WindDensity-MBIR: Model-Based Iterative Reconstruction for Wind Tunnel 3D Density Estimation

[2602.16621] WindDensity-MBIR: Model-Based Iterative Reconstruction for Wind Tunnel 3D Density Estimation Skip to main content Search Submit Donate Log in Electrical Engineering and Systems Science > Signal Processing arXiv:2602.16621 (eess) [Submitted on 18 Feb 2026] Title: WindDensity-MBIR: Model-Based Iterative Reconstruction for Wind Tunnel 3D Density Estimation Authors: Karl J. Weisenburger , Gregery T. Buzzard , Charles A. Bouman , Matthew R. Kemnetz View PDF HTML (experimental) Abstract: Experimentalists often use wind tunnels to study aerodynamic turbulence, but most wind tunnel imaging techniques are limited in their ability to take non-invasive 3D density measurements of turbulence. Wavefront tomography is a technique that uses multiple wavefront measurements from various viewing angles to non-invasively measure the 3D density field of a turbulent medium. Existing methods make strong assumptions, such as a spline basis representation, to address the ill-conditioned nature of this problem. We formulate this problem as a Bayesian, sparse-view tomographic reconstruction problem and develop a model-based iterative reconstruction algorithm for measuring the volumetric 3D density field inside a wind tunnel. We call this method WindDensity-MBIR and apply it using simulated data to difficult reconstruction scenarios with sparse data, small projection field of view, and limited angular extent. WindDensity-MBIR can recover high-order features in these scenarios within 10% to 25% error even when the tip, tilt, and piston are removed from the wavefront measurements. Comments: Submitted to the Unconventional Imaging, Sensing, and Adaptive Optics special session of Optical Engineering Subjects: Signal Processing (eess.SP) ; Fluid Dynamics (physics.flu-dyn); Optics (physics.optics) Cite as: arXiv:2602.16621 [eess.SP] (or arXiv:2602.16621v1 [eess.SP] for this version) https://doi.org/10.48550/arXiv.2602.16621 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Opt. Eng. 65(8) 081820 (2026) Related DOI : https://doi.org/10.1117/1.OE.65.8.081820 Focus to learn more DOI(s) linking to related resources Submission history From: Karl Weisenburger [ view email ] [v1] Wed, 18 Feb 2026 17:16:44 UTC (10,896 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: eess.SP < prev | next > new | recent | 2026-02 Change to browse by: eess physics physics.flu-dyn physics.optics References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from (opens in new tab) (opens in new tab) (opens in new tab)

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