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terrain-independent capabilities for seamless monitoring. Specific emitter identification (SEI) leverages the distinctive features of ADS-B data. High data collection and annotation costs, along with limited dataset size, can lead to overfitting during training and low model recognition accuracy. Transfer learning, which does not require source and target domain data to share the same distribution, significantly reduces the sensitivity of traditional models to data volume and distribution. It can also address issues related to the incompleteness and inadequacy of communication emitter datasets. This paper proposes a distributed sensor system based on transfer learning to address the specific emitter identification. Firstly, signal fingerprint features are extracted using a bispectrum transform (BST) to train a convolutional neural network (CNN) preliminarily. Decision fusion is employed to tackle the challenges of the distributed system. Subsequently, a transfer learning strategy is employed, incorporating frozen model parameters, maximum mean discrepancy (MMD), and classification error measures to reduce the disparity between the target and source domains. A hyperbolic space module is introduced before the output layer to enhance the expressive capacity and data information extraction. After iterative training, the transfer learning model is obtained. Simulation results confirm that this method enhances model generalization, addresses the issue of slow convergence, and leads to improved training accuracy.<\/jats:p>","DOI":"10.3390\/rs16122068","type":"journal-article","created":{"date-parts":[[2024,6,7]],"date-time":"2024-06-07T10:43:42Z","timestamp":1717757022000},"page":"2068","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Transfer Learning-Based Specific Emitter Identification for ADS-B over Satellite System"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9872-9710","authenticated-orcid":false,"given":"Mingqian","family":"Liu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Integrated Service Networks, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Yae","family":"Chai","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Integrated Service Networks, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Ming","family":"Li","sequence":"additional","affiliation":[{"name":"Guangxi Intelligent Electromagnetic Spectrum Sensing and Control Research Center of Engineering Technology, Guilin Changhai Development Co., Ltd., Guilin 541001, China"}]},{"given":"Jiakun","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Integrated Service Networks, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Nan","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, Dalian University of Technology, Dalian 116024, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1160","DOI":"10.1109\/LCOMM.2023.3247900","article-title":"Specific emitter identification via contrastive learning","volume":"27","author":"Wu","year":"2023","journal-title":"IEEE Commun. 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