds about different aspects of UAVs. In [46], the established review took only into consideration the interactions between a UAV and the payload (i.e., suspended loads), whereas in [47], the state-ofthe-art focused mainly on drone detection and classification techniques. From another perspective, the review in [48] identifies current gaps in the application of UAVs for the creation of 3D models in the contexts of urban planning and historic monuments preservation.…”

mentioning

confidence: 99%

“…As can be noted from the literature survey encompassing the modern reviews in [46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63], the majority of these articles actually focus on the UAV domain of usage and application fields. In other terms, there exist no reviews about the drones themselves, involving different architectures, structures, avionics, and software-to-hardware branches (i.e., the results of algorithms over the behavioral mechanism of sensors).…”

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confidence: 99%

Comprehensive Investigation of Unmanned Aerial Vehicles (UAVs): An In-Depth Analysis of Avionics Systems

Osmani,

Schulz

2024

Sensors

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The evolving technologies regarding Unmanned Aerial Vehicles (UAVs) have led to their extended applicability in diverse domains, including surveillance, commerce, military, and smart electric grid monitoring. Modern UAV avionics enable precise aircraft operations through autonomous navigation, obstacle identification, and collision prevention. The structures of avionics are generally complex, and thorough hierarchies and intricate connections exist in between. For a comprehensive understanding of a UAV design, this paper aims to assess and critically review the purpose-classified electronics hardware inside UAVs, each with the corresponding performance metrics thoroughly analyzed. This review includes an exploration of different algorithms used for data processing, flight control, surveillance, navigation, protection, and communication. Consequently, this paper enriches the knowledge base of UAVs, offering an informative background on various UAV design processes, particularly those related to electric smart grid applications. As a future work recommendation, an actual relevant project is openly discussed.

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“…However, it highlights the lack of in-depth research on the impact of range on detection effectiveness. A more recent review paper on UAV detection techniques is [19], which reviews the knowledge about drone detection, studying the threats and challenges faced due to drones' dynamic behavior, size and speed diversity, battery life, etc. Novel processing techniques to improve the accuracy and efficiency of UAV detection and identification are also studied.…”

mentioning

confidence: 99%

Analysis of Distance and Environmental Impact on UAV Acoustic Detection

Tejera-Berengue,

Zhu-Zhou,

Utrilla-Manso

et al. 2024

Electronics

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This article explores the challenge of acoustic drone detection in real-world scenarios, with an emphasis on the impact of distance, to see how sound propagation affects drone detection. Learning machines of varying complexity are used for detection, ranging from simpler methods such as linear discriminant, multilayer perceptron, support vector machines, and random forest to more complex approaches based on deep neural networks like YAMNet. Our evaluation meticulously assesses the performance of these methods using a carefully curated database of a wide variety of drones and interference sounds. This database, processed through array signal processing and influenced by ambient noise, provides a realistic basis for our analyses. For this purpose, two different training strategies are explored. In the first approach, the learning machines are trained with unattenuated signals, aiming to preserve the inherent information of the sound sources. Subsequently, testing is then carried out under attenuated conditions at various distances, with interfering sounds. In this scenario, effective detection is achieved up to 200 m, which is particularly notable with the linear discriminant method. The second strategy involves training and testing with attenuated signals to consider different distances from the source. This strategy significantly extends the effective detection ranges, reaching up to 300 m for most methods and up to 500 m for the YAMNet-based detector. Additionally, this approach raises the possibility of having specialized detectors for specific distance ranges, significantly expanding the range of effective drone detection. Our study highlights the potential of drone acoustic detection at different distances and encourages further exploration in this research area. Unique contributions include the discovery that training with attenuated signals with a worse signal-to-noise ratio allows the improvement of the general performance of learning machine-based detectors, increasing the effective detection range achieved, and the feasibility of real-time detection, even with very complex learning machines, opening avenues for practical applications in real-world surveillance scenarios.

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Advances and Challenges in Drone Detection and Classification Techniques: A State-of-the-Art Review

Cited by 15 publications

References 77 publications

Design and Optimization of UAV Aerial Recovery System Based on Cable-Driven Parallel Robot

Design and Optimization of UAV Aerial Recovery System Based on Cable-Driven Parallel Robot

Comprehensive Investigation of Unmanned Aerial Vehicles (UAVs): An In-Depth Analysis of Avionics Systems

Analysis of Distance and Environmental Impact on UAV Acoustic Detection

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