摘要
对防空作战目标识别阶段中的传感器管理问题进行了研究,提出基于Rényi信息增量的多传感器管理调度方案。首先利用D-S证据理论进行融合推理,得出不同目标与不同传感器配对时的Rényi信息增量;然后,建立了基于系统总Rényi信息增量最大化的传感器分配模型,此外,对量子粒子群智能优化(QPSO)算法进行自适应改进,能够对分配模型进行快速求解;最后,通过仿真实验验证了算法的合理性和有效性。
Abstract： Aiming at the multi-sensor management problem in target recognition stage under complex aerial defense combat environment, a new multi-sensor scheduling method is proposed based on R6nyi divergence. Firstly, the D-S evidence theory is applied to obtain the R6nyi divergence of different sensors matched with different targets. Then, the sensor allocation model based on the maximized total R6nyi divergence is established. Besides, the Quantum Particle Swarm Optimization （QPSO） algorithm is improved in order to quickly solve the management model. Finally, the experiments show that the improved algorithm is feasible and effective.
出处
《电光与控制》
北大核心
2017年第5期15-19,共5页
Electronics Optics & Control
基金
军内科研基金重点资助项目(ZS2015070132A12009)
关键词
多传感器管理
目标识别
Rényi信息增量
证据理论
量子粒子群
Key words： multi-sensor management
target recognition
R6nyi divergence
evidence theory
quantum particle swarm optimization
