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### 基于多模态特征融合网络的空时分组码识别算法

1. 海军航空大学信息融合研究所，山东 烟台 264001
• 收稿日期:2021-03-09 修回日期:2021-04-12 出版日期:2023-02-01
• 作者简介:
• 张聿远 男，1997年1月生，山西长治人，现为海军航空大学硕士研究生，主要研究方向为空时分组码识别.E-mail: 2932484433@qq.com
闫文君 男，1986年生，山东莱州人，现为海军航空大学副教授，主要研究方向为空时分组码识别.
张立民 男，1966年生，辽宁开原人，现为海军航空大学教授、博士生导师，主要研究方向为卫星信号处理及应用.E-mail: iamzlm@163.com
• 基金资助:
• 国家自然科学基金重大研究计划(91538201);泰山学者工程专项经费(Ts201511020)

### Space-Time Block Code Recognition Algorithm Based on Multi-Modality Features Fusion Network

ZHANG Yu-yuan, YAN Wen-jun, ZHANG Li-min

1. Department of Information Fusion，Naval Astronautical University，Yantai，Shandong 264001，China
• Received:2021-03-09 Revised:2021-04-12 Online:2023-02-01
• Supported by:
• Foundation Item(s):　The National Natural Science Foundation of China(91538201);The Taishan Scholar Special Foundation(Ts201511020)

Abstract:

* Aiming at the problems of the existing algorithms in the process of space-time block code (STBC) recognition, such as high misdiagnosis probability and low recognition efficiency under low signal to noise ratio (SNR), this paper proposes an automatic space-time block code recognition method based on multi-modality feature fusion network (MMFFN). Firstly, on the basis of mapping STBC time-domain samples into one-dimensional feature vectors by merging convolution layers, the dilated convolution at multiple dilation rates is used to extract STBC code features from discontinuous time windows, and the self-extraction of multi-delay features is realized. Then, the multi-sequence feature self-extraction module is constructed to extract the inter-code sequence feature, and the mapping feature types are further extended. Finally, the maximum delay feature of the multi-delay Mosaic layer is extracted as the deep fusion feature, and the residual layer with span connection is added to improve the utilization of fusion feature and realize space-time block code recognition. Simulation results show that the recognition accuracy of the proposed algorithm for 6 types of STBC signals reaches more than 90% under -9dB, which is significantly improved compared with the performance of existing recognition algorithms, and has a strong adaptability to low SNR. The STBC multi-delay feature extraction and fusion method proposed in this paper provides a new idea for the design of deep learning network structure by combining traditional algorithms, and the idea can also be applied to other communication signal recognition fields.