State Estimation for Large-Scale Systems Based on Reduced-Order Error-Covariance Propagation | IEEE Conference Publication | IEEE Xplore

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


Abstract:

We compare several reduced-order Kalman filters for discrete-time LTI systems based on reduced-order error-covariance propagation. These filters use combinations of balan...Show More

Abstract:

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.
Date of Conference: 09-13 July 2007
Date Added to IEEE Xplore: 30 July 2007
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Conference Location: New York, NY, USA

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