Title :
Multi-model self-tuning weighted fusion Kalman filter
Author :
Wenqiang Liu ; Guili Tao
Author_Institution :
Comput. & Inf. Eng. Coll., Heilongjiang Univ. of Sci. & Technol., Harbin, China
fDate :
May 31 2014-June 2 2014
Abstract :
For the multisensor single channel autoregressive moving average (ARMA) signal with a white measurement noise and autoregressive (AR) colored measurement noises as common disturbance noises, when the model parameters and noise statistics are partially unknown, a self-tuning weighted fusion Kalman filter is presented based on classical Kalman filter method. The local estimates are obtained by applying the recursive instrumental variable (RIV) and correlation method. The fused estimates are obtained by taking the average of all corresponding local estimates. Then the optimal weighted fusion Kalman filter is obtained by substituting all the fusion estimates into the corresponding optimal Kalman filter. A simulation example shows its effectiveness.
Keywords :
Kalman filters; autoregressive moving average processes; white noise; RIV; autoregressive colored measurement noise; correlation method; multimodel self-tuning weighted fusion Kalman filter; multisensor single channel autoregressive moving average signal; recursive instrumental variable; white measurement noise; Autoregressive processes; Equations; Kalman filters; Mathematical model; Noise; Noise measurement; Weight measurement; Identification; Multisensor Information Fusion; RIV Algorithm; Self-tuning Kalman Filter;
Conference_Titel :
Control and Decision Conference (2014 CCDC), The 26th Chinese
Conference_Location :
Changsha
Print_ISBN :
978-1-4799-3707-3
DOI :
10.1109/CCDC.2014.6852693