A New Method of Cognitive Signal Recognition Based on Hybrid Information Entropy and D-S Evidence Theory
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The automatic modulation recognition of communication signal has been widely used in many fields. However, it is very difficult to recognize the modulation in low SNR. Based on information entropy features and Dempster-Shafer evidence theory, a novel automatic modulation recognition methods is proposed in this paper. Firstly, Rényi entropy and singular entropy is used to obtain the modulation feature. Secondly, based on the normal test theory, a novel basic probability assignment function(BPAF) is presented. Finally, Dempster-Shafer evidence theory is used as a classifier. Experiment results indicate that the new approach can obtain a higher recognition result in low SNR.
KeywordsAutomatic Modulation Recognition Information Entropy Dempster-Shafer Evidence Theory Normal Test
This work is supported by the National Natural Science Foundation of China (61771154,61301095), the Key Development Program of Basic Research of China (JCKY2013604B001), the Fundamental Research Funds for the Central Universities (GK2080260148 and HEUCF1508).
We gratefully thank of very useful discussions of reviewers.
Compliance with Ethical Standards
Conflict of Interest
Meantime, all the authors declare that there is no conflict of interests regarding the publication of this article.
- 2.Guo J-j, Hong-dong Y, Lu J, Heng-fang M (2014) Recognition of Digital Modulation Signals via Higher Order Cumulants[J]. Communications Technology 11:1255–1260Google Scholar
- 3.Zhu L, Cheng H-W, Wu L-n (2009) Identification of Digital Modulation Signals Based on Cyclic Spectral Density and Statistical Parameters[J]. J Appl Sci 2:137–143Google Scholar
- 6.Li J (2015) A New Robust Signal Recognition Approach Based on Holder Cloud Features under Varying SNR Environment[J]. KSII Transactions on Internet and Information Systems 9(12):4934–4949 12.31Google Scholar
- 7.Liu S, Pan Z, Cheng X (2017) A Novel Fast Fractal Image Compression Method based on Distance Clustering in High Dimensional Sphere Surface [J]. Fractals, 25(4), 1740004: 1–11Google Scholar
- 10.Li J, Guo J (2015) A New Feature Extraction Algorithm Based on Entropy Cloud Characteristics of Communication Signals[J]. Math Probl Eng 2015:1–8Google Scholar
- 11.He Z-y, Yu-mei C, Qing-quan Q (2005) A Study of Wavelet Entropy Theory and its Application in Electric Power System Fault Detection[J]. Proceedings of the CSEE 5:40–45Google Scholar
- 12.Bo J, Dong X-z, Shen-xing S (2015) Application of approximate entropy to cross-country fault detection in distribution networks[J]. Power System Protection and Control 7:15–21Google Scholar
- 13.Lin-yi Z, Zhi-cheng L, He J-z (2009) Application of Hierarchy-Entropy Combination Assigning Method in Radar Emitter Recognition[J]. Command Control & Simulation 6:27–29Google Scholar
- 14.Jing-chao Li, Yu-long Ying (2014) Radar Signal Recognition Algorithm Based on Entropy Theory[C]. 2014 2nd International Conference on Systems and Informatics, 718–723. https://doi.org/10.1109/ICSAI.2014.7009379
- 16.Hang B, Yong-jun Z, Shen W, Xu Y-g (2013) Radar emitter recognition based on rényi entropy of time-frequency distribution[J]. Journal of Circuits and System 1:437–442Google Scholar
- 17.Rui Z, Si Z, He Z, et al (2015) A joint detection based on the DS evidence theory for multi-user superposition modulation[C]. IEEE International Conference on Network Infrastructure and Digital Content. IEEE, 390–393. https://doi.org/10.1109/ICNIDC.2014.7000331
- 18.Xin Y, Zhu Q (2013) Cooperative Modulation Recognition Method based on Multi-Type Feature Parameters and Improved DS Evidence Theory[J]. J Converg Inf Technol 8(11):258–266Google Scholar
- 21.Luo X, Luo H, Jin-deng Z, Lei L (2012) Error-correcting Output Codes Based on Classifier’ Confidence for Multi-class Classification[J]. Science Technology and Engineering 22:5502–5508Google Scholar
- 22.Wen-sheng DENG, Xiao-mei SHAO, Hai LIU (2017) Discussion of Remote Sensing Image Classification Method Based on Evidence Theory[J]. Journal of Remote Sensing 4:568–573Google Scholar
- 23.Peng-fei NIU, Sheng-da WANG, Jian MA (2007) Radar target recognition based on Subordinate Function and D-S Theory[J]. Microcomputer Information 31:218–220Google Scholar
- 24.Jie X (2006) Fuzzy recognition of airborne radar based on D-S evidence theory[J]. Command Control & Simulation 4:33–36Google Scholar
- 28.Deng X-m, Ying-sheng Z (1964) The introduction of a simple method for the normal test[J]. Chinese School Health 3:167–169Google Scholar
- 29.Xu Pei-da; Deng Yong, Su, Xiao-yan. A new method to determine basic probability assignment from training data[J]. Knowl-Based Syst, 2013, 46(1): 69-80Google Scholar