Volume 36, Number 4, August 2018
|Page(s)||722 - 727|
|Published online||24 October 2018|
An Airborne Multi-Sensor Task Allocation Method Based on Improved Particle Swarm Optimization Algorithm
School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China
2 Shenyang Aircraft Design & Research Institute, Shenyang 110035, China
The characteristics of airborne multi-sensor task allocation problem are analyzed, and an airborne multi-sensor task allocation model is established. In order to solve the problems of local convergence and slow convergence of the traditional Particle Swarm Optimization (PSO) algorithm, the structure and parameters of the existing Particle Swarm Optimization algorithm are adjusted, and the direction coefficient and far away factor are introduced to control the velocity and direction of the particle far away from the worst solution, so that the particle moves away from the worst solution while moving to the optimal solution. Based on the improved Particle Swarm Optimization algorithm, an airborne multi-sensor task allocation method is proposed using maximum detection probability as objective function, and the algorithm is simulated. The simulation results show that this algorithm can effectively allocate tasks and improve allocation effects.
Key words: task allocation / airborne multi-sensor / improved particle swarm optimization algorithm / detection probability
关键字 : 任务分配 / 机载多传感器 / 改进粒子群算法 / 探测概率
© 2018 Journal of Northwestern Polytechnical University. All rights reserved.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.