{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T06:29:04Z","timestamp":1775543344735,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,3,6]],"date-time":"2022-03-06T00:00:00Z","timestamp":1646524800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","award":["02006370285"],"award-info":[{"award-number":["02006370285"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Airship-based Earth observation is of great significance in many fields such as disaster rescue and environment monitoring. To facilitate efficient observation of high-altitude airships (HAA), a high-quality observation scheduling approach is crucial. This paper considers the scheduling of the imaging sensor and proposes a hierarchical observation scheduling approach based on task clustering (SA-TC). The original observation scheduling problem of HAA is transformed into three sub-problems (i.e., task clustering, sensor scheduling, and cruise path planning) and these sub-problems are respectively solved by three stages of the proposed SA-TC. Specifically, a novel heuristic algorithm integrating an improved ant colony optimization and the backtracking strategy is proposed to address the task clustering problem. The 2-opt local search is embedded into a heuristic algorithm to solve the sensor scheduling problem and the improved ant colony optimization is also implemented to solve the cruise path planning problem. Finally, extensive simulation experiments are conducted to verify the superiority of the proposed approach. Besides, the performance of the three algorithms for solving the three sub-problems are further analyzed on instances with different scales.<\/jats:p>","DOI":"10.3390\/s22052050","type":"journal-article","created":{"date-parts":[[2022,3,6]],"date-time":"2022-03-06T20:40:02Z","timestamp":1646599202000},"page":"2050","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["An Observation Scheduling Approach Based on Task Clustering for High-Altitude Airship"],"prefix":"10.3390","volume":"22","author":[{"given":"Jiawei","family":"Chen","sequence":"first","affiliation":[{"name":"School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2311-6415","authenticated-orcid":false,"given":"Qizhang","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Traffic & Transportation Engineering, Central South University, Changsha 410075, China"},{"name":"Department of Electrical & Computer Engineering, National University of Singapore, Singapore 119260, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1552-9620","authenticated-orcid":false,"given":"Guohua","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Traffic & Transportation Engineering, Central South University, Changsha 410075, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bruno, M.F., Molfetta, M.G., Pratola, L., Mossa, M., Nutricato, R., Morea, A., Nitti, D.O., and Chiaradia, M.T. 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