摘要
针对无源跟踪中,标准当前统计模型无法自适应调整加速度极限值的缺点,设计了一种修正系数来通过机动目标的当前加速度自适应调整模型的加速度极限值,同时利用模糊控制的方法对修正系数的取值进行实时调整,实现了对当前统计模型的改进。最后结合容积卡尔曼滤波算法构造基于改进当前统计模型的自适应无源跟踪算法。仿真结果表明,相比基于标准当前统计模型的自适应跟踪算法,新算法对非机动目标、弱机动目标以及强机动目标都有更好的跟踪效果。
Aiming at the defect that normal current statistical model can not adjust the limits of target acceleration adaptivelyin passive tracking,a correctional coefficient is designed,through the current acceleration of maneuvering targets toadjust the limits of target acceleration adaptively.Meanwhile,with fuzzy control,the correctional coefficient is adjusted inreal-time,then the model is improved.Finally,this improved model is combined with a Cubature Kalman Filter(CKF)toform the modified current statistic model for the passive tracking algorithm.Simulation results show that,compared withthe adaptive tracking algorithm based on normal current statistical model,the new algorithm has better performance ontracking non-maneuvering and weak and strong maneuvering targets.
作者
张卓然
叶广强
赵晓林
ZHANG Zhuoran;YE Guangqiang;ZHAO Xiaolin(College of Aeronautics and Astronautics Engineering, Air Force Engineering University, Xi’an 710038, China)
出处
《计算机工程与应用》
CSCD
北大核心
2017年第3期124-130,共7页
Computer Engineering and Applications
基金
国家自然科学基金(No.61132007)
航空科学基金(No.20145596024)
关键词
无源跟踪
当前统计模型
机动目标
自适应
修正系数
模糊控制
passive tracking
current statistical model
maneuvering targets
adaptive
correctional coefficient
fuzzy control
