Title :
Minimum variance filtering with the linearly constrained inverse QRD-RLS algorithm
Author :
Chern, Shiunn-Jang ; Chang, Chung-Yao
Author_Institution :
Dept. of Electr. Eng., Nat. Sun Yat-Sen Univ., Kaohsiung, Taiwan
Abstract :
A general linearly constrained recursive least squares (RLS) filtering algorithm, based on an inverse QR decomposition, is developed and applied to the minimum variance filtering problem, where the adaptation (or Kalman) gain is evaluated via the Givens rotation. Also, the LS weight vector can be computed without back substitution and achieve fast convergence and good numerical properties. The numerical stability of the proposed method, in terms of constrained drift is emphasized. We show that it outperforms the method using the fast linearly constrained RLS algorithm and its modified version
Keywords :
adaptive Kalman filters; least squares approximations; matrix decomposition; matrix inversion; minimisation; numerical stability; recursive filters; Givens rotation; Kalman gain; LS weight vector; constrained drift; convergence; inverse QR decomposition; inverse QRD-RLS algorithm; linearly constrained algorithm; minimum variance filtering; numerical stability; recursive least squares; Adaptive arrays; Adaptive filters; Filtering algorithms; Kalman filters; Least squares methods; Linear antenna arrays; Numerical stability; Resonance light scattering; Signal processing algorithms; Vectors;
Conference_Titel :
Signal Processing and its Applications, Sixth International, Symposium on. 2001
Conference_Location :
Kuala Lumpur
Print_ISBN :
0-7803-6703-0
DOI :
10.1109/ISSPA.2001.949840