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.
Robust $H_{2}$-OFIR Filtering: Improving Tracking of Disturbed Systems Under Initial and Data Errors
IEEE Transactions on Aerospace and Electronic Systems ; 58 , 5 ; 4761-4770
2022-10-01
845310 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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