Title :
Performance analysis of general tracking algorithms
Author :
Guo, Lei ; Ljung, Lennart
Author_Institution :
Inst. of Syst. Sci., Acad. Sinica, Beijing, China
fDate :
8/1/1995 12:00:00 AM
Abstract :
A general family of tracking algorithms for linear regression models is studied. It includes the familiar least mean square gradient approach, recursive least squares, and Kalman filter based estimators. The exact expressions for the quality of the obtained estimates are complicated. Approximate, and easy-to-use, expressions for the covariance matrix of the parameter tracking error are developed. These are applicable over the whole time interval, including the transient, and the approximation error can be explicitly calculated
Keywords :
Kalman filters; adaptive control; adaptive signal processing; covariance matrices; least mean squares methods; parameter estimation; tracking; Kalman filter; adaptive algorithm; approximation error; covariance matrix; least mean square gradient method; linear regression models; parameter tracking error; performance analysis; tracking algorithms; transient; Adaptive algorithm; Approximation error; Covariance matrix; Filters; History; Least squares approximation; Linear regression; Performance analysis; Recursive estimation; Vectors;
Journal_Title :
Automatic Control, IEEE Transactions on
