摘要
临空高速飞行器具有飞行空域大、速度快等特点。针对临空高速飞行器协同跟踪面临分配资源要素众多、协同关系复杂等问题,在构建了面向临空高速飞行器的多传感器协同跟踪优化模型的基础上,通过改进粒子群优化算法的速度及位置更新方式,提出了结合置信算子及排斥算子的粒子群优化(confidence operator and repulsion operator particle swarm optimization,CORO-PSO)算法。仿真实验验证了所提算法能够满足临空高速飞行器协同跟踪对精确性及实时性的高要求,对临空高速飞行器探测跟踪系统的发展提供了一定的方法支撑。
The near space high-speed vehicle has the characteristics of large aerospace and high speed. As the allocation resources involve numerous factors and the cooperative relations are complex, multisensor cooperative tracking puts forward higher requirements for the optimization algorithm about tracking accuracy and realtime. Firstly, the multi-sensor cooperative tracking optimization model is proposed. Secondly, the confidence operator and repulsion operator particle swarm optimization （CORO-PSO） is proposed by introducing confidence and repulsion operators into the basic PSO. Finally, experimental results demonstrate this algorithm is reliable and can provide the method support for the development of cooperative tracking of the near space highspeed vehicle.
作者
范成礼
付强
邢清华
FAN Chengli FU Qiang XING Qinghua(School of Air And Missile Defense, Air Force Engineering University, Xi~ an 710051 , China)
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2017年第3期476-481,共6页
Systems Engineering and Electronics
基金
国家自然科学基金(61272011)资助课题
关键词
临空高速飞行器
多传感器协同
跟踪优化
改进粒子群优化算法
near space high-speed vehicle
multi-sensor cooperative
tracking optimization
improved particle swarm optimization （PSO）
