Research Article
Modulation Pattern Recognition Based on Wavelet Approximate Coefficient Entropy
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@INPROCEEDINGS{10.1007/978-3-030-67514-1_53, author={Xiaoya Zuo and Donghuan Xu and Peng Wang and Rugui Yao and Junjie Yang and Lulu Pan}, title={Modulation Pattern Recognition Based on Wavelet Approximate Coefficient Entropy}, proceedings={IoT as a Service. 6th EAI International Conference, IoTaaS 2020, Xi’an, China, November 19--20, 2020, Proceedings}, proceedings_a={IOTAAS}, year={2021}, month={1}, keywords={Modulation pattern recognition Wavelet approximate coefficient entropy Signal to noise ratio Recognition rate}, doi={10.1007/978-3-030-67514-1_53} }- Xiaoya Zuo
Donghuan Xu
Peng Wang
Rugui Yao
Junjie Yang
Lulu Pan
Year: 2021
Modulation Pattern Recognition Based on Wavelet Approximate Coefficient Entropy
IOTAAS
Springer
DOI: 10.1007/978-3-030-67514-1_53
Abstract
Aiming at the modulation pattern recognition of multiple signals in complex electromagnetic environments, a modulation pattern recognition method based on wavelet approximate coefficient entropy is proposed. Based on the traditional wavelet entropy, an improved wavelet entropy, wavelet approximate coefficient entropy, is proposed, which has strong ability to represent the modulation signal characteristics and has good noise suppression effect. The simulation results verify the correctness of the theoretical analysis, and show that the proposed method can effectively realize the modulation pattern recognition of multiple signals at low signal to noise ratio.
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