基于多模态特征融合网络的空时分组码识别算法

张聿远; 闫文君; 张立民

doi:10.12263/DZXB.20210326

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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

学术论文 | 更新时间：2025-12-08

- 基于多模态特征融合网络的空时分组码识别算法
- Space-Time Block Code Recognition Algorithm Based on Multi-Modality Features Fusion Network
- 电子学报 2023年51卷第2期页码：489-498
- 作者机构：
  
  海军航空大学信息融合研究所，山东烟台 264001
- 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（通讯作者）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.E-mail: wj_yan@foxmail.com" ]
  [ "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" ]
- 基金信息：
  
  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)
- DOI：10.12263/DZXB.20210326
  中图分类号： TN911.7;
- 收稿：2021-03-09，
  
  修回：2021-04-12，
  
  纸质出版：2023-02-25
- 稿件说明：
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张聿远,闫文君,张立民.基于多模态特征融合网络的空时分组码识别算法[J].电子学报,2023,51(02):489-498.

ZHANG Yu-yuan,YAN Wen-jun,ZHANG Li-min.Space-Time Block Code Recognition Algorithm Based on Multi-Modality Features Fusion Network[J].ACTA ELECTRONICA SINICA,2023,51(02):489-498.
张聿远,闫文君,张立民.基于多模态特征融合网络的空时分组码识别算法[J].电子学报,2023,51(02):489-498. DOI： 10.12263/DZXB.20210326.

ZHANG Yu-yuan,YAN Wen-jun,ZHANG Li-min.Space-Time Block Code Recognition Algorithm Based on Multi-Modality Features Fusion Network[J].ACTA ELECTRONICA SINICA,2023,51(02):489-498. DOI： 10.12263/DZXB.20210326.

摘要

针对现有算法在空时分组码（Space-Time Block Code，STBC）识别过程中存在的低信噪比下误判概率高、识别效率低等问题，本文提出了一种基于多模态特征融合网络（Multi-Modality Features Fusion Network，MMFFN）的空时分组码自动识别方法.首先，在合并卷积层将STBC时域样本映射为一维特征向量的基础上，采用多扩张率下的扩张卷积提取非连续时间窗的STBC码内特征，实现多时延特征自提取；然后，构建多时序特征自提取模块以提取码间时序特征，进一步扩展映射特征类型；最后，将多时延拼接层获取的最大时延特征作为深层融合特征，并增加了带跨越连接的残差层以提升融合特征利用率，实现空时分组码识别.仿真实验结果表明，本文算法在-9dB下对6类STBC信号的识别准确率达到了90%以上，较现有识别算法的性能获得了显著提升，对低信噪比有较强的适应性.本文提出的STBC多时延特征提取和融合方法，为结合传统算法设计深度学习网络结构提供了新思路，其思想同样可应用于其他通信信号识别领域.

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.

关键词

Keywords

references

ELDEMERDASH Y A , DOBRE O A , ÖNER M . Signal identification for multiple-antenna wireless systems: Achievements and challenges [J]. IEEE Communications Surveys & Tutorials , 2016 , 18 ( 3 ): 1524 - 1551 .

KARAMI E , DOBRE O A . Identification of SM-OFDM and AL-OFDM signals based on their second-order cyclostationarity [J]. IEEE Transactions on Vehicular Technology , 2015 , 64 ( 3 ): 942 - 953 .

LING Q , ZHANG L , YAN W , et al . Hierarchical space-time block codes signals classification using higher order cumulants [J]. Chinese Journal of Aeronautics , 2016 , 29 ( 3 ): 754 - 762 .

闫文君 , 张立民 , 凌青 , 等 . 基于高阶统计特征的空时分组码盲识别方法 [J]. 电子与信息学报 , 2016 , 38 ( 3 ): 668 - 673 .

YAN W J , ZHANG L M , LING Q , et al . An algorithm for blind classification of space-time block code based on higher-order statistics [J]. Journal of Electronics & Information Technology , 2016 , 38 ( 3 ): 668 - 673 . (in Chinese)

DEHRI B , BESSEGHIER M , DJEBBAR A B , et al . Blind digital modulation classification for STBC-OFDM system in presence of CFO and channels estimation errors [J]. IET Communications , 2019 , 13 ( 17 ): 2827 - 2833 .

KHOSRAVIYANI M , KALBKHANI H , SHAYESTEH M G . Higher order statistics for modulation and STBC recognition in MIMO systems [J]. IET Communications , 2019 , 13 ( 16 ): 2436 - 2446 .

