Modulation classification is a classical topic in the field of signal classification, and is of great significance in various applications including aeronautical communications. However, changes in the features of the signals transmitted through the Rician time-varying channels deteriorate the performance of modulation classification. In this study, a robust moment-based algorithm is proposed to counter the influence of the channels. The statistical correlation of the received samples are utilized for the estimation on the Rician factor. According to the derivation of the relationship between the moments of the transmitted samples and the received samples, the features utilized for classification are compensated mathematically. Finally, decision tree and random forest classifier are presented for classification. The estimation and classification performance of the proposed algorithm are analyzed for aeronautical en-route (EN) and arrival/takeoff (AT) channels through simulations. Meanwhile, real world signals of L-band digital aeronautical communication system (L-DACS1) are utilized for verification. The results show that our algorithm has high classification accuracy and low computational complexity.
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