Title :
Innovation-based adaptive Kalman filter derivation
Author :
Fokin, Leonid A. ; Shchipitsyn, Anatoly G.
Author_Institution :
South Ural State Univ., Chelyabinsk
Abstract :
In this paper we develop innovation-based adaptive Kalman filter (IAKF) based on linear time-varying (LTV) state-space model. Thorough argumentation of process and measurement noise covariance adaptation based on innovation sequence covariance is provided. As a result of implementing the proposed methods the process noise and measurement noise covariance matrices are adapted within a sliding window of a fixed depth.
Keywords :
adaptive Kalman filters; covariance matrices; state-space methods; time-varying filters; IAKF; innovation-based adaptive Kalman filter; linear time-varying state-space model; measurement noise covariance adaptation; sliding window; Adaptive control; Communication system control; Covariance matrix; Extraterrestrial measurements; Geophysical measurements; Navigation; Noise measurement; Programmable control; Sea measurements; Technological innovation; Adaptive Kalman filter; Frobenius equation; correlation time adjustment; innovation sequence; maximum-likelihood; noise covariance adaptation; sliding window;
Conference_Titel :
Control and Communications, 2009. SIBCON 2009. International Siberian Conference on
Conference_Location :
Tomsk
Print_ISBN :
978-1-4244-2007-0
DOI :
10.1109/SIBCON.2009.5044877
