为了解决常规卡尔曼滤波方法存在的不足,给出了用模糊推理系统与卡尔曼滤波相结合的方法。该方法通过调节理论残差与实测残差协方差的不一致程度,应用模糊推理系统不断调整滤波器的增益系数,对卡尔曼滤波器进行在线实时控制,最终保证最优估计。通过对反舰导弹飞行高度测量仿真,结果表明,该算法比常规卡尔曼滤波算法更能适应噪声变化的需求,具有较高的估计精度。

To resolve the shortcoming of the traditional Kalman filtering, a new method which combines the fuzzy inference system with the traditional Kalman filter was presented by monitoring the covariance between abstract residual and actual residual, this algorithm modified recursively the gain coefficient of the filter to control the Kalman filter. Finally, the optimal estimation was achieved. The simulation results of the anti-ship missile altitude measurement show that this algorithm can improve the precision of the result, adapt the system's noise variation better, and it is effective and practical.