摘要
捷联惯导系统在初始对准过程中由于模型参数与实际系统存在偏差,并且系统噪声与量测噪声统计特性往往是未知的,采用卡尔曼滤波不能取得理想的滤波效果。为避免滤波发散以及模型的不确定性,提出了基于Sage-Husa算法的区间自适应卡尔曼滤波方法。给出了捷联惯导系统的误差模型以及区间自适应卡尔曼滤波方程。在噪声统计特性未知时,比较了常规卡尔曼滤波与区间自适应卡尔曼滤波在初始对准中的应用效果。仿真结果表明,区间自适应卡尔曼滤波在噪声统计特性未知时能够有效地提高系统的滤波效果,是一种比较理想的初始对准滤波方法。
In initial alignment of strap-down inertial navigation system （SINS）, because of the parameters model have some deviations from the real system, and the noise statistic characteristics is not exactly known, the regular Kalman filter can not gain the ideal consequence. For avoiding filter divergence and model incertitude, an interval adaptive Kalman filter （IAKF） based on Sage-Husa algorithm is proposed, exactitude error model of SINS and the interval adaptive Kalman filter equation are given. When the noise statistical characteristics are unknown, comparing the usage between regular Kalman filter and interval adaptive Kalman filter in initial alignment. The sim- ulation result show that the algorithm can improve system effectiveness, it is an ideal navigation filter algorithm of initial alignment.
出处
《科学技术与工程》
北大核心
2012年第28期7231-7235,共5页
Science Technology and Engineering
关键词
捷联惯导系统
区间自适应卡尔曼滤波
初始对准
strap-down inertial navigation system interval adaptive Kalman filter initial alignment
