自适应卡尔曼滤波在惯导初始对准中的应用研究

本文研究了自适应卡尔曼滤波技术在惯导系统中的应用。在噪声统计特性未知或近似已知的情况下，采用常规卡尔曼滤波会导致较大的状态估计误差，甚至使滤波发散；而自适应卡尔曼滤波在估计状态的同时，利用观测数据带来的信息，可在线估计噪声的统计特性，从而不断地改进滤波器的设计，由此得到的滤波估计比常规卡尔曼估计精度更高。本文采用Ｓａｇｅ 和Ｈｕｓａ 自适应滤波算法，结合惯导初始对准，给出了计算机仿真。仿真结果进一步证实在噪声统计特性不确切知道的情况下，自适应卡尔曼滤波的估计精度高于常规卡尔曼滤波的估计精度。

The Application of Adaptive Kalman Filter Technique in Initial Alignment of Inertial Navigation System

The application of adaptive Kalman filtertechnique in inertial navigtion system is studied in this paper.In case of the noisestatistic characteristic being unknown or not completely known,there would exist a largeerror in estimating state using conventional Kalman filter,even made the filter diverge.However,adaptive Kalman filter can,at the same time of estimating state,estimate thestatistic characteristic of noise online using observation information.Therefore thedesign of the filter can be continuously improved and the obtained estimation usingadaptive Kalman filter possessed higher accuracy compared with that of conventional Kalmanfilter.The paper gives the simulation of initial alignment,which adopted Sage & Husaadaptive Kalman filter algorithm.The validity of the proposed means is proved by thesimulation result.