摘要
应用自适应模糊逻辑系统(AFLS)原理,研究了一种基于卡尔曼滤波器的信息融合算法;AFLS通过在线监视融合数据新息是否为零均值白噪音,然后根据模糊规则调整融合滤波器的指数加权值,从而保证了滤波器的最优估计性能;仿真结果证明该方法在高噪声环境中具有良好的信息融合能力,能有效跟踪研究对象的状态变化。
A efficient information fusion algorithm is presented using adaptive fuzzy logic system （AFLS） to tune the Kalman filter. Through monitoring the innovation of data to be fused on real time, Kalman filter is adopted by exponential weighting according to the fuzzy rules to get the optimal state estimation. Simulation result indicates that the algorithm is efficiency under high noise circumstance and can track the variance of state.
出处
《计算机测量与控制》
CSCD
2006年第9期1230-1232,共3页
Computer Measurement &Control
基金
航空科学基金资助项目(01153075
01A53001)。
