摘要
针对实际的应用中车载航位推算系统的模型参数、噪声的统计特性不确定性,影响估计效果,提出了车载航位推算的模糊自适应卡尔曼滤波模型及其滤波算法;该方法通过监视理论残差与实际残差的比值是否接近1,应用模糊推理系统不断地调整量测噪声协方差的加权,对自适应卡尔曼滤波的量测噪声协方差进行递推修正,通过该算法来抑制噪声对精度的影响,进而提高系统的导航精度;仿真结果表明,这种算法能够有效地提高系统的精度,是一种比较理想的车载DR导航滤波方法。
For the practical application of vehicle dead reckoning system model parameters, noise statistics cannot he fully estimated, proposed vehicle dead reckoning model of the fuzzy adaptive Kalman filter and its filtering algorithm. This method is mainly used in vehicle Dead Reckoning system to deal with time varied statistic of measurement noise in different working conditions. By monitoring if the ratio be- tween filter residual and actual residual is near 1, this algorithm modifies recursively the measurement noise covariance of Kalman Filtering online using the Fuzzy Inference System （FIS） to make the covariance close to real measurement covariance gradually. Accordingly the kal- man filter performs optimally and the accuracy of the navigation system is improved. Simulation results show that the algorithm can improve system accuracy; it is an ideal vehicle DR navigation filter algorithm
出处
《计算机测量与控制》
CSCD
北大核心
2012年第3期774-776,796,共4页
Computer Measurement &Control
基金
国防预研(103030203)
关键词
车载导航
航位推算
模糊自适应滤波
滤波算法
vehicle navigation
dead reckoningl fuzzy adaptive filtering
filter algorithms
