In this article, estimates of the Kalman filter (KF) and weighted unbiased finite impulse response (UFIR) filter are fused in discrete time-varying state-space to improve the robustness in uncertain environments associated with industrial applications. The weighted UFIR filter is derived using the Frobenius norm and termed as Frobenius finite impulse response (FFIR) filter. It is confirmed that the FFIR filter has better performance under the uncertainties and errors in the noise statistics, while the KF filter is best when the model and noise are exactly known. Based on a numerical example of a hover system, we show that the FFIR filter is able to outperform the UFIR filter and that the fusion KF/FFIR filter is able to outperform both of them. An experimental verification provided for the drone velocity estimation under the hover operation conditions has proved a better accuracy and robustness of the proposed fusion KF/FFIR filter.
Bibliographical noteFunding Information:
Manuscript received December 12, 2018; revised May 12, 2019 and September 17, 2019; accepted November 12, 2019. Date of publication December 12, 2019; date of current version August 18, 2020. This work was supported in part by “Human Resources Program in Energy Technology” of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea under Grant 20174030201820 and in part by the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning under Grant NRF-2017R1A1A1A05001325, and in part by the National Natural Science Foundation of China under Grant 61973136. (Corresponding author: Choon Ki Ahn.) S. H. You and C. K. Ahn are with the School of Electrical Engineering, Korea University, Seoul 136-701, South Korea (e-mail: email@example.com).
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- Frobenius norm
- Kalman filter (KF)
- fusion filter
- unbiased finite impulse response (FIR) filter
ASJC Scopus subject areas
- Control and Systems Engineering
- Electrical and Electronic Engineering