摘要
以捷联惯导系统为应用背景,在建立MEMS陀螺的数学模型后,针对常规卡尔曼滤波算法的不足,引入一个加权因子,实时地对系统噪声和观测噪声进行估计和修正,从而降低系统的模型误差,抑制滤波发散。实验证明,在确保整个导航系统可靠性的前提下,MEMS陀螺提供了更加精确的角速率信息。
In the applicatien background of navigation system, the mathematical model of MEMS gyro is founded. In order to improve the performance of the kalman filter, the attenuation factor is imported. With the help of attenuation factor, adaptive kalman filter can estimate and correct the system noise and observation noise. The error of the system model is reduced and filter divergence is inhibited. The resuits of simulation show that the a＇aptive kalman filter has better performance. On the premise of ensuring the stability of the system, the accuracy of angular rate is improved obviously.
出处
《北京信息科技大学学报（自然科学版）》
2010年第1期46-48,77,共4页
Journal of Beijing Information Science and Technology University
基金
北京市属高等学校人才强教深化计划学术创新人才项目(PHR200906131)
现代测控技术教育部重点实验室资助
国家自然科学基金资助项目(60972118)
关键词
惯性导航
模糊
自适应卡尔曼
加权因子
inertial navigation
fuzzy
adaptive kalman
weighted factor
