Performance and stochastic stability of the adaptive fading extended Kalman filter with the matrix forgetting factor

Cenker Biçer, Levent Özbek, and Hasan Erbay. "Performance and stochastic stability of the adaptive fading extended Kalman filter with the matrix forgetting factor." Open Mathematics 14.1 (2016): 934-945. <http://eudml.org/doc/287135>.

@article{CenkerBiçer2016,
abstract = {In this paper, the stability of the adaptive fading extended Kalman filter with the matrix forgetting factor when applied to the state estimation problem with noise terms in the non–linear discrete–time stochastic systems has been analysed. The analysis is conducted in a similar manner to the standard extended Kalman filter’s stability analysis based on stochastic framework. The theoretical results show that under certain conditions on the initial estimation error and the noise terms, the estimation error remains bounded and the state estimation is stable. The importance of the theoretical results and the contribution to estimation performance of the adaptation method are demonstrated interactively with the standard extended Kalman filter in the simulation part.},
author = {Cenker Biçer, Levent Özbek, Hasan Erbay},
journal = {Open Mathematics},
keywords = {Kalman filter; Non-linear systems; Stability; Adaptive filtering; non-linear systems; stability; adaptive filtering},
language = {eng},
number = {1},
pages = {934-945},
title = {Performance and stochastic stability of the adaptive fading extended Kalman filter with the matrix forgetting factor},
url = {http://eudml.org/doc/287135},
volume = {14},
year = {2016},
}

TY - JOUR
AU - Cenker Biçer
AU - Levent Özbek
AU - Hasan Erbay
TI - Performance and stochastic stability of the adaptive fading extended Kalman filter with the matrix forgetting factor
JO - Open Mathematics
PY - 2016
VL - 14
IS - 1
SP - 934
EP - 945
AB - In this paper, the stability of the adaptive fading extended Kalman filter with the matrix forgetting factor when applied to the state estimation problem with noise terms in the non–linear discrete–time stochastic systems has been analysed. The analysis is conducted in a similar manner to the standard extended Kalman filter’s stability analysis based on stochastic framework. The theoretical results show that under certain conditions on the initial estimation error and the noise terms, the estimation error remains bounded and the state estimation is stable. The importance of the theoretical results and the contribution to estimation performance of the adaptation method are demonstrated interactively with the standard extended Kalman filter in the simulation part.
LA - eng
KW - Kalman filter; Non-linear systems; Stability; Adaptive filtering; non-linear systems; stability; adaptive filtering
UR - http://eudml.org/doc/287135
ER -