{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T16:16:25Z","timestamp":1788365785025,"version":"build-2803163510"},"reference-count":28,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T00:00:00Z","timestamp":1730505600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Digital multi-beam synthesis technology is generally used in the on-orbit satellite-based Automatic Dependent Surveillance\u2013Broadcast (ADS-B) system. However, the probability of successfully detecting aircraft with uneven surface distribution is low. An adaptive digital beamforming method is proposed to improve the efficiency of aircraft detection probability. The current method has the problem of long operation time and is not suitable for on-orbit operation. Therefore, this paper proposes an adaptive beamforming method for the ADS-B system based on a fully connected neural network (FCNN). The simulation results show that the calculation time of this method is about 2.6 s when more than 15,000 sets of data are inputted, which is 15\u201380% better than the existing methods. Its detection success probability is 10% higher than those of existing methods, and it has better robustness against large amounts of data.<\/jats:p>","DOI":"10.3390\/s24217065","type":"journal-article","created":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T10:57:20Z","timestamp":1730717840000},"page":"7065","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Adaptive Beamforming for On-Orbit Satellite-Based ADS-B Based on FCNN"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-9599-8014","authenticated-orcid":false,"given":"Yiran","family":"Xiang","sequence":"first","affiliation":[{"name":"College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Songting","family":"Li","sequence":"additional","affiliation":[{"name":"College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lihu","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,2]]},"reference":[{"key":"ref_1","first-page":"2327","article-title":"5S: Design and In-Orbit Demonstration of a Multifunctional Integrated Satellite-based Internet of Things Payload","volume":"11","author":"Chen","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1734","DOI":"10.1109\/LWC.2020.3002914","article-title":"Adversarial Examples on Deep-learning-based ADS-B Spoofing Detection","volume":"9","author":"Shang","year":"2020","journal-title":"IEEE Wirel. 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