A moment-based Kalman filtering approach for estimation in ensemble systems Article Swipe
André Luiz P. de Lima
,
Jr-Shin Li
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1063/5.0200614
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1063/5.0200614
A persistent challenge in tasks involving large-scale dynamical systems, such as state estimation and error reduction, revolves around processing the collected measurements. Frequently, these data suffer from the curse of dimensionality, leading to increased computational demands in data processing methodologies. Recent scholarly investigations have underscored the utility of delineating collective states and dynamics via moment-based representations. These representations serve as a form of sufficient statistics for encapsulating collective characteristics, while simultaneously permitting the retrieval of individual data points. In this paper, we reshape the Kalman filter methodology, aiming its application in the moment domain of an ensemble system and developing the basis for moment ensemble noise filtering. The moment system is defined with respect to the normalized Legendre polynomials, and it is shown that its orthogonal basis structure introduces unique benefits for the application of Kalman filter for both i.i.d. and universal Gaussian disturbances. The proposed method thrives from the reduction in problem dimension, which is unbounded within the state-space representation, and can achieve significantly smaller values when converted to the truncated moment-space. Furthermore, the robustness of moment data toward outliers and localized inaccuracies is an additional positive aspect of this approach. The methodology is applied for an ensemble of harmonic oscillators and units following aircraft dynamics, with results showcasing a reduction in both cumulative absolute error and covariance with reduced calculation cost due to the realization of operations within the moment framework conceived.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1063/5.0200614
- https://pubs.aip.org/aip/cha/article-pdf/doi/10.1063/5.0200614/19974250/063107_1_5.0200614.pdf
- OA Status
- bronze
- Cited By
- 1
- References
- 30
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399284352
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4399284352Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1063/5.0200614Digital Object Identifier
- Title
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A moment-based Kalman filtering approach for estimation in ensemble systemsWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-06-01Full publication date if available
- Authors
-
André Luiz P. de Lima, Jr-Shin LiList of authors in order
- Landing page
-
https://doi.org/10.1063/5.0200614Publisher landing page
- PDF URL
-
https://pubs.aip.org/aip/cha/article-pdf/doi/10.1063/5.0200614/19974250/063107_1_5.0200614.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
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https://pubs.aip.org/aip/cha/article-pdf/doi/10.1063/5.0200614/19974250/063107_1_5.0200614.pdfDirect OA link when available
- Concepts
-
Kalman filter, Moment (physics), Dimensionality reduction, Ensemble Kalman filter, Computer science, Robustness (evolution), Covariance, Mathematics, Algorithm, Artificial intelligence, Extended Kalman filter, Statistics, Physics, Classical mechanics, Chemistry, Gene, BiochemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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30Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.to | 32, 114, 169, 224 |
| abstract_inverted_index.we | 81 |
| abstract_inverted_index.The | 107, 144, 192 |
| abstract_inverted_index.and | 13, 51, 98, 119, 140, 161, 181, 202, 217 |
| abstract_inverted_index.can | 162 |
| abstract_inverted_index.due | 223 |
| abstract_inverted_index.for | 65, 102, 131, 137, 196 |
| abstract_inverted_index.its | 88, 124 |
| abstract_inverted_index.the | 19, 27, 45, 72, 83, 91, 100, 115, 132, 149, 158, 170, 174, 225, 230 |
| abstract_inverted_index.via | 53 |
| abstract_inverted_index.both | 138, 213 |
| abstract_inverted_index.cost | 222 |
| abstract_inverted_index.data | 24, 37, 76, 178 |
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| abstract_inverted_index.from | 26, 148 |
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| abstract_inverted_index.such | 9 |
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| abstract_inverted_index.with | 112, 207, 219 |
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| abstract_inverted_index.error | 14, 216 |
| abstract_inverted_index.noise | 105 |
| abstract_inverted_index.serve | 58 |
| abstract_inverted_index.shown | 122 |
| abstract_inverted_index.state | 11 |
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| abstract_inverted_index.which | 154 |
| abstract_inverted_index.while | 69 |
| abstract_inverted_index.Kalman | 84, 135 |
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| abstract_inverted_index.aiming | 87 |
