{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T11:20:02Z","timestamp":1767352802076,"version":"3.48.0"},"reference-count":22,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Fundamental Research Funds for the National Key Laboratory of Unmanned Aerial Vehicle Technology","award":["WRFX-202502"],"award-info":[{"award-number":["WRFX-202502"]}]},{"name":"Science and Technology Program of Xizang Autonomous Region","award":["XZ202403ZY0014"],"award-info":[{"award-number":["XZ202403ZY0014"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>In order to effectively ensure the flight safety of unmanned aerial vehicles (UAVs) and effectively deal with the risk of integrated airspace operation, this study carried out a series of key technology exploration and verification. In terms of data processing, Density-based spatial clustering of applications with noise (DBSCAN) clustering method is used to preprocess the characteristics of UAV automatic dependent surveillance\u2013broadcast (ADS-B) data, effectively purify the data from the source, eliminate the noise and outliers of track data in spatial dimension and spatial-temporal dimension, significantly improve the data quality and standardize the data characteristics, and lay a reliable and high-quality data foundation for subsequent trajectory analysis and prediction. In terms of trajectory prediction, the convolutional neural networks-bidirectional gated recurrent unit (CNN-BiGRU) trajectory prediction model is innovatively constructed, and the integrated intelligent calculation of \u2018prediction-judgment\u2019 is successfully realized. The output of the model can accurately and prospectively judge the conflict situation and conflict degree between any two trajectories, and provide core and direct technical support for trajectory conflict warning. In the aspect of conflict detection, the performance of the model and the effect of conflict detection are fully verified by simulation experiments. By comparing the predicted data of the model with the real track data, it is confirmed that the CNN-BiGRU prediction model has high accuracy and reliability in calculating the distance between aircraft. At the same time, the preset conflict detection method is used for further verification. The results show that there is no conflict risk between the UAV and the manned aircraft in integrated airspace during the full 800 s of terminal area flight. In summary, the trajectory prediction model and conflict detection method proposed in this study provide a key technical guarantee for the construction of an active and accurate integrated airspace security management and control system, and have important application value and reference significance for improving airspace management efficiency and preventing flight conflicts.<\/jats:p>","DOI":"10.3390\/a19010032","type":"journal-article","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T11:11:46Z","timestamp":1767352306000},"page":"32","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Cooperative 4D Trajectory Prediction and Conflict Detection in Integrated Airspace"],"prefix":"10.3390","volume":"19","author":[{"given":"Xin","family":"Ma","sequence":"first","affiliation":[{"name":"Air Traffic Management Institute, Civil Aviation Flight University of China, Guanghan 618307, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1676-9677","authenticated-orcid":false,"given":"Linxin","family":"Zheng","sequence":"additional","affiliation":[{"name":"Air Traffic Management Institute, Civil Aviation Flight University of China, Guanghan 618307, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiajun","family":"Zhao","sequence":"additional","affiliation":[{"name":"AVIC Chengdu Aircraft Design & Research Institute, Chengdu 610091, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuxin","family":"Wu","sequence":"additional","affiliation":[{"name":"Air Traffic Management Institute, Civil Aviation Flight University of China, Guanghan 618307, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.cja.2020.08.033","article-title":"Civil unmanned aircraft system operation in national airspace: A survey from Air Navigation Service Provider perspective","volume":"34","author":"Liu","year":"2021","journal-title":"Chin. 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