{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T08:20:18Z","timestamp":1747210818204,"version":"3.40.5"},"reference-count":21,"publisher":"American Institute of Aeronautics and Astronautics (AIAA)","issue":"11","funder":[{"name":"Center for Multi-Intelligence Studies, Naval Postgraduate School"}],"content-domain":{"domain":["arc.aiaa.org"],"crossmark-restriction":true},"short-container-title":["Journal of Aerospace Information Systems"],"published-print":{"date-parts":[[2021,11]]},"abstract":"<jats:p> The recent widespread implementation of Automatic Dependent Surveillance\u2013Broadcasting (ADS-B) systems on aircraft allows for improved monitoring and air traffic control management. As part of this monitoring, it is important to be able to detect unusual flight trajectories due to weather events, detection avoidance, aircraft malfunction, or other activities that may signal anomalous behavior. Given the large volume of ADS-B data available from aircraft around the world, the ability to automatically determine the shape of the trajectory and identify anomalous behavior is important to reduce the need for human identification and labeling. A neural network model is developed for multicategory classification of the shape of the trajectory using features derived from a large ADS-B data set such as bearing and curvature. The results suggest promise in differentiating common trajectory shapes using key factors, with the accuracy of the classifier being comparable to human accuracy. <\/jats:p>","DOI":"10.2514\/1.i010923","type":"journal-article","created":{"date-parts":[[2021,9,13]],"date-time":"2021-09-13T08:50:11Z","timestamp":1631523011000},"page":"762-773","update-policy":"https:\/\/doi.org\/10.2514\/aiaa_crossmarkpolicy","source":"Crossref","is-referenced-by-count":0,"title":["Shape Analysis of Flight Trajectories Using Neural Networks"],"prefix":"10.2514","volume":"18","author":[{"given":"Colton","family":"Gingrass","sequence":"first","affiliation":[{"name":"Naval Postgraduate School, Monterey, California 93943"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4201-4585","authenticated-orcid":false,"given":"Dashi I.","family":"Singham","sequence":"additional","affiliation":[{"name":"Naval Postgraduate School, Monterey, California 93943"}]},{"given":"Michael P.","family":"Atkinson","sequence":"additional","affiliation":[{"name":"Naval Postgraduate School, Monterey, California 93943"}]}],"member":"1387","reference":[{"key":"r3","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2008.927109"},{"key":"r8","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2012.2188391"},{"key":"r9","doi-asserted-by":"publisher","DOI":"10.2514\/1.I010080"},{"key":"r10","doi-asserted-by":"publisher","DOI":"10.2514\/1.I010329"},{"key":"r13","unstructured":"LiL. \u201cAnomaly Detection in Airline Routine Operations Using Flight Data Recorder Data,\u201d Ph.D. Thesis, Massachusetts Inst. of Technology, Cambridge, MA, 2013."},{"key":"r14","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2016.01.007"},{"key":"r15","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2019.11.011"},{"key":"r17","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2011.2160628"},{"key":"r19","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2018.09.024"},{"key":"r20","doi-asserted-by":"publisher","DOI":"10.2514\/1.I010582"},{"key":"r21","doi-asserted-by":"publisher","DOI":"10.2514\/1.I010711"},{"key":"r27","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2008.2005603"},{"key":"r28","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2013.04.007"},{"key":"r29","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2018.10.021"},{"key":"r31","unstructured":"GuoY.XuQ.YangY.LiangS.LiuY.SbertM. \u201cAnomaly Detection Based on Trajectory Analysis Using Kernel Density Estimation and Information Bottleneck Techniques,\u201d Univ. of Girona TR 108, Girona, Spain, 2014."},{"key":"r32","doi-asserted-by":"publisher","DOI":"10.3390\/e19070323"},{"key":"r34","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2011.2125550"},{"key":"r36","unstructured":"GingrassC. \u201cClassifying ADS-B Trajectory Shapes Using a Dense Feed-Forward Neural Network,\u201d Master\u2019s Thesis, Naval Postgraduate School, Monterey, CA, 2020."},{"key":"r37","doi-asserted-by":"publisher","DOI":"10.2514\/1.I010520"},{"key":"r39","doi-asserted-by":"publisher","DOI":"10.2307\/121074"},{"key":"r41","doi-asserted-by":"publisher","DOI":"10.1198\/004017006000000453"}],"container-title":["Journal of Aerospace Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/arc.aiaa.org\/doi\/pdf\/10.2514\/1.I010923","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,14]],"date-time":"2023-07-14T17:22:50Z","timestamp":1689355370000},"score":1,"resource":{"primary":{"URL":"https:\/\/arc.aiaa.org\/doi\/10.2514\/1.I010923"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11]]},"references-count":21,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2021,11]]}},"alternative-id":["10.2514\/1.I010923"],"URL":"https:\/\/doi.org\/10.2514\/1.i010923","relation":{},"ISSN":["1940-3151","2327-3097"],"issn-type":[{"type":"print","value":"1940-3151"},{"type":"electronic","value":"2327-3097"}],"subject":[],"published":{"date-parts":[[2021,11]]},"assertion":[{"value":"2020-10-21","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-05-26","order":1,"name":"revised","label":"Revised","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-08-09","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-09-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}