State Estimation for Large-Scale Systems Based on Reduced-Order Error-Covariance Propagation

We compare several reduced-order Kalman filters for discrete-time LTI systems based on reduced-order error-covariance propagation. These filters use combinations of balanced model truncation and complementary steady-state covariance compensation. After describing each method, we compare their perfor...

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Bibliographic Details
Published in2007 American Control Conference pp. 5700 - 5705
Main Authors Kim, I.S., Chandrasekar, J., Palanthandalam-Madapusi, H.J., Ridley, A.J., Bernstein, D.S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2007
Subjects
Aerodynamics
Error correction
Filters
Gain measurement
Large-scale systems
Noise measurement
Reduced order systems
Sea measurements
State estimation
Steady-state
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Summary:We compare several reduced-order Kalman filters for discrete-time LTI systems based on reduced-order error-covariance propagation. These filters use combinations of balanced model truncation and complementary steady-state covariance compensation. After describing each method, we compare their performance through numerical studies using a compartmental model example. These methods are aimed at large-scale data-assimilation problems where reducing computational complexity is critical.
ISBN:9781424409884
1424409888
ISSN:0743-1619
2378-5861
DOI:10.1109/ACC.2007.4282477