Unmanned Aerial Vehicles Sensor-Based Detection Systems Using Machine Learning Algorithms

Al-Adwan, Romil S.; Al-Habahbeh, Osama M.

doi:10.18178/ijmerr.11.9.662-668

Cited by 1 publication

(2 citation statements)

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“…The accuracy of any UAV detection system might be hindered by different interference factors in a real-world scenario. [21] and weather conditions [17]; obstructions; moving objects [20] nighttime [21] or varying weather conditions such as fog, rain, and snow [17]; forested areas or cluttered environments; birds, airplanes, insects, and moving parts of scenes [20] reduced visibility; line-of-sight blockages preventing visual identification; increased false negatives…”

Section: Vision-based Detectionmentioning

confidence: 99%

“…4. Environmental conditions : Environmental factors that can impair the effectiveness of UAV detection systems include varying weather conditions [ 17 ] (such as rain, fog, or wind); urban locations with plenty of obstructions, such as buildings and trees; terrain; background noise; adverse lighting conditions [ 21 ]; etc. Severe environmental factors can affect the precision and robustness of sensors, such as radar or optical systems, resulting in false positives or false negatives in UAV identification.…”

Section: Introductionmentioning

confidence: 99%

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

Seidaliyeva,

Ilipbayeva,

Taissariyeva

et al. 2023

Sensors

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The fast development of unmanned aerial vehicles (UAVs), commonly known as drones, has brought a unique set of opportunities and challenges to both the civilian and military sectors. While drones have proven useful in sectors such as delivery, agriculture, and surveillance, their potential for abuse in illegal airspace invasions, privacy breaches, and security risks has increased the demand for improved detection and classification systems. This state-of-the-art review presents a detailed overview of current improvements in drone detection and classification techniques: highlighting novel strategies used to address the rising concerns about UAV activities. We investigate the threats and challenges faced due to drones’ dynamic behavior, size and speed diversity, battery life, etc. Furthermore, we categorize the key detection modalities, including radar, radio frequency (RF), acoustic, and vision-based approaches, and examine their distinct advantages and limitations. The research also discusses the importance of sensor fusion methods and other detection approaches, including wireless fidelity (Wi-Fi), cellular, and Internet of Things (IoT) networks, for improving the accuracy and efficiency of UAV detection and identification.

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