Two-Layer Nonlinear FIR Filter and Unscented Kalman Filter Fusion with Application to Mobile Robot Localization

Young Eun Kim; Hyun Ho Kang; Choon Ki Ahn

doi:10.1109/ACCESS.2020.2992695

Two-Layer Nonlinear FIR Filter and Unscented Kalman Filter Fusion with Application to Mobile Robot Localization

Young Eun Kim, Hyun Ho Kang, Choon Ki Ahn

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

Abstract

In this paper, we propose a new state estimator called the two-layer nonlinear finite impulse response (TLNF) filter and adopt this new filter and unscented Kalman filter (UKF) as subfilters to create the fusion TLNF/UK filter. The TLNF filter is constructed with measurements that are redefined by weighting the estimated states acquired through minimizing the cost function based on the Frobenius norm. The efficient iterative form of the TLNF filter is also developed in this paper. Using the fact that the UKF and the TLNF filter each takes a different type of memory structure, the fusion TLNF/UK filter is designed as a robust nonlinear state estimator taking both advantages of each filter. To obtain the best fusion estimates, probabilistic weights are computed based on Bayes' rule and the likelihood of each filter. Both simulation and experimental results for mobile robot indoor localization have shown that the fusion TLNF/UK filter achieves a higher level of accuracy and robustness under practical situations.

Original language	English
Article number	9086455
Pages (from-to)	87173-87183
Number of pages	11
Journal	IEEE Access
Volume	8
DOIs	https://doi.org/10.1109/ACCESS.2020.2992695
Publication status	Published - 2020

Bibliographical note

Funding Information:
This work was supported in part by the National Research Foundation (NRF) of Korea Grant funded by the Korea Government (Ministry of Science and ICT) under Grant NRF-2020R1A2C1005449, and in part by the 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 and Energy, Republic of Korea under Grant 20174030201820.

Publisher Copyright:
© 2013 IEEE.

Keywords

State estimation
finite impulse response (FIR) filter
fusion algorithm
unscented Kalman filter (UKF)

ASJC Scopus subject areas

General Computer Science
General Materials Science
General Engineering
Electrical and Electronic Engineering

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10.1109/ACCESS.2020.2992695

Cite this

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title = "Two-Layer Nonlinear FIR Filter and Unscented Kalman Filter Fusion with Application to Mobile Robot Localization",

abstract = "In this paper, we propose a new state estimator called the two-layer nonlinear finite impulse response (TLNF) filter and adopt this new filter and unscented Kalman filter (UKF) as subfilters to create the fusion TLNF/UK filter. The TLNF filter is constructed with measurements that are redefined by weighting the estimated states acquired through minimizing the cost function based on the Frobenius norm. The efficient iterative form of the TLNF filter is also developed in this paper. Using the fact that the UKF and the TLNF filter each takes a different type of memory structure, the fusion TLNF/UK filter is designed as a robust nonlinear state estimator taking both advantages of each filter. To obtain the best fusion estimates, probabilistic weights are computed based on Bayes' rule and the likelihood of each filter. Both simulation and experimental results for mobile robot indoor localization have shown that the fusion TLNF/UK filter achieves a higher level of accuracy and robustness under practical situations.",

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note = "Funding Information: This work was supported in part by the National Research Foundation (NRF) of Korea Grant funded by the Korea Government (Ministry of Science and ICT) under Grant NRF-2020R1A2C1005449, and in part by the 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 and Energy, Republic of Korea under Grant 20174030201820. Publisher Copyright: {\textcopyright} 2013 IEEE.",

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N2 - In this paper, we propose a new state estimator called the two-layer nonlinear finite impulse response (TLNF) filter and adopt this new filter and unscented Kalman filter (UKF) as subfilters to create the fusion TLNF/UK filter. The TLNF filter is constructed with measurements that are redefined by weighting the estimated states acquired through minimizing the cost function based on the Frobenius norm. The efficient iterative form of the TLNF filter is also developed in this paper. Using the fact that the UKF and the TLNF filter each takes a different type of memory structure, the fusion TLNF/UK filter is designed as a robust nonlinear state estimator taking both advantages of each filter. To obtain the best fusion estimates, probabilistic weights are computed based on Bayes' rule and the likelihood of each filter. Both simulation and experimental results for mobile robot indoor localization have shown that the fusion TLNF/UK filter achieves a higher level of accuracy and robustness under practical situations.

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Two-Layer Nonlinear FIR Filter and Unscented Kalman Filter Fusion with Application to Mobile Robot Localization

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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