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论文:2023,Vol:41,Issue(6):1134-1145 |
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引用本文： |
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张梓轩, 齐子森, 许华, 史蕴豪. 基于自编码器的阵列时变幅相误差校正算法[J]. 西北工业大学学报 |
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ZHANG Zixuan, QI Zisen, XU Hua, SHI Yunhao. Error correction algorithm of array time-varying amplitude and phase based on autoencoder[J]. Journal of Northwestern Polytechnical University |
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| 基于自编码器的阵列时变幅相误差校正算法 |
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| 张梓轩, 齐子森, 许华, 史蕴豪 |
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| 空军工程大学 信息与导航学院, 陕西 西安 710077 |
| 摘要: |
| 随着阵列天线在各类移动平台上的广泛应用,时变幅相误差成为影响阵列信号处理技术工程化应用的重要因素。针对当前时变幅相误差无法有效校正的问题,结合自编码器思想,提出一种基于深度学习的阵列时变幅相误差校正算法。算法充分利用自编码器网络的数据特征提取与重构能力,设计了针对通道时变幅相误差校正的深度学习网络,给出了不含时变幅相误差数据(无扰数据)与含时变幅相误差数据(扰动数据)双驱动下的学习机制,基于期望输出与理想模型均方误差最小化原则,完成了对阵列流形隐匿特征提取,实现了阵列时变幅相误差的有效校正。仿真实验表明,所提算法可有效实现各通道时变幅相误差校正,在通道存在±80%随机时变幅度误差以及±5°随机时变相位误差时,幅度与相位误差校正后的均方差分别在0.5%和1.5%以内,当信噪比大于等于0 dB时,校正后数据的MUSIC算法测向精度与无误差数据基本一致,验证了所提算法的有效性。 |
| 关键词:
阵列校正
时变误差
自编码器
机器学习
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| Error correction algorithm of array time-varying amplitude and phase based on autoencoder |
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| ZHANG Zixuan, QI Zisen, XU Hua, SHI Yunhao |
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| Institute of Telecommunication Engineering, Aire Force Engineering University, Xi'an 710077, China |
| Abstract: |
| As array antennas are widely used in various mobile platforms, the time-varying amplitude and phase error has become an important factor affecting the application of array signal processing technology. A deep learning-based algorithm for the correction of time-varying amplitude and phase errors in arrays is proposed in terms of the idea of autoencoder. The algorithm makes full use of the data feature extraction and reconstruction capability of the autoencoder network, designs a deep learning network for the correction of time-varying amplitude phase error of the channel, gives a double-driven learning mechanism without time-varying amplitude phase error data (unperturbed data) and time-varying amplitude phase error data (perturbed data), completes the extraction of the array stream shape hidden features based on the principle of minimising the mean square error of the desired output and the ideal model. The simulated experiments show that the algorithm can effectively correct the time-varying amplitude and phase errors of each channel, and the mean square error of the corrected amplitude and phase errors are within 0.5% and 1.5% respectively when there are �80% random time-varying amplitude errors and �5� random time-varying phase errors. The effectiveness of the proposed algorithm is verified. |
| Key words:
array correction
time-varying error
autoencoder
machine learning
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| 收稿日期: 2023-01-02
修回日期:
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| DOI: 10.1051/jnwpu/20234161134 |
| 基金项目: 国家自然科学基金(62131020)资助 |
| 通讯作者: 齐子森(1982-),空军工程大学副教授,主要从事通信信号处理研究。e-mail:qizisen@163.com
Email：qizisen@163.com |
| 作者简介: 张梓轩(1994-),空军工程大学硕士研究生,主要从事信息安全与对抗研究。
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参考文献: |
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[1] 平伏龙. 矢量共形阵列DOA与极化参数联合估计算法研究[D]. 成都:电子科技大学, 2016 PING Fulong. Joint DOA and polarization estimation algorithm researching for polarization sensetive conformal array antenna[D]. Chengdu: University of Electronic Science and Technology of China, 2016 (in Chinese)
[2] 姜雅东. 机载相干信源DOA估计与误差校正[D]. 成都:电子科技大学, 2021 JIANG Yadong. DOA estimation of coherent source and array calibration for airborne[D]. Chengdu: University of Electronic Science and Technology of China, 2021 (in Chinese)
