Two-Layer Nonlinear FIR Filter and Unscented Kalman Filter Fusion With Application to Mobile Robot Localization Article Swipe
Young Eun Kim
,
Hyun Ho Kang
,
Choon Ki Ahn
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/access.2020.2992695
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/access.2020.2992695
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.
Related Topics
Concepts
Control theory (sociology)
Computer science
Kernel adaptive filter
Extended Kalman filter
Alpha beta filter
Filter design
Sensor fusion
Adaptive filter
Kalman filter
Filter (signal processing)
Ensemble Kalman filter
Nonlinear filter
Unscented transform
Invariant extended Kalman filter
Algorithm
Mathematics
Artificial intelligence
Computer vision
Control (management)
Moving horizon estimation
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2020.2992695
- https://ieeexplore.ieee.org/ielx7/6287639/8948470/09086455.pdf
- OA Status
- gold
- Cited By
- 7
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3022776293
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3022776293Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/access.2020.2992695Digital Object Identifier
- Title
-
Two-Layer Nonlinear FIR Filter and Unscented Kalman Filter Fusion With Application to Mobile Robot LocalizationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-01-01Full publication date if available
- Authors
-
Young Eun Kim, Hyun Ho Kang, Choon Ki AhnList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2020.2992695Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/6287639/8948470/09086455.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://ieeexplore.ieee.org/ielx7/6287639/8948470/09086455.pdfDirect OA link when available
- Concepts
-
Control theory (sociology), Computer science, Kernel adaptive filter, Extended Kalman filter, Alpha beta filter, Filter design, Sensor fusion, Adaptive filter, Kalman filter, Filter (signal processing), Ensemble Kalman filter, Nonlinear filter, Unscented transform, Invariant extended Kalman filter, Algorithm, Mathematics, Artificial intelligence, Computer vision, Control (management), Moving horizon estimationTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 3, 2022: 1, 2021: 2Per-year citation counts (last 5 years)
- References (count)
-
36Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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