摘要
为了解决联合卡尔曼滤波法存在的不足,文中给出了用模糊推理系统与卡尔曼法相结合的方法,该方法可以在线对噪声统计特性进行修改。它不仅可以对子滤波器进行自适应,而且对主滤波器的融合方式提出了全新的信息融合算法,构造一个模糊推理系统,根据实际情况由该系统得到各子滤波器的各状态估计量的不同信任权值,实时地进行加权融合,既避免了传统联合卡尔曼法的复杂计算,又提高了融合精度。最后把此法用于INS/GPS组合导航系统中,仿真结果证明该方法是有效的,实用的。
In order to resolve the shortcoming of the traditional federal Kalman filtering, a new method is presented in which the fuzzy reasoning system is combined with the traditional Kalman technology. This method can modify the statistical characteristic of noises on real time. It can not only modify the local filter but also bring forward a bran-new information fusion arithmetic which is applied to central filter. A fuzzy reasoning system is built to obtain different weights of the all states estimate values that are based on the actual circumstance and fuse these values. Then the complicated calculation is avoided and precision is advanced. At last the new means is applied in the INS/GPS Integrated Navigation system and the result of computer simulation indicates that it is useful with high effectiveness and practical.
出处
《系统仿真学报》
EI
CAS
CSCD
北大核心
2005年第1期124-128,共5页
Journal of System Simulation
基金
863子课题资助项目(KL0202200201)
关键词
信息融合
组合导航
模糊推理系统
联合卡尔曼滤波
information fusion
integrated navigation
fuzzy reasoning system
federal Kalman filtering
