In this section
@article{Ge:2016:10.1080/00207179.2016.1228123,
author = {Ge, M and Kerrigan, EC},
doi = {10.1080/00207179.2016.1228123},
journal = {International Journal of Control},
pages = {1903--1915},
title = {Noise Covariance Identification for Time-varying and Nonlinear Systems},
url = {http://dx.doi.org/10.1080/00207179.2016.1228123},
volume = {90},
year = {2016}
}
TY - JOUR
AB - Kalman-based state estimators assume a priori knowledge of the covariance matrices of the process and observation noise. However, in most practical situations, noise statistics and initial conditions are often unknown and need to be estimated from measurement data. This paper presents an auto-covariance least-squares-based algorithm for noise and initial state error covariance estimation of large-scale linear time-varying (LTV) and nonlinear systems. Compared to existing auto-covariance least-squares based-algorithms, our method does not involve any approximations for LTV systems, has fewer parameters to determine and is more memory/computationally efficient for large-scale systems. For nonlinear systems, our algorithm uses full information estimation/moving horizon estimation instead of the extended Kalman filter, so that the stability and accuracy of noise covariance estimation for nonlinear systems can be guaranteed or improved, respectively.
AU - Ge,M
AU - Kerrigan,EC
DO - 10.1080/00207179.2016.1228123
EP - 1915
PY - 2016///
SN - 1366-5820
SP - 1903
TI - Noise Covariance Identification for Time-varying and Nonlinear Systems
T2 - International Journal of Control
UR - http://dx.doi.org/10.1080/00207179.2016.1228123
UR - http://hdl.handle.net/10044/1/40609
VL - 90
ER -