Design of Standoff Cooperative Target-Tracking Guidance Laws for Autonomous Unmanned Aerial Vehicles

Li, Zhen; Chen, Xin; Zhao, Zhenhua

doi:10.1155/2021/6682160

Cited by 4 publications

(5 citation statements)

References 23 publications

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“…From the perspective of control system design, Li et al studied the two guidance laws of multiple autonomous UAVs for coordinated tracking of static and moving targets outside the defense zone. It adopts quantitative analysis method, fixes relevant parameters, only measures one neighbor in the communication topology network, and uses guidance laws to track static or moving targets [7].…”

Section: Related Workmentioning

confidence: 99%

Technical Research on Moving Target Monitoring and Intelligent Tracking Algorithm Based on Machine Vision

Zhou

Geng

2022

Wireless Communications and Mobile Computing

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Machine vision is an important branch of the rapid development of modern artificial intelligence, and it is a key technology to convert the image information of monitoring targets into digital signals. However, due to the wide range of machine vision applications, this research focuses on its application in video surveillance. In the era of artificial intelligence, the detection and tracking of moving objects have always been a key issue in video surveillance. The simulation of human vision is realized by combining the relevant functions of the computer and the image acquisition device, which enables the computer to have the ability to recognize the surrounding environment through images. The intelligent video analysis technology can automatically analyze and extract the key useful information from the video source with the powerful data processing ability of the computer, so as to realize the computer’s “understanding” of the video. It allows the computer to “understand” what is shown in the video or what kind of “event” happened and provides a new method and reliable basis for accident detection and accident analysis. Therefore, after a brief introduction to machine vision, moving target monitoring methods, and intelligent tracking algorithms, this paper will focus on moving target monitoring and intelligent tracking strategies for video surveillance. In addition, this paper will focus on introducing the principle of intelligent tracking algorithm through formulas and compare the accuracy and success rate of target monitoring and intelligent tracking between the machine vision-based algorithm and other algorithms during the experiment. Finally, experiments show that the monitoring and tracking effect of machine vision combined with “cloud” is the best, and the overall average can reach 85.7%. Based on this, this paper fully confirms the feasibility of the moving target monitoring and intelligent tracking algorithm based on machine vision.

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Section: Related Workmentioning

confidence: 99%

Technical Research on Moving Target Monitoring and Intelligent Tracking Algorithm Based on Machine Vision

Zhou

Geng

2022

Wireless Communications and Mobile Computing

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“…Accordingly, the UAVs must fly in a circle around the ground-based moving target [2]. On the other hand, to avoid collisions and to ensure that the sensors can cover the target, the UAVs need to be evenly distributed around the target and maintain a certain phase interval [3]. Standoff tracking is a possible solution for target tracking using a team of fixed-wing UAVs.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Standoff Target Tracking using Multiple Fixed-Wing UAVs with Input Constraints in Unknown Wind

Liu,

Xiang,

Zhu

2023

Drones

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This paper investigates the problem of cooperative standoff tracking using multiple fixed-wing unmanned aerial vehicles (UAVs) with control input constraints. In order to achieve accurate moving target tracking in the presence of unknown background wind, a coordinated standoff target tracking algorithm is proposed. The objective of the research is to steer multiple UAVs to fly a circular orbit around a moving target with prescribed intervehicle angular spacing. To achieve this goal, two control laws are proposed, including relative range regulation and space phase separation. On one hand, a heading rate control law based on a Lyapunov guidance vector field is proposed. The convergence analysis shows that the UAVs can asymptotically converge to a desired circular orbit around the target, regardless of their initial position and heading. Through a rigorous theoretical proof, it is concluded that the command signal of the proposed heading rate controller will not violate the boundary constraint on the heading rate. On the other hand, a temporal phase is introduced to represent the phase separation and avoid discontinuity of the wrapped space phase angle. On this basis, a speed controller is developed to achieve equal phase separation. The proposed airspeed controller meets the requirements of the airspeed constraint. Furthermore, to improve the robustness of the aircraft during target tracking, an estimator is developed to estimate the composition velocity of the unknown wind and target motion. The proposed estimator uses the offset vector between the UAV’s actual flight path and the desired orbit, which is defined by the Lyapunov guidance vector field, to estimate the composition velocity. The stability of the estimator is proved. Simulations are conducted under different scenarios to demonstrate the effectiveness of the proposed cooperative standoff target tracking algorithm. The simulation results indicate that the temporal-phase-based speed controller can achieve a fast convergence speed and small phase separation error. Additionally, the composition velocity estimator exhibits a fast response speed and high estimation accuracy.

