基于动态多尺度的火箭飞行数据模糊kalman融合
Fuzzy Kalman Fusion of Rocket Flight Data
为了进一步提高火箭飞行跟踪系统的精度和可靠性，解决常规kalman滤波法存在的不足，本文在动态多尺度融合的基础上，给出了用模糊推理系统与kalman方法相结合的方法，构成一个多采样率跟踪滤波器。该方法通过实时模糊控制器监视理论残差和实际残差的协方差一致程度，利用调整因子不断调整滤波器的增益系数，对kalman滤波器进行在线控制，最终实现最优估计。模拟火箭飞行数据进行仿真验证，结果表明提出的融合算法有较好的跟踪精度，优于传统的kalman多传感器融合算法。
o improve the accuracy and reliability of the rocket flight tracking system, and solve the deficiency of conventional kalman filtering, based on dynamic multi-scales fusion, a method combining fuzzy inference system and kalman filtering is presented, that constitutes a multi-resolution tracking filter. A real-time fuzzy adaptive filter controller is used to monitor the consistency of real value and theoretical value of residual covariance, and constantly adjust the filter\
柴毅、蒲爱香、胡利民、翟茹玲
航天
动态多尺度模糊kalman滤波小波变换广义回归神经网络模糊逻辑
multi-scalefuzzy kalman filteringwavelet transformGRNNfuzzy logic
柴毅,蒲爱香,胡利民,翟茹玲.基于动态多尺度的火箭飞行数据模糊kalman融合[EB/OL].(2009-02-16)[2025-08-16].http://www.paper.edu.cn/releasepaper/content/200902-718.点此复制
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