Constrained Recursive Parameter Estimation for InSAR ARCS

Yuqing Wang; Wietske S. Brouwer; Freek J. Van Leijen; Ramon F. Hanssen

doi:10.1109/IGARSS53475.2024.10642786

Constrained Recursive Parameter Estimation for InSAR ARCS

Yuqing Wang^*, Wietske S. Brouwer, Freek J. Van Leijen, Ramon F. Hanssen

^*Corresponding author for this work

Mathematical Geodesy and Positioning

Research output: Contribution to conference › Paper › peer-review

Abstract

The growing availability of SAR data offers a real-time deformation monitoring opportunity, but data utilization can be inefficient. Our study introduces a mathematical framework using recursive least-squares and the wrapped phase, allowing efficient updates when new data arrives. This method also incorporates prior knowledge about signal smoothness for non-linear displacement estimation. Compared to the batch solution, our recursive approach achieves parameter estimation without storing past measurements while respecting signal smoothness constraints.

Original language	English
Pages	10689-10693
Number of pages	5
DOIs	https://doi.org/10.1109/IGARSS53475.2024.10642786
Publication status	Published - 2024
Event	2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024 - Athens, Greece Duration: 7 Jul 2024 → 12 Jul 2024

Conference

Conference	2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024
Country/Territory	Greece
City	Athens
Period	7/07/24 → 12/07/24

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

InSAR point scatterers
parameter estimation
recursive least-squares
smoothness constraints

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10.1109/IGARSS53475.2024.10642786

Constrained Recursive Parameter Estimation for InSAR ARCSFinal published version, 2.78 MB

Cite this

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title = "Constrained Recursive Parameter Estimation for InSAR ARCS",

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AU - Van Leijen, Freek J.

AU - Hanssen, Ramon F.

N1 - Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

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N2 - The growing availability of SAR data offers a real-time deformation monitoring opportunity, but data utilization can be inefficient. Our study introduces a mathematical framework using recursive least-squares and the wrapped phase, allowing efficient updates when new data arrives. This method also incorporates prior knowledge about signal smoothness for non-linear displacement estimation. Compared to the batch solution, our recursive approach achieves parameter estimation without storing past measurements while respecting signal smoothness constraints.

AB - The growing availability of SAR data offers a real-time deformation monitoring opportunity, but data utilization can be inefficient. Our study introduces a mathematical framework using recursive least-squares and the wrapped phase, allowing efficient updates when new data arrives. This method also incorporates prior knowledge about signal smoothness for non-linear displacement estimation. Compared to the batch solution, our recursive approach achieves parameter estimation without storing past measurements while respecting signal smoothness constraints.

KW - InSAR point scatterers

KW - parameter estimation

KW - recursive least-squares

KW - smoothness constraints

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T2 - 2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024

Y2 - 7 July 2024 through 12 July 2024

ER -

Constrained Recursive Parameter Estimation for InSAR ARCS

Abstract

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Keywords

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