Title :
Fast tracking and noise-immunised RLS algorithm based on Kalman filter
Author :
Byung-Eul Jun ; Dong-Jo Park
fDate :
12/5/1996 12:00:00 AM
Abstract :
A new least-squares algorithm based on the Kalman filter is presented. The algorithm has a self-perturbing term added to the covariance matrix, which keeps the gain vector from going infinitely small. It not only has a fast tracking capability, but also is immunised against measurement noise. The effectiveness of the algorithm is confirmed through computer simulations
Keywords :
Kalman filters; covariance matrices; filtering theory; least squares approximations; noise; signal processing; Kalman filter; covariance matrix; fast tracking RLS algorithm; fast tracking capability; gain vector; least-squares algorithm; measurement noise; noise-immunised RLS algorithm; recursive least squares; self-perturbing term;
Journal_Title :
Electronics Letters
DOI :
10.1049/el:19961574
