摘要
以GPS/SINS组合导航为应用背景,针对常规Kalman滤波由于先验知识不足,观测数据突变等容易引起的发散问题,提出了一种改进的自适应Kalman滤波。该算法将Sage-Huse自适应滤波和衰减记忆滤波相结合,以解决由于先验知识不足引起的滤波发散问题;在此基础上引入压缩函数,通过对野值进行有效地判断和处理以达到抑制滤波发散的目的。仿真结果表明:改进的自适应滤波算法不但可以有效地解决由于模型不够准确和野值等容易引起的发散问题,同时与传统滤波算法相比水平位置滤波精度分别提高了6倍和5.7倍,高程滤波精度提高了2.39倍,具有较好的自适应性和稳定性。
Taking GPS/SINS integrated navigation system as an application background in light of the problem that the conventional Kalman filter can easily diverge because of lack of prior knowledge and outli- ers, an improved adaptive Kalman filtering is proposed.The algorithm is based on the combination of Sage _Huse adaptive filter and fading memory filter which can suppress the filter divergence caused by lack of prior knowledge, and then a compression function which can effectively identify and deal with outliers is introduced,so the divergence problem caused by outliers can be solved.Simulation results indicate that the improved adaptive filtering algorithm can suppress the divergence caused by the inaccurate models and out- liers,and simultaneously the filter accuracy of the horizontal positions is improved 6 times and 5.7 times, and the filter accuracy of the height position is improved 2.39 times compared to the traditional algorithms, at the same time it is better in adaptability and stability.
出处
《空军工程大学学报（自然科学版）》
CSCD
北大核心
2015年第5期65-69,共5页
Journal of Air Force Engineering University(Natural Science Edition)
基金
国家自然科学基金资助项目(61273049)
