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Bias-Constrained H ₂ Optimal Finite Impulse Response Filtering for Object Tracking Under Disturbances and Data Errors.

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Title: Bias-Constrained H ₂ Optimal Finite Impulse Response Filtering for Object Tracking Under Disturbances and Data Errors.
Authors: Pale-Ramon, Eli G.1 (AUTHOR) eg.paleramon@ugto.mx, Shmaliy, Yuriy S.1 (AUTHOR) shmaliy@ugto.mx, Andrade-Lucio, Jose A.1 (AUTHOR) andrade@ugto.mx, Morales-Mendoza, Luis J.2 (AUTHOR) javmorales@uv.mx
Source: IEEE Transactions on Control Systems Technology. Jul2022, Vol. 30 Issue 4, p1782-1789. 8p.
Subjects: Tracking algorithms, Discrete time filters, Finite impulse response filters, Impulse response, Global Positioning System, Linear matrix inequalities, Euler method, Transfer functions
Abstract: The H2 finite impulse response (FIR) filtering approach allows for optimal object tracking under harsh industrial conditions. In this brief, we propose a bias-constrained H2 optimal unbiased FIR (H2-OUFIR) filter for linear discrete time-invariant systems under bounded disturbances, data errors, and initial errors. The H2-OUFIR filter is derived using the backward Euler method by minimizing the squared Frobenius norm of the weighted transfer function. A bias-constrained suboptimal H2 FIR filtering algorithm using a linear matrix inequality (LMI) is also designed. Based on experimental examples of global positioning system (GPS)-based vehicle tracking and video human tracking, it is shown that the batch H2-OUFIR filter operating on short horizons with full error matrices is able to outperform the Kalman, optimal finite impulse response (OFIR), and unbiased finite impulse response (UFIR) filters. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:The H2 finite impulse response (FIR) filtering approach allows for optimal object tracking under harsh industrial conditions. In this brief, we propose a bias-constrained H2 optimal unbiased FIR (H2-OUFIR) filter for linear discrete time-invariant systems under bounded disturbances, data errors, and initial errors. The H2-OUFIR filter is derived using the backward Euler method by minimizing the squared Frobenius norm of the weighted transfer function. A bias-constrained suboptimal H2 FIR filtering algorithm using a linear matrix inequality (LMI) is also designed. Based on experimental examples of global positioning system (GPS)-based vehicle tracking and video human tracking, it is shown that the batch H2-OUFIR filter operating on short horizons with full error matrices is able to outperform the Kalman, optimal finite impulse response (OFIR), and unbiased finite impulse response (UFIR) filters. [ABSTRACT FROM AUTHOR]
ISSN:10636536
DOI:10.1109/TCST.2021.3118321