{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:00:27Z","timestamp":1760144427626,"version":"build-2065373602"},"reference-count":50,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,4,4]],"date-time":"2024-04-04T00:00:00Z","timestamp":1712188800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>This paper discusses using ground-based imagery to determine the attitude of a flying projectile assuming prior knowledge of its external geometry. It presents a segmentation-based approach to follow the object and evaluates it quantitatively with simulated data and qualitatively with both simulated and real data. Two experimental cases are considered: One assumes reliable target distance measurement from an auxiliary range sensor, while the other assumes no range information. The results show that in the case of an unknown projectile\u2013camera distance, with projectile dimensions of 1.378 m and 0.08 m in length and diameter, the estimated distance, in-plane location, and pitch angle accuracies are about 50 m, 0.15 m, and 6 degrees, respectively. Yaw angle estimation is ambiguous. In the second case, assuming that the projectile\u2013camera distance is known resolves the ambiguity of yaw estimation, resulting in accuracies of about 0.15 m, 3 degrees, and 20 degrees for in-plane location, pitch, and yaw angles, respectively. These accuracies were normalized to a 1-km projectile\u2013camera distance.<\/jats:p>","DOI":"10.3390\/info15040201","type":"journal-article","created":{"date-parts":[[2024,4,4]],"date-time":"2024-04-04T06:57:34Z","timestamp":1712213854000},"page":"201","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Flying Projectile Attitude Determination from Ground-Based Monocular Imagery with a Priori Knowledge"],"prefix":"10.3390","volume":"15","author":[{"given":"Huamei","family":"Chen","sequence":"first","affiliation":[{"name":"Intelligent Fusion Technology, Inc., Germantown, MD 20874, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9990-1137","authenticated-orcid":false,"given":"Zhigang","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City University of New York, New York, NY 10031, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6197-0874","authenticated-orcid":false,"given":"Hao","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of Computer Information Systems, Borough of Manhattan Community College, City University of New York, New York, NY 10007, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6894-6108","authenticated-orcid":false,"given":"Erik","family":"Blasch","sequence":"additional","affiliation":[{"name":"Air Force Research Laboratory, Arlington, VA 22203, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Khanh D.","family":"Pham","sequence":"additional","affiliation":[{"name":"Air Force Research Laboratory, Kirtland Air Force Base, Albuquerque, NM 87117, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2656-9380","authenticated-orcid":false,"given":"Genshe","family":"Chen","sequence":"additional","affiliation":[{"name":"Intelligent Fusion Technology, Inc., Germantown, MD 20874, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hinterstoisser, S., Holzer, S., Cagniart, C., Ilic, S., Konolige, K., Navab, N., and Lepetit, V. 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