{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T11:18:01Z","timestamp":1772363881424,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,6,3]],"date-time":"2022-06-03T00:00:00Z","timestamp":1654214400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62171336"],"award-info":[{"award-number":["62171336"]}]},{"name":"National Natural Science Foundation of China","award":["61871305"],"award-info":[{"award-number":["61871305"]}]},{"name":"National Natural Science Foundation of China","award":["61731023"],"award-info":[{"award-number":["61731023"]}]},{"name":"National Natural Science Foundation of China","award":["2021ZDLGY08-02"],"award-info":[{"award-number":["2021ZDLGY08-02"]}]},{"name":"key R&amp;D project of Shaanxi Province","award":["62171336"],"award-info":[{"award-number":["62171336"]}]},{"name":"key R&amp;D project of Shaanxi Province","award":["61871305"],"award-info":[{"award-number":["61871305"]}]},{"name":"key R&amp;D project of Shaanxi Province","award":["61731023"],"award-info":[{"award-number":["61731023"]}]},{"name":"key R&amp;D project of Shaanxi Province","award":["2021ZDLGY08-02"],"award-info":[{"award-number":["2021ZDLGY08-02"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Long-time integration is an effective method for improving the signal\u2013to\u2013noise ratio (SNR) of an echo. However, if the target radar cross-section (RCS) fluctuates over the long integration time, the traditional coherent integration and noncoherent integration methods will produce significant performance losses, making it impossible to achieve a favorable integration performance at low SNRs. This study proposes a new hybrid integration method based on the generalized Radon\u2013Fourier transform (GRFT) and generalized Radon transform (GRT) for targets with which echoes are partially coherent. First, a coherent integration is performed with GRFT within the optimal coherent processing segment using optimal coherent processing segmented matching. Then, the GRT is used for noncoherent integration between the coherent processing sections, and the target motion parameters are obtained through a global search. Compared with the GRFT, GRT, and moving target detection (MTD)-GRT methods, the proposed method applies to targets with arbitrary RCS fluctuations, arbitrary cross-range cells, and cross-Doppler cells, and offers the best detection performance. Finally, both simulation results and measured data processing results demonstrate the effectiveness of the algorithm.<\/jats:p>","DOI":"10.3390\/rs14112695","type":"journal-article","created":{"date-parts":[[2022,6,3]],"date-time":"2022-06-03T10:33:01Z","timestamp":1654252381000},"page":"2695","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Coherent Integration Segment Searching Based GRT-GRFT Hybrid Integration Method for Arbitrary Fluctuating Target"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6784-6444","authenticated-orcid":false,"given":"Zhenghe","family":"Zhang","sequence":"first","affiliation":[{"name":"National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nan","family":"Liu","sequence":"additional","affiliation":[{"name":"National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongning","family":"Hou","sequence":"additional","affiliation":[{"name":"National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shiyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linrang","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1109\/MAES.2017.160142","article-title":"Focus-before-detection radar signal processing: Part I\u2014Challenges and methods","volume":"32","author":"Xu","year":"2017","journal-title":"IEEE Aerosp. 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