Recursive self-tuning algorithm for adaptive Kalman filtering

1 January 1983

journal article
Published by Institution of Engineering and Technology (IET) in IEE Proceedings D Control Theory and Applications

Vol. 130 (6) , 341-344
https://doi.org/10.1049/ip-d.1983.0056

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 R_s. 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

STATE ESTIMATION
ADAPTIVE KALMAN FILTERING
PARAMETER ESTIMATION
SIGNAL STATE-SPACE MODEL
NOISE STATISTICS
SELF-TUNING ALGORITHM
RECURSIVE ALGORITHM

This publication has 1 reference indexed in Scilit:

The Innovations Process with Applications to Identifications
Published by Elsevier ,1976