On Filtering and Smoothing Algorithms for Linear State-Space Models Having Quantized Output Data Article Swipe
Angel L. Cedeño
,
Rodrigo A. González
,
Boris I. Godoy
,
Rodrigo Carvajal
,
Juan C. Agüero
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/math11061327
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/math11061327
The problem of state estimation of a linear, dynamical state-space system where the output is subject to quantization is challenging and important in different areas of research, such as control systems, communications, and power systems. There are a number of methods and algorithms to deal with this state estimation problem. However, there is no consensus in the control and estimation community on (1) which methods are more suitable for a particular application and why, and (2) how these methods compare in terms of accuracy, computational cost, and user friendliness. In this paper, we provide a comprehensive overview of the state-of-the-art algorithms to deal with state estimation subject to quantized measurements, and an exhaustive comparison among them. The comparison analysis is performed in terms of the accuracy of the state estimation, dimensionality issues, hyperparameter selection, user friendliness, and computational cost. We consider classical approaches and a new development in the literature to obtain the filtering and smoothing distributions of the state conditioned to quantized data. The classical approaches include the extended Kalman filter/smoother, the quantized Kalman filter/smoother, the unscented Kalman filter/smoother, and the sequential Monte Carlo sampling method, also called particle filter/smoother, with its most relevant variants. We also consider a new approach based on the Gaussian sum filter/smoother. Extensive numerical simulations—including a practical application—are presented in order to analyze the accuracy of the state estimation and the computational cost.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/math11061327
- https://www.mdpi.com/2227-7390/11/6/1327/pdf?version=1678355165
- OA Status
- gold
- Cited By
- 8
- References
- 46
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4323847197
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- OpenAlex ID
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https://openalex.org/W4323847197Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/math11061327Digital Object Identifier
- Title
-
On Filtering and Smoothing Algorithms for Linear State-Space Models Having Quantized Output DataWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-03-09Full publication date if available
- Authors
-
Angel L. Cedeño, Rodrigo A. González, Boris I. Godoy, Rodrigo Carvajal, Juan C. AgüeroList of authors in order
- Landing page
-
https://doi.org/10.3390/math11061327Publisher landing page
- PDF URL
-
https://www.mdpi.com/2227-7390/11/6/1327/pdf?version=1678355165Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.mdpi.com/2227-7390/11/6/1327/pdf?version=1678355165Direct OA link when available
- Concepts
-
Smoothing, Kalman filter, Computer science, Particle filter, Algorithm, Curse of dimensionality, Ensemble Kalman filter, State space, Unscented transform, Gaussian, Extended Kalman filter, Mathematical optimization, Mathematics, Artificial intelligence, Statistics, Quantum mechanics, Computer vision, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
8Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 5, 2023: 2Per-year citation counts (last 5 years)
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46Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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