最小二乘滤波及其在INS/双星系统中的应用  

Least Square Filtering and Its Application in INS/DS Integrated System

作　　者：赵龙[1] 陈哲[1] 

机构地区：[1]北京航空航天大学自动化科学与电气工程学院,北京100083

出　　处：《压电与声光》2006年第4期483-485,共3页Piezoelectrics & Acoustooptics

摘　　要：为了克服传统Kalman滤波在实际应用中存在的局限性,研究了基于状态估计的最小二乘滤波。该方法将传统的最小二乘估计与状态估计问题有机结合,对系统噪声和量测噪声的统计特性不敏感。利用实测的激光捷联惯导系统/双星(LSINS/DS)组合数据对最小二乘滤波和传统Kalman滤波进行仿真比较,结果表明,在噪声统计特性未知的情况下,最小二乘滤波的精度更高、收敛速度更快、鲁棒性更强。To overcome the limitation of conventional Kalman filtering （CKF） in engineering application, a least square filtering （LSF） based on the state estimation is studied. LSF combined conventional least square estimation with the state estimation problem, and is without the requirement of noise statistics information. The two methods were compared using practical measuring data in laser strapdown inertial navigation system （LSINS）/DS integrated system. The simulating results shows that compared with CKF,LSF has better estimation accuracy when noise statistics information is unknown, and the convergence performance of LSF is faster, robustness is better.

关 键 词：组合导航系统 惯导系统 双星系统 最小二乘滤波 

分 类 号：V249.32[航空宇航科学与技术—飞行器设计]