{"paper_id":"13af06de-5f70-4972-9bc0-ea02d84fbbac","body_text":"Autofocus Method for SAR Based on Dynamically Randomized Block Sampling in Wavenumber Domain | 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 Research Article Autofocus Method for SAR Based on Dynamically Randomized Block Sampling in Wavenumber Domain Xingyu Zhao, Yanheng Ma, Lina Chu, Wei Li, Hanzhi Han This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7228724/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Apr, 2026 Read the published version in Journal on Advances in Signal Processing → Version 1 posted You are reading this latest preprint version Abstract Synthetic Aperture Radar (SAR) systems on small unmanned aerial vehicles (UAVs) are challenged by complex, spatially-variant motion errors, which conventional autofocus methods often fail to adequately correct. Specifically, approaches using fixed sub-block partitioning struggle to adapt to the non-uniform spatial distribution of phase errors, while static sample selection mechanisms lack the flexibility to optimize estimation throughout the iterative process. To overcome these limitations, this paper proposes a novel autofocus algorithm that integrates two key innovations. The first, adaptive slant-range wavenumber sub-block partitioning, dynamically adjusts the division granularity based on the local severity of the phase error, ensuring an optimal trade-off between estimation accuracy and robustness. The second, a sample selection strategy guided by a Dynamic Probability Density Function (DPDF), adaptively modifies sample selection probability during iterations, balancing broad exploration in early stages with focused exploitation of high-quality samples in later stages. Integrated within a fast factorized back-projection (FFBP) framework, the method leverages the coherence between azimuth phase error (APE) and non-systematic range cell migration (NsRCM) for joint estimation and correction. Validation using both simulated and measured UAV SAR data demonstrates that the proposed method significantly enhances focusing quality. synthetic aperture radar motion error autofocus adaptive partitioning dynamic probability density function Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 20 Apr, 2026 Read the published version in Journal on Advances in Signal Processing → Version 1 posted 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-7228724\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":505498316,\"identity\":\"5cf4ab85-d839-47f2-87ab-02ece056f402\",\"order_by\":0,\"name\":\"Xingyu Zhao\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Army Engineering University of PLA\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xingyu\",\"middleName\":\"\",\"lastName\":\"Zhao\",\"suffix\":\"\"},{\"id\":505498317,\"identity\":\"adebc3cf-1e59-4208-94fc-094ca1e62832\",\"order_by\":1,\"name\":\"Yanheng 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