Optimal Designs for Steady-State Kalman Filters

2015 ◽  
pp. 149-157
Author(s):  
Guillaume Sagnol ◽  
Radoslav Harman
Keyword(s):  
Steady State ◽  
Kalman Filters ◽  
Optimal Designs

Closed-Form Approximation for the Steady-State Performance of Second-Order Kalman Filters

2018 ◽  
Vol 25 (2) ◽  
pp. 268-272 ◽  
Author(s):  
Sergi Locubiche-Serra ◽  
Gonzalo Seco-Granados ◽  
Jose A. Lopez-Salcedo
Keyword(s):  
Steady State ◽  
Closed Form ◽  
Kalman Filters ◽  
Second Order ◽  
Closed Form Approximation ◽  
Steady State Performance

scholarly journals Weighted Fusion Robust Steady-State Kalman Filters for Multisensor System with Uncertain Noise Variances

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Wen-Juan Qi ◽  
Peng Zhang ◽  
Zi-Li Deng
Keyword(s):  
Steady State ◽  
Kalman Filters ◽  
Robustness Analysis ◽  
Optimal Estimation ◽  
Lyapunov Equation ◽  
Conservative System ◽  
Minimum Variance ◽  
Upper Bounds ◽  
Worst Case ◽  
Weighted Fusion

A direct approach of designing weighted fusion robust steady-state Kalman filters with uncertain noise variances is presented. Based on the steady-state Kalman filtering theory, using the minimax robust estimation principle and the unbiased linear minimum variance (ULMV) optimal estimation rule, the six robust weighted fusion steady-state Kalman filters are designed based on the worst-case conservative system with the conservative upper bounds of noise variances. The actual filtering error variances of each fuser are guaranteed to have a minimal upper bound for all admissible uncertainties of noise variances. A Lyapunov equation method for robustness analysis is proposed. Their robust accuracy relations are proved. A simulation example verifies their robustness and accuracy relations.

scholarly journals Extracting Steady State Components from Synchrophasor Data Using Kalman Filters

Energies ◽  
2016 ◽  
Vol 9 (5) ◽  
pp. 315 ◽  
Author(s):  
Farhan Mahmood ◽  
Hossein Hooshyar ◽  
Luigi Vanfretti
Keyword(s):  
Steady State ◽  
Kalman Filters

Steady state river quality modeling by sequential extended Kalman filters

1978 ◽  
Vol 14 (1) ◽  
pp. 84-96 ◽  
Author(s):  
David S. Bowles ◽  
William J. Grenney
Keyword(s):  
Steady State ◽  
Kalman Filters ◽  
Extended Kalman Filters ◽  
Quality Modeling ◽  
River Quality

State-Space Model and Kalman Filter Gain Identification by a Kalman Filter of a Kalman Filter

2017 ◽  
Vol 140 (3) ◽  
Author(s):  
Minh Q. Phan ◽  
Francesco Vicario ◽  
Richard W. Longman ◽  
Raimondo Betti
Keyword(s):  
Kalman Filter ◽  
Steady State ◽  
State Space ◽  
Kalman Filters ◽  
State Space Model ◽  
Model Structure ◽  
System State ◽  
Input Output ◽  
Output Data ◽  
Space Model

This paper describes an algorithm that identifies a state-space model and an associated steady-state Kalman filter gain from noise-corrupted input–output data. The model structure involves two Kalman filters where a second Kalman filter accounts for the error in the estimated residual of the first Kalman filter. Both Kalman filter gains and the system state-space model are identified simultaneously. Knowledge of the noise covariances is not required.

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