Health monitoring for strongly non-linear systems using the Ensemble Kalman filter

2006 ◽  
Vol 13 (1) ◽  
pp. 245-259 ◽  
Author(s):  
R. Ghanem ◽  
G. Ferro
Keyword(s):  
Kalman Filter ◽  
Linear Systems ◽  
Health Monitoring ◽  
Ensemble Kalman Filter ◽  
Non Linear ◽  
Non Linear Systems

An improved fuzzy Kalman filter for state estimation of non-linear systems

2010 ◽  
Vol 41 (5) ◽  
pp. 537-546 ◽  
Author(s):  
Zhijie Zhou ◽  
Changhua Hu ◽  
Maoyin Chen ◽  
Huafeng He ◽  
Bangcheng Zhang
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Linear Systems ◽  
Non Linear ◽  
Non Linear Systems

scholarly journals A Mnemonic Kalman Filter for Non-Linear Systems With Extensive Temporal Dependencies

2020 ◽  
Vol 27 ◽  
pp. 1005-1009 ◽  
Author(s):  
Steffen Jung ◽  
Isabel Schlangen ◽  
Alexander Charlish
Keyword(s):  
Kalman Filter ◽  
Linear Systems ◽  
Non Linear ◽  
Non Linear Systems ◽  
Temporal Dependencies

scholarly journals Robust strong tracking unscented Kalman filter for non‐linear systems with unknown inputs

2022 ◽  
Author(s):  
Xinghua Liu ◽  
Jianwei Guan ◽  
Rui Jiang ◽  
Xiang Gao ◽  
Badong Chen ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Linear Systems ◽  
Unscented Kalman Filter ◽  
Unknown Inputs ◽  
Non Linear ◽  
Non Linear Systems

Holomorphically projective mappings between generalized m-parabolic Kähler manifolds

Filomat ◽  
2017 ◽  
Vol 31 (15) ◽  
pp. 4865-4873 ◽  
Author(s):  
Milos Petrovic
Keyword(s):  
Linear Systems ◽  
Kähler Manifolds ◽  
Kahler Manifolds ◽  
Covariant Derivatives ◽  
Linearly Independent ◽  
Non Linear ◽  
Non Linear Systems ◽  
Curvature Tensors ◽  
Derivatives Of

Generalized m-parabolic K?hler manifolds are defined and holomorphically projective mappings between such manifolds have been considered. Two non-linear systems of PDE?s in covariant derivatives of the first and second kind for the existence of such mappings are given. Also, relations between five linearly independent curvature tensors of generalized m-parabolic K?hler manifolds with respect to these mappings are examined.

Distributed track‐to‐track fusion for non‐linear systems with Gaussian mixture noise

2019 ◽  
Vol 13 (5) ◽  
pp. 740-749 ◽  
Author(s):  
Kelin Lu ◽  
Changyin Sun ◽  
Qien Fu ◽  
Qian Zhu
Keyword(s):  
Linear Systems ◽  
Gaussian Mixture ◽  
Track Fusion ◽  
Non Linear ◽  
Non Linear Systems
Sign in / Sign up

Export Citation Format

Share Document