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Multi-Sensor Planning and Scheduling of Dual Unmanned Flight Platforms.
- Source :
- Journal of Zhengzhou University: Engineering Science; Jul2023, Vol. 44 Issue 4, p67-73, 7p
- Publication Year :
- 2023
-
Abstract
- The multi-sensor scheduling problem of dual unmanned flight platforms has a complex coupling relationship, and how to reasonably match flight segments, sensors and targets is an intricate part of the problem. To solve the multi-sensor scheduling problem of dual unmanned flight platforms, a flight segment task allocation mechanism was proposed in this study, which assigned tasks through the position relationship between the flight segment and the target. This mechanism could effectively split the tasks and solve the task coupling problem of the two platforms. Pre-processing targets and sensors were carried out to identify scarce sensor resources and independent targets in advance to guide subsequent scheduling matching. Then, sensor-target matching was carried out to plan the scheduling of sensors based on the task revenue. The scheduling of conflicting segments was adjusted according to the availability of sensor resources. Finally, task migration was carried out for targets that still need to complete their assigned tasks. The available free sensors in the corresponding flight segments were matched according to the target task remaining to improve the task revenue. In order to verify the effectiveness of the proposed algorithm in this study, existing algorithms were compared through simulation experiments on 10 test problems. The experimental results showed that this algorithm outperformed existing algorithms in task revenue and stability with a significant reduction in running time. [ABSTRACT FROM AUTHOR]
Details
- Language :
- Chinese
- ISSN :
- 16716833
- Volume :
- 44
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- Journal of Zhengzhou University: Engineering Science
- Publication Type :
- Academic Journal
- Accession number :
- 164717695
- Full Text :
- https://doi.org/10.13705/j.issn.1671-6833.2023.04.007