[3] 吴迪. 阵列误差校正和波达方向估计研究[D]. 哈尔滨:哈尔滨工程大学, 2015 WU Di. Research on array errors calibration and direction of arrival estimation[D]. Harbin: Harbin Engineering University, 2015 (in Chinese)
[4] LIU Shiyan, ZHANG Zhi, GUO Yu. 2-D DOA estimation with imperfect L-shaped array using active calibration[J]. IEEE Communications Letters, 2021, 25(4): 1178-1182
[5] 张珂, 程菊明, 付进. 阵列通道不一致性误差快速有源校正算法[J]. 电子与信息学报, 2015, 37(9): 2110-2116 ZHANG Ke, CHENG Juming, FU Jin. Fast active error calibration algorithm for array chanel uncertainty[J]. Journal of Electronics & Information Technology, 2015, 37(9): 2110-2116 (in Chinese)
[6] NG B P, LIE J P, ER M H, et al. A practical simple geometry and gain/phase calibration technique for antenna array processing[J]. IEEE Trans on Antennas and Propagation, 2009, 57(7): 1963-1972
[7] 姜祖青, 杨宾. 一种新的多径场景下乘性阵列误差有源校正算法[J]. 信息工程大学学报, 2022, 23(2): 129-134 JIANG Zuqing, YANG Bin. Novel multiplicative array errors active calibration algorithm in presence of multipath[J]. Journal of Information Engineering University, 2022, 23(2): 129-134 (in Chinese)
[8] ZHAO Zheng, TIAN Weiming, DENG Yunkai, et al. Calibration method of array errors for wideband MIMO imaging radar based on multiple prominent targets[J]. Remote Sensin, 2021, 13(15): 2997
[9] 林潇. 极化敏感阵列幅相误差自校正技术研究[D]. 哈尔滨:哈尔滨工业大学, 2020 LIN Xiao. Research on self-correction technology for gain-phase error in polarization-sensitive array[J]. Harbin: Harbin Institute of Technology, 2020 (in Chinese)
[10] LIU H, ZHAO L, LI Y, et al. A sparse-based approach for DOA estimation and array calibration in uniform linear array[J]. IEEE Sensors Journal, 2016, 16(15): 1-5
[11] TUNCER E, FRIEDLANDER B. Classical and modern direction-of-arrival estimation[J]. Applied Acoustics, 2009, 71(5): 493
[12] RUMELHART D E, HINTON G E, WILLIAMS R J. Learning representations by back-propagating errors[J]. Nature, 1986, 323(6088): 533-536
[13] NG A. Sparse autoencoder[J]. CS294A Lecture Notes, 2011, 72(1): 1-19
[14] VINCENT P, LAROCHELLE H, LAJOIE I, et al. Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion[J]. Journal of Machine Learning Research, 2010, 11(12): 3371-3408
[15] RIFAI S, VINCENT P, MULLER X, et al. Contractive auto-encoders: explicit invariance during feature extraction[C]//Proceedings of the 28th International Conference on Machine Learning, 2011: 833-840
[16] KINGMA D P, WELLING M. Auto-encoding variational Bayes[J/OL](2022-12-10)[2023-01-01]. https://arxiv.org/abs/1312.6114.2022.3
[17] MIAN P, JIE J, ZHU L, et al. Radar HRRP recognition based on discriminant deep autoencoders with small training data size[J]. Electronics Letters, 2016, 52(20): 1725-1727
[18] 王晨, 张迪明, 韩斌. 基于变分自编码和三支决策的工控入侵检测算法[J]. 信息技术与网络安全, 2022, 41(6): 10-17 WANG Chen, ZHANG Diming, HAN Bin. An industrial intrusion detection algorithm based on variational autoencoder and three-way decisions[J]. NETwork and Information Security, 2022, 41(6): 10-17 (in Chinese)
[19] ZHANG Y Y, GAO L, LI X Y, et al. A novel data-driven fault diagnosis method based on deep learning[C]//International Conference on Data Mining and Big Data, 2017: 442-452
[20] 王永良, 陈辉, 彭应宁, 等. 空间谱估计理论与算法[M]. 北京:清华大学出版社, 2004:19-20 WANG Yongliang, CHEN Hui, PENG Yingning, et al. Spatial spectrum estimation theory and algorithms[M]. Beijing: Tsinghua University Press, 2004:19-20 (in Chinese)
[21] 崔伟. 基于改进粒子群算法的阵列天线误差校正的研究[D]. 镇江:江苏科技大学, 2020 CUI Wei. Research on error correction of array antenna based on improved particle swarm algorithm[D]. Zhengjiang: Jiangsu University of Science and Technology, 2020 (in Chinese)
[22] GLOROT X, BORDES A, BENGIO Y. Deep spare rectifier neural networks[C]//Proceedings of the 14th International Conference on Artificial Intelligence and Statistics, 2011: 315-323 |
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