Unmanned aerial vehicle cluster air combat method based on wolf roundup behavior

A technology of unmanned aerial vehicles and air combat, applied in the direction of non-electric variable control, instruments, control/adjustment systems, etc., can solve the problems of chaotic air combat situation, unfavorable air combat method design and implementation, and difficulty in evaluating combat effects

Active Publication Date: 2019-07-30
BEIHANG UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] For multi-UAV air combat, disorderly combat forms will cause confusion in the air combat situation, making it

Method used

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  • Unmanned aerial vehicle cluster air combat method based on wolf roundup behavior
  • Unmanned aerial vehicle cluster air combat method based on wolf roundup behavior
  • Unmanned aerial vehicle cluster air combat method based on wolf roundup behavior

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Embodiment Construction

[0099]The effectiveness of the method proposed by the present invention is verified below through a specific example. In this example, N=7 UAVs are used to conduct an air game with M=3 enemy targets, and 7 UAVs form an encirclement formation to attack 3 targets. In this example, the expected enclosure radius is d=50m. The target tries not to be surrounded by drone formations, with a range of d D = 1.2d detection radius, all UAVs within this range can be detected, and the repulsive potential field function shown in formula (17) is used to guide them to get rid of the movement.

[0100]

[0101] Among them, j represents the jth target, G is the number of UAVs within the detection radius, σ r =3 is the repulsion coefficient, k 13 = 3, k 23 =1.5,k 14 = 3, k 24 =1.5 is the state tracking gain, is the yaw angle and yaw rate of the jth target, is the desired flight altitude of the target, Desired nose turn for target. At the same time, in order to ensure that the targ...

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Abstract

The invention discloses an unmanned aerial vehicle cluster air combat method based on a wolf roundup behavior. The method comprises the steps that first, an unmanned aerial vehicle cluster air combatis initialized; second, cluster air combat situation threat cost is constructed; third, an unmanned aerial vehicle cluster air combat encirclement topological structure is determined by use of wolf situation learning; fourth, the encirclement topological structure is mapped into a wolf roundup formation configuration; fifth, unmanned aerial vehicle formation encirclement control is performed basedon the wolf roundup configuration; and sixth, a cluster air combat formation control instruction is output. The method can support a dynamically-changing air combat environment, an attack rick of anunmanned aerial vehicle cluster is effectively lowered, and optimal overcome effectiveness is achieved; and meanwhile, the stability of an attack formation, the consistency of attack behaviors and a high target damage probability are guaranteed, and dynamic decision, consistency control and other difficulties in cluster control are overcome.

Description

technical field [0001] The invention is an unmanned aerial vehicle cluster air combat method based on the hunting behavior of ferocious wolves, and belongs to the field of autonomous control of unmanned aerial vehicles. Background technique [0002] With the complexity of combat missions, cluster air combat based on the coordinated operations of multiple UAVs has become one of the important research topics in the current military. On the one hand, due to the requirements in the practical application of UAVs, it may be necessary to carry multiple tasks and sensor loads in a single combat mission, and it needs to have long range, long endurance and high reliability. Very demanding. On the other hand, due to the bottleneck of a single drone technology, it is foreseeable that it will be difficult to make breakthroughs in a short period of time, the size of sensors will be difficult to further reduce, the integration of heterogeneous sensors will be difficult, the energy density...

Claims

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Application Information

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IPC IPC(8): G05D1/10
CPCG05D1/104
Inventor 段海滨张岱峰周锐邓亦敏申燕凯霍梦真陈琳赵建霞仝秉达于月平魏晨阮婉莹
Owner BEIHANG UNIV
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