Estimation of time‐varying signal parameters via VFM Kalman filtering

1999 ◽  
Vol 105 (2) ◽  
pp. 1309-1310
Author(s):  
Sang‐Wook Lee ◽  
Jun‐Seok Lim ◽  
Byung‐Doo Jun ◽  
Koeng‐Mo Sung
Keyword(s):  
Kalman Filtering ◽  
Time Varying ◽  
Signal Parameters ◽  
Estimation Of Time

Observer-Based Fault Diagnosis of Satellite Systems Subject to Time-Varying Thruster Faults

2006 ◽  
Vol 129 (3) ◽  
pp. 352-356 ◽  
Author(s):  
Wen Chen ◽  
Mehrdad Saif
Keyword(s):  
Fault Diagnosis ◽  
Estimation Error ◽  
Fault Estimation ◽  
Time Varying ◽  
Satellite Systems ◽  
Adaptive Observers ◽  
Error Dynamics ◽  
The Stability ◽  
Estimation Of Time ◽  
Identification Strategy

This paper presents a novel fault diagnosis approach in satellite systems for identifying time-varying thruster faults. To overcome the difficulty in identifying time-varying thruster faults by adaptive observers, an iterative learning observer (ILO) is designed to achieve estimation of time-varying faults. The proposed ILO-based fault-identification strategy uses a learning mechanism to perform fault estimation instead of using integrators that are commonly used in classical adaptive observers. The stability of estimation-error dynamics is established and proved. An illustrative example clearly shows that time-varying thruster faults can be accurately identified.

scholarly journals Maximum Likelihood Estimation of Time-Varying Sparsity Level for Dynamic Sparse Signals

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 136687-136701
Author(s):  
Thiruppathirajan S. ◽  
Lakshmi Narayanan R. ◽  
Sreelal S. ◽  
Manoj B. S.
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Likelihood Estimation ◽  
Sparse Signals ◽  
Time Varying ◽  
Sparsity Level ◽  
Estimation Of Time
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