带模型误差系统的自适应卡尔曼滤波在捷联惯导系统初始对准中的应用研究

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。可采用自适应卡尔曼滤波算法，通过引入虚拟噪声，利用观测数据带来的信息，在线改进滤波器的设计，并将其运用到捷联惯导系统的初始对准中，由此得到的滤波估计比常规卡尔曼估计具有更高的精度和准确度。试验及计算机仿真结果验证了该方法的有效性。

Application of Adaptive Kalman Filter Model Error System in Initial Alignment of Strapdown INS

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.This paper presents a new method,which employs adaptive Kalman filter algorithm.It improves the design of Kalman filter online by introducing the dummy noise and using the observation information,and apply it to the initial alignment of strapdown inertial navigation system.The estimation accuracy of the filter using this method is higher than that of conventional Kalman filter.The examination and the simulation show the validity of this method.