Robust H-OFIR Filtering: Improving Tracking of Disturbed Systems Under Initial and Data Errors

In harsh environments, tracking is organized assuming disturbances, initial errors, and data errors that requires robust algorithms. In this article, we develop, in discrete-time state space, a robust a posteriori H_{2} optimal finite impulse response (H_{2}-OFIR) filter of disturbed systems under i...

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Bibliographic Details
Published in:IEEE transactions on aerospace and electronic systems 2022-10, Vol.58 (5), p.4761-4770
Main Authors: Ortega-Contreras, Jorge A., Shmaliy, Yuriy S., Andrade-Lucio, Jose A., Ibarra-Manzano, Oscar G.
Format: Article
Language:English
Subjects:
Finite impulse response filters
Global positioning system (GPS)-based tracking
harsh environments
Kalman filter (KF)
Kalman filters
Linear matrix inequalities
robust <named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX"> H_2</tex-math> </inline-formula> </named-content> optimal finite impulse response (<named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX"> H_2</tex-math> </inline-formula> </named-content>-OFIR) filter
State estimation
Target tracking
Transfer functions
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Summary:In harsh environments, tracking is organized assuming disturbances, initial errors, and data errors that requires robust algorithms. In this article, we develop, in discrete-time state space, a robust a posteriori H_{2} optimal finite impulse response (H_{2}-OFIR) filter of disturbed systems under initial and measurement errors. The derivation is provided using a novel H_{2} finite impulse response (H_{2}-FIR) state estimation approach by minimizing the squared Frobenius norm of the weighted transfer function. The robust H_{2}-OFIR filter is designed for full block error matrices, and its recursive forms are shown for diagonal error matrices. Also presented is the suboptimal H_{2}-FIR filtering algorithm using the linear matrix inequality. It is shown that, in global-positioning-system-based tracking of moving vehicles, the H_{2}-OFIR filter outperforms the Kalman and unbiased FIR filters in terms of accuracy and robustness. Moreover, the ability to operate on short horizons makes the H_{2}-OFIR filter computationally efficient.
ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2022.3155588