Title :
Recursive self-tuning algorithm for adaptive Kalman filtering
Author_Institution :
CIT Alcatel, D¿¿partment de Commutation, Lannion, France
fDate :
11/1/1983 12:00:00 AM
Abstract :
A new recursive algorithm for adaptive Kalman filtering is proposed. The signal state-space model and its noise statistics are assumed to depend on an unknown parameter taking values in a subset [´, ´] of Rs. The parameter is estimated recursively using the gradient of the innovation sequence of the Kalman filter. The unknown parameter is replaced by its current estimate in the Kalman-filtering algorithm. The asymptotic properties of the adaptive Kalman filter are discussed.
Keywords :
Kalman filters; adaptive systems; filtering and prediction theory; parameter estimation; state estimation; adaptive Kalman filtering; noise statistics; parameter estimation; recursive algorithm; self-tuning algorithm; signal state-space model; state estimation;
Journal_Title :
Control Theory and Applications, IEE Proceedings D
DOI :
10.1049/ip-d.1983.0056