{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:45:53Z","timestamp":1760150753539,"version":"build-2065373602"},"reference-count":51,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,20]],"date-time":"2022-01-20T00:00:00Z","timestamp":1642636800000},"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>An elliptical Hough transform (EHT) algorithm is proposed in the framework of track-before-detect (TBD) for joint detection and tracking of weak exoatmospheric targets. The new approach exploits the fact that when restricted to a two-body problem, the exoatmospheric target often follows an elliptical orbit, and thus the Hough transform integrated with orbital geometry information would have better detection performance. The relationship between the original radar measurements in data space and the elliptical parameters in parameter space is explicitly derived with multiple steps of coordinate transformation. It is found that the data points mapping into the parameter space essentially represent a quartic curve. An EHT-based algorithm is then designed, and orbit planarity is also taken into account to reduce the effect of noise accumulation. The influences of primary and secondary thresholds and the signal-to-noise ratio (SNR) on the detection performance are compared by simulations. Additionally, a real radar tracking dataset from a scientific satellite on 28 May 2017 is used to investigate the efficiency of the method. By adding some imaginary clutter to the raw orbit, the results indicate that it is very effective in detecting the real satellite trajectory in a low signal-to-noise ratio (SNR) environment. The advantage of the new method lies in it can not only simultaneously detect and track weak exoatmospheric targets but also can predict the trajectory by using these available detected parameters.<\/jats:p>","DOI":"10.3390\/rs14030491","type":"journal-article","created":{"date-parts":[[2022,1,20]],"date-time":"2022-01-20T22:51:06Z","timestamp":1642719066000},"page":"491","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Detection and Tracking of Weak Exoatmospheric Target with Elliptical Hough Transform"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9805-3028","authenticated-orcid":false,"given":"Bin","family":"Rao","sequence":"first","affiliation":[{"name":"The School of Electronics and Communication Engineering, Sun Yat-sen University, Shenzhen 518107, China"}]},{"given":"Yongkun","family":"Zhou","sequence":"additional","affiliation":[{"name":"The School of Electronics and Communication Engineering, Sun Yat-sen University, Shenzhen 518107, China"}]},{"given":"Yuanping","family":"Nie","sequence":"additional","affiliation":[{"name":"The National Key Laboratory of Science and Technology on Information System Security, Beijing 100101, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"15372","DOI":"10.1109\/JIOT.2021.3064376","article-title":"An efficient strategy for accurate detection and localization of UAV swarms","volume":"8","author":"Zheng","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5059","DOI":"10.1109\/TII.2020.3015730","article-title":"Accurate detection and localization of unmanned aerial vehicle swarms-enabled mobile edge computing system","volume":"7","author":"Zheng","year":"2021","journal-title":"IEEE Trans. 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