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On one hand, the system completes the individual feature extraction of the signal preamble. On the other hand, it realizes decoding of the transmitter\u2019s individual identity information and generates an individual recognition training data set, on which we can train the recognition network to achieve individual signal recognition. For the collected signals, six parameters were extracted as individual features. To reduce the feature dimensions, a Bessel curve fitting method is used for four of the features. The spatial distribution of the Bezier curve control points after fitting is taken as an individual feature. The processed features are classified with multiple classifiers, and the classification results are fused using the improved Dempster\u2013Shafer evidence theory. Field measurements show that the average individual recognition accuracy of the system reaches 88.3%, which essentially meets the requirements. <\/jats:p>","DOI":"10.1177\/1550147721992626","type":"journal-article","created":{"date-parts":[[2021,2,9]],"date-time":"2021-02-09T11:59:13Z","timestamp":1612871953000},"page":"155014772199262","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["Research on emitter individual identification technology based on Automatic Dependent Surveillance\u2013Broadcast signal"],"prefix":"10.1177","volume":"17","author":[{"given":"Shiwen","family":"Chen","sequence":"first","affiliation":[{"name":"PLA Strategic Support Force Information Engineering University, Zhengzhou, 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