Strategies for Optimized UAV Surveillance in Various Tasks and Scenarios: A Review

Fang, Zixuan; Savkin, Andrey V.

doi:10.3390/drones8050193

Cited by 1 publication

(1 citation statement)

References 261 publications

(254 reference statements)

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“…Wu et al [20] proposed a fixed-time extended state observer (FXTESO)-based preset performance fault-tolerant control method for handling the attitude tracking problem of UAVs under actuator failures, and the effectiveness of the proposed control strategy is verified through simulation results. Fan et al [21] comprehensively evaluated optimization strategies for UAVs in a number of key areas such as infrastructure inspection, security surveillance, and environmental monitoring; analyzed the effectiveness of UAVs in specific tasks; and explored the challenges of operating in complex environments. Liu et al [22] proposed a simultaneous path planning method based on an improved deep reinforcement learning (DRL) algorithm, combined with radio mapping techniques, to optimize 3D trajectories for cellular-connected UAVs in order to improve the system efficiency and reliability.…”

Section: Introductionmentioning

confidence: 99%

Design of a UAV Trajectory Prediction System Based on Multi-Flight Modes

Shi,

Zhang,

Shi

et al. 2024

Drones

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With the burgeoning impact of artificial intelligence on the traditional UAV industry, the pursuit of autonomous UAV flight has emerged as a focal point of contemporary research. Addressing the imperative for advancing critical technologies in autonomous flight, this paper delves into the realm of UAV flight state recognition and trajectory prediction. Presenting an innovative approach focused on improving the precision of unmanned aerial vehicle (UAV) path forecasting via the identification of flight states, this study demonstrates its efficacy through the implementation of two prediction models. Firstly, UAV flight data acquisition was realized in this paper by the use of multi-sensors. Finally, two models for UAV trajectory prediction were designed based on machine learning methods and classical mathematical prediction methods, respectively, and the results before and after flight pattern recognition are compared. The experimental results show that the prediction error of the UAV trajectory prediction method based on multiple flight modes is smaller than the traditional trajectory prediction method in different flight stages.

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Section: Introductionmentioning

confidence: 99%