张天琪 , 范聪聪 , 喻盛琪 , 等 . 基于JADE与特征提取的正交/非正交空时分组码盲识别 [J]. 系统工程与电子技术 , 2020 , 42 ( 4 ): 933 - 939 .

ZHANG T Q , FAN C C , YU S Q , et al . Blind recognition of orthogonal and non-orthogonal space-time block codes based on JADE and feature extraction [J]. Systems Engineering and Electronics , 2020 , 42 ( 4 ): 933 - 939 . (in Chinese)

YAN W , LING Q , ZHANG L , et al . Convolutional neural networks for space-time block coding recognition [EB/OL].( 2019-10-09 )[ 2021-03-09 ]. https://arxiv.org/abs/1910.09952v1 https://arxiv.org/abs/1910.09952v1 .

于柯远 , 张立民 , 闫文君 , 等 . 基于深度学习的多STBC盲识别算法 [J/OL]. 系统工程与电子技术 . https://kns.cnki.net/kcms/detail/11.2422.TN.20201014.1326.022.html https://kns.cnki.net/kcms/detail/11.2422.TN.20201014.1326.022.html .

YU K Y , ZHANG L M , YAN W J , et al . Blind recognition algorithm for multi-STBC based on deep learning [J/OL]. Systems Engineering and Electronics . https://kns.cnki.net/kcms/detail/11.2422.TN.20201014.1326.022.html https://kns.cnki.net/kcms/detail/11.2422.TN.20201014.1326.022.html (in Chinese)

张聿远 , 闫文君 , 林冲 , 等 . 利用卷积-循环神经网络的串行序列空时分组码识别方法 [J]. 信号处理 , 2021 , 37 ( 1 ): 19 - 27 .

ZHANG Y Y , YAN W J , LIN C , et al . Serial sequence space-time block code recognition method by using convolutional- recurrent neural networks [J]. Journal of Signal Processing , 2021 , 37 ( 1 ): 19 - 27 . (in Chinese)

O'SHEA T J , CORGAN J , CLANCY T C . Convolutional radio modulation recognition networks [C]// International Conference on Engineering Applications of Neural Networks . Aberdeen : Springer , 2016 : 213 - 226 .

ZHANG Z F , WANG C , GAN C Q , et al . Automatic modulation classification using convolutional neural network with features fusion of SPWVD and BJD [J]. IEEE Transactions on Signal and Information Processing over Networks , 2019 , 5 ( 3 ): 469 - 478 .

ZHENG S L , QI P H , CHEN S C , et al . Fusion methods for CNN-based automatic modulation classification [J]. IEEE Access , 2019 , 7 : 66496 - 66504 .

BU K , HE Y , JING X J , et al . Adversarial transfer learning for deep learning based automatic modulation classification [J]. IEEE Signal Processing Letters , 2020 , 27 : 880 - 884 .

TUNZE G B , HUYNH-THE T , LEE J M , et al . Sparsely connected CNN for efficient automatic modulation recognition [J]. IEEE Transactions on Vehicular Technology , 2020 , 69 ( 12 ): 15557 - 15568 .

ZHAO S Q , WANG W H , ZENG D G , et al . A novel aggregated multi-path extreme gradient boosting approach for radar emitter classification [J/OL]. IEEE Transactions on Industrial Electronics , 2022 , 69 ( 1 ): 703 - 712 . DOI: 10.1109/TIE.2021.3055155 http://dx.doi.org/10.1109/TIE.2021.3055155 .

王传旭 , 薛豪 . 基于GFU和分层LSTM的组群行为识别研究方法 [J]. 电子学报 , 2020 , 48 ( 8 ): 1465 - 1471 .

WANG C X , XUE H . Group activity recognition based on GFU and hierarchical LSTM [J]. Acta Electronica Sinica , 2020 , 48 ( 8 ): 1465 - 1471 . (in Chinese)

王传旭 , 胡小悦 , 孟唯佳 , 等 . 基于多流架构与长短时记忆网络的组群行为识别方法研究 [J]. 电子学报 , 2020 , 48 ( 4 ): 800 - 807 .

WANG C X , HU X Y , MENG W J , et al . Research on group behavior recognition method based on multi-stream architecture and long short-term memory network [J]. Acta Electronica Sinica , 2020 , 48 ( 4 ): 800 - 807 . (in Chinese)

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