| abstract_inverted_index.around | 17 |
| abstract_inverted_index.aspect | 188 |
| abstract_inverted_index.domain | 93 |
| abstract_inverted_index.filter | 85, 136 |
| abstract_inverted_index.i.i.d. | 139 |
| abstract_inverted_index.method | 146 |
| abstract_inverted_index.moment | 92, 103, 108, 177, 231 |
| abstract_inverted_index.paper, | 80 |
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| abstract_inverted_index.suffer | 25 |
| abstract_inverted_index.system | 97, 109 |
| abstract_inverted_index.toward | 179 |
| abstract_inverted_index.unique | 129 |
| abstract_inverted_index.values | 166 |
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| abstract_inverted_index.achieve | 163 |
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| abstract_inverted_index.defined | 111 |
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| abstract_inverted_index.reduced | 220 |
| abstract_inverted_index.reshape | 82 |
| abstract_inverted_index.respect | 113 |
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| abstract_inverted_index.smaller | 165 |
| abstract_inverted_index.thrives | 147 |
| abstract_inverted_index.utility | 46 |
| abstract_inverted_index.Gaussian | 142 |
| abstract_inverted_index.Legendre | 117 |
| abstract_inverted_index.absolute | 215 |
| abstract_inverted_index.aircraft | 205 |
| abstract_inverted_index.benefits | 130 |
| abstract_inverted_index.dynamics | 52 |
| abstract_inverted_index.ensemble | 96, 104, 198 |
| abstract_inverted_index.harmonic | 200 |
| abstract_inverted_index.outliers | 180 |
| abstract_inverted_index.positive | 187 |
| abstract_inverted_index.proposed | 145 |
| abstract_inverted_index.revolves | 16 |
| abstract_inverted_index.systems, | 8 |
| abstract_inverted_index.approach. | 191 |
| abstract_inverted_index.challenge | 2 |
| abstract_inverted_index.collected | 20 |
| abstract_inverted_index.converted | 168 |
| abstract_inverted_index.dynamical | 7 |
| abstract_inverted_index.dynamics, | 206 |
| abstract_inverted_index.following | 204 |
| abstract_inverted_index.framework | 232 |
| abstract_inverted_index.increased | 33 |
| abstract_inverted_index.involving | 5 |
| abstract_inverted_index.localized | 182 |
| abstract_inverted_index.reduction | 150, 211 |
| abstract_inverted_index.retrieval | 73 |
| abstract_inverted_index.scholarly | 41 |
| abstract_inverted_index.structure | 127 |
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| abstract_inverted_index.unbounded | 156 |
| abstract_inverted_index.universal | 141 |
| abstract_inverted_index.additional | 186 |
| abstract_inverted_index.collective | 49, 67 |
| abstract_inverted_index.conceived. | 233 |
| abstract_inverted_index.covariance | 218 |
| abstract_inverted_index.cumulative | 214 |
| abstract_inverted_index.developing | 99 |
| abstract_inverted_index.dimension, | 153 |
| abstract_inverted_index.estimation | 12 |
| abstract_inverted_index.filtering. | 106 |
| abstract_inverted_index.individual | 75 |
| abstract_inverted_index.introduces | 128 |
| abstract_inverted_index.normalized | 116 |
| abstract_inverted_index.operations | 228 |
| abstract_inverted_index.orthogonal | 125 |
| abstract_inverted_index.permitting | 71 |
| abstract_inverted_index.persistent | 1 |
| abstract_inverted_index.processing | 18, 38 |
| abstract_inverted_index.reduction, | 15 |
| abstract_inverted_index.robustness | 175 |
| abstract_inverted_index.showcasing | 209 |
| abstract_inverted_index.statistics | 64 |
| abstract_inverted_index.sufficient | 63 |
| abstract_inverted_index.Frequently, | 22 |
| abstract_inverted_index.application | 89, 133 |
| abstract_inverted_index.calculation | 221 |
| abstract_inverted_index.delineating | 48 |
| abstract_inverted_index.large-scale | 6 |
| abstract_inverted_index.methodology | 193 |
| abstract_inverted_index.oscillators | 201 |
| abstract_inverted_index.realization | 226 |
| abstract_inverted_index.state-space | 159 |
| abstract_inverted_index.underscored | 44 |
| abstract_inverted_index.Furthermore, | 173 |
| abstract_inverted_index.inaccuracies | 183 |
| abstract_inverted_index.methodology, | 86 |
| abstract_inverted_index.moment-based | 54 |
| abstract_inverted_index.polynomials, | 118 |
| abstract_inverted_index.computational | 34 |
| abstract_inverted_index.disturbances. | 143 |
| abstract_inverted_index.encapsulating | 66 |
| abstract_inverted_index.measurements. | 21 |
| abstract_inverted_index.moment-space. | 172 |
| abstract_inverted_index.significantly | 164 |
| abstract_inverted_index.investigations | 42 |
| abstract_inverted_index.methodologies. | 39 |
| abstract_inverted_index.simultaneously | 70 |
| abstract_inverted_index.dimensionality, | 30 |
| abstract_inverted_index.representation, | 160 |
| abstract_inverted_index.representations | 57 |
| abstract_inverted_index.characteristics, | 68 |
| abstract_inverted_index.representations. | 55 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.67153001 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |