A New Framework for Nonlinear Kalman Filters Article Swipe
Shida Jiang
,
Junzhe Shi
,
Scott Moura
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.05717
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.05717
The Kalman filter (KF) is a state estimation algorithm that optimally combines system knowledge and measurements to minimize the mean squared error of the estimated states. While KF was initially designed for linear systems, numerous extensions of it, such as extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), etc., have been proposed for nonlinear systems over the last sixty years. Although different types of nonlinear KFs have different pros and cons, they all use the same framework of linear KF. Yet, according to our theoretical and empirical analysis, the framework tends to give overconfident and less accurate state estimations when the measurement functions are nonlinear. Therefore, in this study, we designed a new framework that can be combined with any existing type of nonlinear KFs and showed theoretically and empirically that the new framework estimates the states and covariance more accurately than the old one. The new framework was tested on four different nonlinear KFs and five different tasks, showcasing its ability to reduce estimation errors by several orders of magnitude in low-measurement-noise conditions. The codes are available at https://github.com/Shida-Jiang/A-new-framework-for-nonlinear-Kalman-filters
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2407.05717
- https://arxiv.org/pdf/2407.05717
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400481092
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4400481092Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2407.05717Digital Object Identifier
- Title
-
A New Framework for Nonlinear Kalman FiltersWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-07-08Full publication date if available
- Authors
-
Shida Jiang, Junzhe Shi, Scott MouraList of authors in order
- Landing page
-
https://arxiv.org/abs/2407.05717Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2407.05717Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2407.05717Direct OA link when available
- Concepts
-
Kalman filter, Moving horizon estimation, Nonlinear system, Extended Kalman filter, Computer science, Control theory (sociology), Artificial intelligence, Physics, Quantum mechanics, Control (management)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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