Title :
Tracking of linear time-variant systems
Author :
Haykin, S. ; Sayed, A.H. ; Zeidler, J. ; Yee, P. ; Wei, P.
Author_Institution :
McMaster Univ., Hamilton, Ont., Canada
Abstract :
In this paper we exploit the one-to-one correspondences between the recursive least-squares (RLS) and Kalman variables to formulate extended forms of the RLS algorithm. Two particular forms are considered, one pertaining to a system identification problem and the other to the tracking of a chirped sinusoid in additive noise. For both applications, experiments are presented that demonstrate the tracking optimality of the extended RLS algorithms, compared with the standard RLS and least-mean-squares (LMS) algorithms
Keywords :
adaptive Kalman filters; identification; interference (signal); least squares approximations; optimisation; recursive filters; tracking; Kalman variables; RLS algorithm; additive noise; chirped sinusoid; least-mean-squares algorithms; linear adaptive filtering; linear time-variant systems; recursive least-squares variables; system identification problem; tracking optimality; Adaptive filters; Chirp; Convergence; Eigenvalues and eigenfunctions; Filtering algorithms; Kalman filters; Least squares approximation; Resonance light scattering; System identification; Vectors;
Conference_Titel :
Military Communications Conference, 1995. MILCOM '95, Conference Record, IEEE
Conference_Location :
San Diego, CA
Print_ISBN :
0-7803-2489-7
DOI :
10.1109/MILCOM.1995.483537
