{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T04:16:23Z","timestamp":1768450583395,"version":"3.49.0"},"reference-count":40,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T00:00:00Z","timestamp":1726185600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The Doppler-spread problem is commonly encountered in space target observation scenarios using ground-based radar when prolonged coherent integration techniques are utilized. Even when the translational motion is accurately compensated, the phase resulting from changes in the target observation attitude (TOA) still leads to extension of the target\u2019s echo energy across multiple Doppler cells. In particular, as the TOA change undergoes multiple cycles within a coherent processing interval (CPI), the Doppler spectrum spreads into equidistant sparse line spectra, posing a substantial challenge for target detection. Aiming to address such problems, we propose a generalized likelihood ratio test based on overlapping group shrinkage denoising and order statistics (OGSos-GLRT) in this study. First, the Doppler domain signal is denoised according to its equidistant sparse characteristics, allowing for the recovery of Doppler cells where line spectra may be situated. Then, several of the largest Doppler cells are integrated into the GLRT for detection. An analytical expression for the false alarm probability of the proposed detector is also derived. Additionally, a modified OGSos-GLRT method is proposed to make decisions based on an increasing estimated number of line spectra (ENLS), thus increasing the robustness of OGSos-GLRT when the ENLS mismatches the actual value. Finally, Monte Carlo simulations confirm the effectiveness of the proposed detector, even at low signal-to-noise ratios (SNRs).<\/jats:p>","DOI":"10.3390\/rs16183413","type":"journal-article","created":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T11:29:59Z","timestamp":1726226999000},"page":"3413","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Doppler-Spread Space Target Detection Based on Overlapping Group Shrinkage and Order Statistics"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-9428-8331","authenticated-orcid":false,"given":"Linsheng","family":"Bu","sequence":"first","affiliation":[{"name":"School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8826-3048","authenticated-orcid":false,"given":"Tuo","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0138-2149","authenticated-orcid":false,"given":"Defeng","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China"}]},{"given":"Huawei","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China"}]},{"given":"Shuo","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China"}]},{"given":"Jialiang","family":"Han","sequence":"additional","affiliation":[{"name":"School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Lin, B., Wang, J., Wang, H., Zhong, L., Yang, X., and Zhang, X. 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