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基于深度学习的无人机数据链信噪比估计算法

孙宇航 曾国奇 刘春辉 张多纳

孙宇航, 曾国奇, 刘春辉, 等 . 基于深度学习的无人机数据链信噪比估计算法[J]. 北京航空航天大学学报, 2019, 45(9): 1855-1863. doi: 10.13700/j.bh.1001-5965.2018.0724
引用本文: 孙宇航, 曾国奇, 刘春辉, 等 . 基于深度学习的无人机数据链信噪比估计算法[J]. 北京航空航天大学学报, 2019, 45(9): 1855-1863. doi: 10.13700/j.bh.1001-5965.2018.0724
SUN Yuhang, ZENG Guoqi, LIU Chunhui, et al. SNR estimation algorithm for UAV data link based on deep learning[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(9): 1855-1863. doi: 10.13700/j.bh.1001-5965.2018.0724(in Chinese)
Citation: SUN Yuhang, ZENG Guoqi, LIU Chunhui, et al. SNR estimation algorithm for UAV data link based on deep learning[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(9): 1855-1863. doi: 10.13700/j.bh.1001-5965.2018.0724(in Chinese)

基于深度学习的无人机数据链信噪比估计算法

doi: 10.13700/j.bh.1001-5965.2018.0724
基金项目: 

国防基础科研计划 JCKY2017601C006

详细信息
    作者简介:

    孙宇航  男, 硕士研究生。主要研究方向:电磁信号智能感知方法

    曾国奇  男, 博士, 高级实验师, 硕士生导师。主要研究方向:无人机遥测系统、相控阵天线

    刘春辉  男, 博士, 工程师。主要研究方向:电磁信号智能感知方法

    张多纳  男, 博士研究生。主要研究方向:电磁信号智能感知方法

    通讯作者:

    曾国奇, E-mail: zengguoqi@buaa.edu.cn

  • 中图分类号: TN911.6

SNR estimation algorithm for UAV data link based on deep learning

Funds: 

National Defense Basic Research Program JCKY2017601C006

More Information
  • 摘要:

    无人机数据链通信受到各种自然与人为的干扰,信噪比(SNR)是信道状态和通信质量的有效评估指标。为解决传统估计算法信噪比估计精度不足的问题,提出了一种卷积神经网络(CNN)与长短时记忆(LSTM)网络结合的估计模型。利用仿真与实测相结合的方式,构建了一个包含不同信噪比、调制方式、衰落信道等信息的无人机通信信号数据集;在网络训练阶段,将样本序列进行分割,对分割后的每一部分序列使用CNN-LSTM网络提取深度特征,多次训练并保存模型参数;在测试阶段,利用构建好的测试集完成对算法的验证与测试,得到信噪比估计值。实验表明,相比于传统信噪比估计算法与单一网络结构的深度学习算法,所提算法的均方误差最低,实现了对信噪比的高精度估计。

     

  • 图 1  基于CNN-LSTM信噪比估计网络框架

    Figure 1.  Network framework of SNR estimation based on CNN-LSTM

    图 2  基于分割窗的信号数据分割算法示意图

    Figure 2.  Schematic diagram of signal data segmentation algorithm based on split window

    图 3  CNN网络运算流程图

    Figure 3.  Operation flowchart of convolution network

    图 4  不同估计算法精度对比

    Figure 4.  Comparison of accuracy between CNN-LSTM network and traditional algorithms

    图 5  不同网络结构的训练损失变化对比

    Figure 5.  Comparison of training loss change with different network structures

    图 6  不同网络结构的测试精度对比

    Figure 6.  Comparison of test accuracy with different network structures

    图 7  不同码元速率的测试精度对比

    Figure 7.  Comparison of test accuracy with different symbol transmission rates

    图 8  不同分割窗长度对测试精度的影响

    Figure 8.  Effect of split window length on test accuracy

    表  1  通信信号数据集属性

    Table  1.   Attributes of communication signal data set

    序号 字段名 定义
    1 编号 数据样本的顺序
    2 SNR 信噪比
    3 调制类型 信息和载波结合的方式
    4 载波频率 通信信号的工作频段
    5 样本数 样本总共的条数
    6 信号长度 信号的序列长度
    7 码元速率 信道中码元传输的速率
    下载: 导出CSV

    表  2  不同分割窗长度实验结果对比

    Table  2.   Comparison of experimental results of different split window lengths

    分割窗长度 损失MSE
    SNR=-10dB SNR=-5dB SNR=0dB SNR=5dB SNR=10dB SNR=15dB SNR=20dB
    无分割 4.417 1.542 0.911 0.796 0.726 0.423 0.315
    1000 3.547 0.896 0.624 0.441 0.272 0.137 0.095
    500 2.716 0.562 0.211 0.146 0.092 0.063 0.021
    400 2.436 0.446 0.165 0.063 0.065 0.043 0.023
    200 8.811 6.428 3.347 3.574 2.916 4.467 5.324
    100 13.424 11.756 8.815 7.741 7.629 9.945 7.453
    注:最小MSE值由下划线标记。
    下载: 导出CSV
  • [1] 张文秋, 丁文锐, 刘春辉.一种无人机数据链信道选择和功率控制方法[J].北京航空航天大学学报, 2017, 43(3):583-591. https://bhxb.buaa.edu.cn/CN/abstract/abstract14020.shtml

