对于有模型误差的惯导系统，采用常规卡尔曼滤波会导致较大的状态估计误差，甚至使滤波器发散。
For the inertial navigation system with model errors, there exists a large error in estimating a state using conventional Kalman filter, and it can even make the filter diverging.
仿真结果表明，两种方法结合，可以有效地防止滤波器发散，缩小实际的滤波误差，提高滤波精度，实现滤波器参数的在线改进。
Simulation result shows that the presented method reduces the error of actual filter, improves the accuracy and can amend filter's parameters on-line.
系统建模是卡尔曼滤波的基础，系统模型不准确带来的验前数据误差，使滤波器精度降低，可能造成发散。
System model is the foundation of Kalman filter. The nondeterminacy of system model brought on apriori data error, as the low precision and the diverge of the filter.
在水下被动目标跟踪系统中，直角坐标系下的扩展卡尔曼滤波器容易发散而导致滤波精度很差。
Concerning the problem to instability and low accuracy of the passive filter on bearings-only target tracking, a modified adaptive Extended Kalman filter algorithm on polar coordinate is presented.
由于系统本身和外部条件的不确定性，很难对系统各状态进行准确的数学描述，造成滤波器不稳定甚至发散。
Due to uncertainty in both system itself and the circumstance condition, it's difficult to take an accurate mathematics description of every state of the system.
由于系统本身和外部条件的不确定性，很难对系统各状态进行准确的数学描述，造成滤波器不稳定甚至发散。
Due to uncertainty in both system itself and the circumstance condition, it's difficult to take an accurate mathematics description of every state of the system.
应用推荐