摘要
传统的双机协同组网目标定位模型中,滤波方法大多为交互式多模型算法。交互式多模型算法的缺陷为需要目标机动先验模型,且模型个数的选择难以同时满足工程上关于跟踪精度和算法复杂性的要求。通过引入渐消因子,实时自适应校正机动目标的状态估计偏差,有效降低了目标运动先验模型对滤波的影响,提高了系统的机动处理能力和模型的工程实用性。
In traditional two cooperative aircraft networking localization models,Interacting Multiple Model algorithm (IMM) is widely used. However,IMM depends on transcendental target maneuvering models,and the model number can hardly meet tracking accuracy and algorithm complexity requirements at the same time in practice. Therefore,the fading factor is introduced here to real-time adaptively correct maneuvering target state estimation deviations,reducing the impact of target movement transcendental model on filtering results and improving the system capability of handling maneuvering targets and the model engineering applicability.
出处
《火力与指挥控制》
CSCD
北大核心
2014年第12期127-129,134,共4页
Fire Control & Command Control
基金
国家自然科学基金资助项目(61273075)
关键词
双机协同
扩展卡尔曼滤波
渐消因子
two cooperative aircraft
extended Kalman filter
fading factor