Page 28 - 《应用声学》2023年第2期
P. 28

216                                                                                  2023 年 3 月


                 Journal of Oceanic Engineering, 2019, 44(1): 156–166.  gression: experimental validation and comparison with
             [13] Bianco M J, Gerstoft P, Traer J, et al. Machine learning in  MUSIC[J]. IEEE Antennas and Wireless Propagation Let-
                 acoustics: theory and applications[J]. The Journal of the  ters, 2007, 6(11): 379–382.
                 Acoustical Society of America, 2019, 146(5): 3590–3628.  [21] Ozanich E, Gerstoft P, Niu H. A feedforward neural
             [14] Huang H, Yang J, Huang H, et al. Deep learning for super-  network for direction-of-arrival estimation[J]. The Jour-
                 resolution channel estimation and DOA estimation based  nal of the Acoustical Society of America, 2020, 147(3):
                 massive MIMO system[J]. IEEE Transactions on Vehicu-  2035–2048.
                 lar Technology, 2018, 67(9): 8549–8560.        [22] Trabelsi C, Bilaniuk O, Zhang Y, et al. Deep complex
             [15] Ahmed A M, Thanthrige U, Gamal A E, et al. Deep  networks[J]. arXiv Preprint, 2017, arXiv: 1705.09792.
                 learning for DOA estimation in MIMO radar systems via
                                                                [23] Cao Y, Lyu T, Lin Z, et al.  Complex ResNet aided
                 emulation of large antenna arrays[J]. IEEE Communica-
                                                                   DOA estimation for near-field MIMO systems[J]. IEEE
                 tions Letters, 2021, 29(5): 1559–1563.
                                                                   Transactions on Vehicular Technology, 2020, 69(10):
             [16] Jha S, Durrani T. Direction of arrival estimation using ar-
                                                                   11139–11151.
                 tificial neural networks[J]. IEEE Transactions on Systems,
                                                                [24] Liu Z M, Zhang C, Yu P S. Direction-of-arrival estima-
                 Man, and Cybernetics, 1991, 21(5): 1192–1201.
                                                                   tion based on deep neural networks with robustness to
             [17] 李洪升, 赵俊渭, 王峰, 等. 一种基于径向基函数网络的盲
                                                                   array imperfections[J]. IEEE Transactions on Antennas
                 波束形成方法研究 [J]. 系统工程与电子技术, 2003, 25(6):
                                                                   and Propagation, 2018, 66(12): 7315–7327.
                 661–663, 687.
                                                                [25] Zhu W, Zhang M, Li P, et al. Two-dimensional DOA esti-
                 Li Hongsheng, Zhao Junwei, Wang Feng, et al. Study of a
                                                                   mation via deep ensemble learning[J]. IEEE Access, 2020,
                 blind beamforming method based on RBFNN[J]. Journal
                                                                   8: 124544–124552.
                 of Air Force Engineering University, 2003, 25(6): 661–663,
                                                                [26] Xiang H, Chen B, Yang M, et al. Improved direction-
                 687.
                                                                   of-arrival estimation method based on LSTM neural net-
             [18] 于斌, 尹成友, 黄冶. 阵列误差影响下的 RBF 神经网络波达
                                                                   works with robustness to array imperfections[J]. Applied
                 方向估计 [J]. 微波学报, 2007, 23(6): 21–25, 31.
                                                                   Intelligence, 2021, 51(2): 1–14.
                 Yu Bin, Yin Chengyou, Huang Ye. Direction of arrival
                 (DOA) estimation for an array with errors using RBF neu-  [27] Biggs D, Andrews M. Acceleration of iterative image
                 ral network[J]. Journal of Microwaves, 2007, 23(6): 21–25,  restoration algorithms[J]. Applied Optics, 1997, 36(8):
                 31.                                               1766–1775.
             [19] Pastorino M, Randazzo A. A smart antenna system for  [28] Ronneberger O, Fischer P, Brox T. U-Net: convolutional
                 direction of arrival estimation based on a support vector  networks for biomedical image segmentation[J]. arXiv
                 regression [J]. IEEE Transactions on Antennas and Prop-  Preprint, 2015, arXiv: 1505.04597v1.
                 agation, 2005, 53(7): 2161–2168.               [29] Dumoulin V, Visin F. A guide to convolution arith-
             [20] Randazzo A, Abou-Khousa M A, Pastorino M, et al. Di-  metic for deep learning[J]. arXiv Preprint, 2016, arXiv:
                 rection of arrival estimation based on support vector re-  1603.07285v2.
   23   24   25   26   27   28   29   30   31   32   33