The Attitude Fingerprint Information Analysis of Space Debris with Photometric Light Curves

The Attitude Fingerprint Information Analysis of Space Debris with Photometric Light Curves | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The Attitude Fingerprint Information Analysis of Space Debris with Photometric Light Curves Yan Wen, Dalei Yao, Fan Bu, Yongqing Yang, Rongli Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9273518/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract To address the challenge of directly observing the attitude fingerprint information of space debris, this paper investigates the problem of extracting attitude-related information of space debris from photometric light curves. A framework named Photometric Light Curve Attitude Fingerprint Network (PAFNet) is proposed to link simulated photometric data with data-driven feature analysis. First, a photometric simulation model that incorporates geometric structure, orbital motion, and surface reflection properties is developed to generate light curves under controlled conditions. Second, a feature extraction method combining Shapelets and neural networks is designed to capture both local temporal patterns and global characteristics of the light curves. Finally, an attitude inversion approach based on photometric matching and a genetic algorithm is employed to estimate attitude parameters by comparing observed and simulated light curves. Experimental results show that the proposed method can recover attitude parameters with reasonable accuracy under varying noise levels and limited observation lengths. Additional analysis indicates that smoothing preprocessing and the use of multiple light curves can improve the stability of the inversion results. Physical sciences/Engineering Physical sciences/Mathematics and computing Physical sciences/Optics and photonics Space debris Photometric light curves Attitude fingerprint information Shapelets Neural networks Full Text Additional Declarations No competing interests reported. Supplementary Files dataset.csv Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 22 Apr, 2026 Reviewers agreed at journal 22 Apr, 2026 Reviewers invited by journal 22 Apr, 2026 Editor assigned by journal 22 Apr, 2026 Editor invited by journal 21 Apr, 2026 Submission checks completed at journal 20 Apr, 2026 First submitted to journal 20 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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