摘要
针对非线性系统中不可观测故障参数估计问题,提出基于多重渐消因子强跟踪平方根容积卡尔曼滤波(multiple fading factors strong tracking square-root cubature Kalman filter,MSTSCKF)的状态和参数联合滤波算法。MSTSCKF基于强跟踪滤波器理论框架,通过引入多重渐消因子实时调整增益矩阵,克服平方根容积卡尔曼滤波(square-root cubature Kalman filter,SCKF)在故障参数变化函数未知或者突变时滤波精度下降甚至发散的缺点,并兼具SCKF在非线性拟合精度和数值稳定性等方面的优点。仿真结果表明,相比SCKF和强跟踪无迹卡尔曼滤波(unscented Kalman filter,UKF),本文提出的方法具有更高的估计精度。
For unmeasured fault parameter estimation of nonlinear system, a state and parameter joint estimation algorithm based on multiple fading factors strong tracking square-root cubature Kalman filter （MSTSCKF） is presented. Under the basic theory framework of strong tracking filter, MSTSCKF introduces the multiple fading factors to adjust gain matrix in real time and avoids the problem that square-root cubature Kalman filter （SCKF） decreases in accuracy and even diverges when the changing function of fault parameters is unknown or fault parameters abruptly change. Meanwhile, MSTSCKF combines high nonlinear curve fitting and numerical stability of SCKF. The simulation results indicate that higher estimation accuracy is obtained compared with SCKF and strong tracking unscented Kalman filter （UKF）.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2014年第4期643-649,共7页
Systems Engineering and Electronics
基金
总装院校科技创新工程项目资助课题
关键词
强跟踪滤波
状态和参数联合估计
平方根容积卡尔曼滤波
故障参数
strong tracking filter
state and parameter joint estimation
square root cubature Kalman filter （SCKF）
fault parameter
