计算机工程与应用2017，Vol.53Issue(3)：124-130,7.DOI:10.3778/j.issn.1002-8331.1505-0149
基于改进当前统计模型的自适应无源跟踪算法
Adaptive algorithm based on modified current statistical model for passive tracking
摘要
Abstract
Aiming at the defect that normal current statistical model can not adjust the limits of target acceleration adap-tively in passive tracking, a correctional coefficient is designed, through the current acceleration of maneuvering targets to adjust the limits of target acceleration adaptively. Meanwhile, with fuzzy control, the correctional coefficient is adjusted in real-time, then the model is improved. Finally, this improved model is combined with a Cubature Kalman Filter(CKF)to form the modified current statistic model for the passive tracking algorithm. Simulation results show that, compared with the adaptive tracking algorithm based on normal current statistical model, the new algorithm has better performance on tracking non-maneuvering and weak and strong maneuvering targets.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Key words
passive tracking/current statistical model/maneuvering targets/adaptive/correctional coefficient/fuzzy control分类
信息技术与安全科学引用本文复制引用
张卓然,叶广强,赵晓林..基于改进当前统计模型的自适应无源跟踪算法[J].计算机工程与应用,2017,53(3):124-130,7.基金项目
国家自然科学基金(No.61132007) （No.61132007）
航空科学基金(No.20145596024). （No.20145596024）
