基于协同Memetic自适应QPSO算法的传感器-目标分配问题求解  

Solution to sensor-target assignment problem based on cooperative memetic adaptive QPSO algorithm

作　　者：段修生[1] 徐公国 单甘霖[1] 

机构地区：[1]军械工程学院电子与光学工程系,河北石家庄050003

出　　处：《系统工程与电子技术》2016年第12期2769-2776,共8页Systems Engineering and Electronics

基　　金：军内科研基金重点资助项目(ZS2015070132A12009)资助课题

摘　　要：对复杂防空作战环境下的传感器-目标分配(sensor target assignment,STA)问题进行了研究,建立了基于识别、跟踪、定位多阶段综合作战效能的分配模型。针对该模型,首先基于粒子群聚集度和进化度判断,对传统量子粒子群(quantum particle swarm optimization,QPSO)算法进行了改进,提出了自适应QPSO算法。然后,结合多粒子群协同和Memetic搜索策略,提出了基于协同Memetic自适应QPSO算法的STA求解方法。同时,为使粒子位置矢量反映分配方案,依据不同战场环境设计了两种特殊的粒子编码方案。最后通过仿真实验验证了所提算法的有效性。Aiming at the sensor-target assignment （STA） problem under the complex aerial defense combat environment, a new STA model is proposed with the total combat effectiveness of the identification, tracking and positioning stages. And then, the quantum particle swarm optimization （QPSO） algorithm is improved based on the judgment of particles aggregation and evolution, which is called the self-adaptive QPSO algorithm. Next, combined with the cooperation of multi-particles and memetic searching strategy, the cooperative memetic self-adaptive QPSO algorithm is proposed. At the same time, in order to reflect the sensor combination in parti- cle position vector, two special particle coding methods are designed for different combat environments. Finally, the experiments show that the improved algorithm is effective.

关 键 词：传感器-目标分配 传感器管理 量子粒子群 自适应 MEMETIC算法 防空系统 

分 类 号：V243[航空宇航科学与技术—飞行器设计]