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“…Accordingly, the UAVs must fly in a circle around the ground moving target [2]. On the other hand, to avoid collisions and to ensure that the sensors can cover the target, the UAVs need to be evenly distributed around the target and 2 maintain a certain phase interval [3]. The standoff tracking is a possible solution for target tracking using a team of fixed-wing UAVs.…”

Section: Introductionmentioning

confidence: 99%

“…(2) Intervehicle phase separation, which focuses on the distributing the loitering UAVs uniformly over a standoff circle with certain angular phase difference by controlling their airspeeds [5]. (3) Background wind resistance, which focuses on how to achieve robust stability for UAVs performing standoff tracking in the presence of wind and target motion [6].…”

Section: Introductionmentioning

confidence: 99%

“…Rigorous proofs for global convergence and satisfying airspeed limitation using the proposed speed controller are provided. (3) The target motion and background wind are regarded as external disturbance. The offset vector caused by external disturbance between the actual trajectory and the perfect/desired orbit is utilized to estimate the composition velocity of background wind and target motion.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Cooperative Standoff Target Tracking using Multiple Fixed-wing UAVs with Input Constraints in Unknown Wind

Zhong¹,

Xiang²,

Zhu³

2023

Preprint

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This paper investigates the problem of cooperative standoff tracking using multiple fixed-wing unmanned aerial vehicles (UAVs) with control input constraints. In order to achieve accurate target tracking in the presence of unknown background wind and target motion, a coordinated standoff target tracking algorithm is proposed. The objective of the research is to steer multiple UAVs to fly a circular orbit around a moving target with prescribed intervehicle angular spacing. To achieve this goal, two control laws are proposed, including relative range regulation and space phase separation. On one hand, a heading rate control law based on Lyapunov guidance vector field is proposed. The convergence analysis show that the UAVs can asymptotically converge to a desired circular orbit around the target, regardless of their initial position and heading. Through rigorous theoretical proof, it is concluded that the command signal of the proposed heading rate controller will not violate the boundary constraint on heading rate. On the other hand, a temporal phase is introduced to represent the phase separation and avoid discontinuity of the wrapped space phase angle. On this basis, a speed controller is developed to achieve equal phase separation. The proposed airspeed controller met the requirement of airspeed constraint. In addition, to improve the robustness of the aircraft during target tracking, an estimator is developed to estimate the composition velocity of the unknown wind and target motion. The proposed estimator uses the offset vector between the UAV’s actual flight path and the desired orbit which is defined by Lyapunov guidance vector field to estimate the composition velocity. The stability of the estimator is proved. Simulations are conducted under different scenarios to demonstrate the effectiveness of the proposed cooperative standoff target tracking algorithm. The simulation results of indicate that, the temporal-phase-based speed controller can achieve fast convergence speed and small phase separation error. Additionally, the composition velocity estimator exhibits fast response speed and high estimation accuracy.

show abstract

Design of Standoff Cooperative Target-Tracking Guidance Laws for Autonomous Unmanned Aerial Vehicles

Cited by 4 publications

References 23 publications

Technical Research on Moving Target Monitoring and Intelligent Tracking Algorithm Based on Machine Vision

Technical Research on Moving Target Monitoring and Intelligent Tracking Algorithm Based on Machine Vision

Cooperative Standoff Target Tracking using Multiple Fixed-Wing UAVs with Input Constraints in Unknown Wind

Cooperative Standoff Target Tracking using Multiple Fixed-wing UAVs with Input Constraints in Unknown Wind

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