    ZHANG W Q, DING W R, LIU C H.A channel selection and power control method for UAV data link[J].Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(3):583-591(in Chinese). https://bhxb.buaa.edu.cn/CN/abstract/abstract14020.shtml
    [2] PAULUZZI D R, BEAUCIEU N C.A comparison of SNR estimation techniques for the AWGN channel[J].IEEE Transactions on Communications, 2000, 48(10):1681-1691. doi: 10.1109/26.871393
    [3] BOUJELBEN M A, BELLILI F.EM algorithm for non-data-aided SNR estimation of linearly-modulated signals over SIMO channels[C]//Lobal Telecommunications Conference.Piscataway, NJ: IEEE Press, 2009: 4464-4469.
    [4] XIAO H F, SHI Y Q, SU W, et al.An investigation of non-data-aided SNR estimation techniques for analog modulation signals[C]//IEEE Sarnoff Symposium.Piscataway, NJ: IEEE Press, 2010: 351-355.
    [5] ÁLVAREZ-DÍAZ M, LÓPEZ-VALCARCE R, MOSQUERA C.SNR estimation for multilevel constellations using higher-order moments[J].IEEE Transactions on Signal Processing, 2010, 58(3):1515-1526. doi: 10.1109/TSP.2009.2036069
    [6] STEPHENNE A, BELLILI F, AFFES S.Moment-based SNR estimation over linearly-modulated wireless SIMO channels[J].IEEE Transactions on Wireless Communications, 2010, 9(2):714-722. doi: 10.1109/TWC.2010.02.081719
    [7] 华惊宇, 黄清, 滑翰, 等.一种移动环境下的信噪比估计算法及其在多普勒频移估计中的应用[J].通信学报, 2005, 26(5):135-140. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=txxb200505022

    HUA J Y, HUANG Q, HUA H, et al.A SNR estimation algorithm in mobile environment and its application in Doppler shift estimation[J].Transactions of Communications, 2005, 26(5):135-140(in Chinese). http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=txxb200505022
    [8] 彭耿, 黄知涛, 陆凤波, 等.中频通信信号信噪比的快速盲估计[J].电子与信息学报, 2010, 32(1):102-106. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dzkxxk201001019

    PENG G, HUANG Z T, LU F B, et al.Fast blind estimation of signal-to-noise ratio of IF communication signals[J].Journal of Electronics & Information Technology, 2010, 32(1):102-106(in Chinese). http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dzkxxk201001019
    [9] O'SHEA T J, HOYDIS J.An introduction to deep learning for the physical layer[J].IEEE Transactions on Cognitive Communications & Networking, 2017, 3(4):563-575. https://ieeexplore.ieee.org/document/8054694
    [10] ZHANG D N, DING W R, ZHANG B C, et al.Automatic modulation classification based on deep learning for unmanned aerial vehicles[J].Sensors, 2018, 18(3):924-939. doi: 10.3390/s18030924
    [11] O'SHEA T J, ROY T, CLANCY T C.Over the air deep learning based radio signal classification[J].IEEE Journal of Selected Topics in Signal Processing, 2017, 12(1):168-179. https://ieeexplore.ieee.org/document/8267032
    [12] O'SHEA T J, CORGAN J, CLANCY T C.Convolutional radio modulation recognition networks[C]//International Conference on Engineering Applications of Neural Networks.Berlin: Springer, 2016: 213-226.
    [13] LÉCUN Y, BOTTOU L, BENGIO Y, et al.Gradient-based learning applied to document recognition[J].Proceedings of the IEEE, 1998, 86(11):2278-2324. doi: 10.1109/5.726791
    [14] SUN Y, WANG X, TANG X.Hybrid deep learning for face verification[C]//IEEE International Conference on Computer Vision.Piscataway, NJ: IEEE Press, 2013: 1489-1496.
    [15] SAK H, SENIOR A, BEAUFAYS F.Long short-term memory based recurrent neural network architectures for large vocabulary speech recognition[C]//15th Annual Conference of the International Speech Communication Association, 2014: 338-342.
    [16] GHOSH S, VINYALS O, STROPE B, et al.Contextual LSTM (CLSTM) models for large scale NLP tasks[EB/OL].(2016-05-31)[2018-10-15].https://www.researchgate.net/publication/301857393_Contextual_LSTM_CLSTM_models_for_Large_scale_NLP_tasks.
    [17] WANG J, CAO Z W.Chinese text sentiment analysis using LSTM Network based on L2 and Nadam[C]//IEEE International Conference on Communication Technology.Piscataway, NJ: IEEE Press, 2017: 1891-1895.
    [18] JOZEFOWICZ R, ZAREMBA W, SUTSKEVER I.An empirical exploration of recurrent network architectures[C]//Proceeding of the 32nd International Conference on Machine Learning, 2015, 37: 2342-2350.
    [19] GREFF K, SRIVASTAVA R K, KOUTNÍK J, et al.LSTM:A search space odyssey[J].IEEE Transactions on Neural Networks & Learning Systems, 2015, 28(10):2222-2232.
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出版历程
  • 收稿日期:  2018-12-17
  • 录用日期:  2019-01-23
  • 刊出日期:  2019-09-20

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