基于自适应信息融合的导航系统构成与算法研究

由于组合导航系统应用环境的不确定性,给噪声统计特性的准确描述带来困难,这将造成Kalman滤波器不稳定甚至发散,目前常用的解决办法是直接估计系统噪声与量测噪声的方差阵 Q及R ,进行自适应滤波.但方程的增加将使计算量加大、实时性不能保证.本文在对基于信息融合的INS/GPS组合导航系统进行分析和设计的基础上,探讨了通过ARMA模型自适应参数辨识求解可变增益K,从而求出状态估计值的方法,并对辨识误差协方差的防饱和算法进行了研究.计算机仿真结果表明:该算法对提高导航精度和运算速度是行之有效的,所得结论有一定的工程实用价值.

System Frame and Algorithm Research Based on Adaptive Information Fusion for the Integrated Navigation

Due to the uncertainty of the application circumstance,it is difficult to describe the noise statistics of integrated navigation system accurately.This may cause the general Kalman filter unstability and even divergence.The common solving method is to estimate covariance matrix Q and R directly by adaptive filter.But the problem is that the increase of equations will cause heavy calculation,thus real-time cannot be ensured.This paper presents a new method to estimate states by ARMA model adaptive parameters identification to solve gain K ,at the same time research an anti-saturation algorithm for the error covariance.Computer simulation was carried out according to the analysis and design of the INS/GPS integrated navigation covariance.Computer simulation was carried out according to the analysis and design of the INS/GPS integrated navigation system based on information fusion,and the results show this method is very useful for improving the accuracy and calculation speed of the system.