The power of prediction: spatiotemporal Gaussian process modeling for predictive control in slope-based wavefront sensing Article Swipe
Jalo Nousiainen
,
Juha-Pekka Puska
,
Tapio Helin
,
Nuutti Hyvönen
,
M. Kasper
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2406.18275
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2406.18275
Time-delay error is a significant error source in adaptive optics (AO) systems. It arises from the latency between sensing the wavefront and applying the correction. Predictive control algorithms reduce the time-delay error, providing significant performance gains, especially for high-contrast imaging. However, the predictive controller's performance depends on factors such as the WFS type, the measurement noise, the AO system's geometry, and the atmospheric conditions. This work studies the limits of prediction under different imaging conditions through spatiotemporal Gaussian process models. The method provides a predictive reconstructor that is optimal in the least-squares sense, conditioned on the fixed times series of WFS data and our knowledge of the atmosphere. We demonstrate that knowledge is power in predictive AO control. With an SHS-based extreme AO instrument, perfect knowledge of Frozen Flow evolution (wind and Cn2 profile) leads to a reduction of the residual wavefront phase variance up to a factor of 3.5 compared to a non-predictive approach. If there is uncertainty in the profile or evolution models, the gain is more modest. Still, assuming that only effective wind speed is available (without direction) led to reductions in variance by a factor of 2.3. We also study the value of data for predictive filters by computing the experimental utility for different scenarios to answer questions such as: How many past data frames should the prediction filter consider, and is it always most advantageous to use the most recent data? We show that within the scenarios considered, more data consistently increases prediction accuracy. Further, we demonstrate that given a computational limitation on how many past frames we can use, an optimized selection of $n$ past frames leads to a 10-15% additional improvement in RMS over using the n latest consecutive frames of data.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2406.18275
- https://arxiv.org/pdf/2406.18275
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400104751
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4400104751Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2406.18275Digital Object Identifier
- Title
-
The power of prediction: spatiotemporal Gaussian process modeling for predictive control in slope-based wavefront sensingWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-06-26Full publication date if available
- Authors
-
Jalo Nousiainen, Juha-Pekka Puska, Tapio Helin, Nuutti Hyvönen, M. KasperList of authors in order
- Landing page
-
https://arxiv.org/abs/2406.18275Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2406.18275Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2406.18275Direct OA link when available
- Concepts
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Wavefront, Gaussian process, Predictive power, Model predictive control, Process (computing), Computer science, Gaussian, Control (management), Artificial intelligence, Optics, Physics, Quantum mechanics, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.consider, | 223 |
| abstract_inverted_index.different | 72, 207 |
| abstract_inverted_index.effective | 174 |
| abstract_inverted_index.evolution | 129, 163 |
| abstract_inverted_index.geometry, | 59 |
| abstract_inverted_index.increases | 246 |
| abstract_inverted_index.knowledge | 104, 111, 125 |
| abstract_inverted_index.optimized | 266 |
| abstract_inverted_index.providing | 32 |
| abstract_inverted_index.questions | 211 |
| abstract_inverted_index.reduction | 137 |
| abstract_inverted_index.scenarios | 208, 241 |
| abstract_inverted_index.selection | 267 |
| abstract_inverted_index.wavefront | 20, 141 |
| abstract_inverted_index.Predictive | 25 |
| abstract_inverted_index.Time-delay | 0 |
| abstract_inverted_index.additional | 276 |
| abstract_inverted_index.algorithms | 27 |
| abstract_inverted_index.conditions | 74 |
| abstract_inverted_index.direction) | 180 |
| abstract_inverted_index.especially | 36 |
| abstract_inverted_index.limitation | 256 |
| abstract_inverted_index.prediction | 70, 221, 247 |
| abstract_inverted_index.predictive | 42, 84, 115, 199 |
| abstract_inverted_index.reductions | 183 |
| abstract_inverted_index.time-delay | 30 |
| abstract_inverted_index.atmosphere. | 107 |
| abstract_inverted_index.atmospheric | 62 |
| abstract_inverted_index.conditioned | 93 |
| abstract_inverted_index.conditions. | 63 |
| abstract_inverted_index.consecutive | 285 |
| abstract_inverted_index.considered, | 242 |
| abstract_inverted_index.correction. | 24 |
| abstract_inverted_index.demonstrate | 109, 251 |
| abstract_inverted_index.improvement | 277 |
| abstract_inverted_index.instrument, | 123 |
| abstract_inverted_index.measurement | 54 |
| abstract_inverted_index.performance | 34, 44 |
| abstract_inverted_index.significant | 4, 33 |
| abstract_inverted_index.uncertainty | 158 |
| abstract_inverted_index.advantageous | 229 |
| abstract_inverted_index.consistently | 245 |
| abstract_inverted_index.controller's | 43 |
| abstract_inverted_index.experimental | 204 |
| abstract_inverted_index.computational | 255 |
| abstract_inverted_index.high-contrast | 38 |
| abstract_inverted_index.least-squares | 91 |
| abstract_inverted_index.reconstructor | 85 |
| abstract_inverted_index.non-predictive | 153 |
| abstract_inverted_index.spatiotemporal | 76 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |