Noise-Robust radar HRRP target recognition based on transformer and Fourier neural operator

Noise-Robust radar HRRP target recognition based on transformer and Fourier neural operator | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 12 July 2025 V1 Latest version Share on Noise-Robust radar HRRP target recognition based on transformer and Fourier neural operator Authors : Tianze Ying 0009-0008-1644-7567 , Mian Pan [email protected] , Zhonghua He , and Yingying Pan Authors Info & Affiliations https://doi.org/10.22541/au.175231307.73566004/v1 246 views 120 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract To address the issue of insufficient model recognition performance under low signal-to-noise ratio (SNR) conditions in the field of radar high-resolution range profile (HRRP) target recognition, this paper proposes a radar HRRP recognition model that combines transformer and Fourier neural operator (FNO). The transformer architecture is used to enhance the model’s global feature perception ability for HRRP, while FNO weakens the influence of noise-interfered high-frequency features on the model through a high-frequency truncation mechanism, ultimately enhancing the model’s noise robustness for low SNR data. Experiments based on real measured data show that the proposed method improves the recognition accuracy for HRRP with SNR below 10dB by more than 10% compared to existing methods, and maintains an advantage when dealing with high SNR data. Supplementary Material File (main document.docx) Download 188.34 KB Information & Authors Information Version history V1 Version 1 12 July 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords artificial intelligence radar signal processing radar target recognition Authors Affiliations Tianze Ying 0009-0008-1644-7567 Hangzhou Dianzi University View all articles by this author Mian Pan [email protected] Hangzhou Dianzi University View all articles by this author Zhonghua He Zhejiang Provincial Climate Center View all articles by this author Yingying Pan Zhejiang Provincial Emergency Management Aviation Rescue Center View all articles by this author Metrics & Citations Metrics Article Usage 246 views 120 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Tianze Ying, Mian Pan, Zhonghua He, et al. Noise-Robust radar HRRP target recognition based on transformer and Fourier neural operator. Authorea . 12 July 2025. DOI: https://doi.org/10.22541/au.175231307.73566004/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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