Page 86 - 《应用声学》2021年第5期
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                                                                   ocean waveguide using supervised machine learning[J].
                                                                   The Journal of the Acoustical Society of America, 2017,
                            参 考     文   献                          142(3): 1176–1188.
                                                                [12] Wang Y, Peng H. Underwater acoustic source localization
              [1] 张旭, 孙翱, 韩旭, 等. 水下垂向运动目标的海底多基站声定                  using generalized regression neural network[J]. The Jour-
                 位方法及精度分析 [J]. 声学学报, 2019, 44(2): 155–169.         nal of the Acoustical Society of America, 2018, 143(4):
                 Zhang Xu, Sun Ao, Han Xu, et al.Acoustic localiza-  2321–2331.
                 tion scheme and accuracy analysis for underwater vertical  [13] Huang Z, Xu J, Gong Z, et al. Source localization using
                 motion target using multi-stations in the seabed[J]. Acta  deep neural networks in a shallow water environment[J].
                 Acustica, 2019, 44(2): 155–169.                   The Journal of the Acoustical Society of America, 2018,
              [2] 张雪冬, 牛海强, 吴立新. 一种基于序贯估计的直达声区水面                   143(5): 2922–2932.
                 舰船被动测距方法 [J]. 应用声学, 2020, 39(4): 491–500.      [14] Liu Y, Niu H, Li Z. Source ranging using ensemble con-
                 Zhang Xuedong, Niu Haiqiang, Wu Lixin. Passive track-  volutional networks in the direct zone of deep water[J].
                 ing of a surface ship in the direct zone using sequential pa-  Chinese Physics Letters, 2019, 36(4): 044302.
                 rameter estimation[J]. Journal of Applied Acoustics, 2020,  [15] Niu H, Gong Z, Ozanich E, et al. Deep-learning source lo-
                 39(4): 491–500.                                   calization using multi-frequency magnitude-only data[J].
              [3] 刘炎堃, 郭永刚, 李整林, 等. 基于路径选择的深海水下运动                  The Journal of the Acoustical Society of America, 2019,
                 目标被动深度估计 [J]. 应用声学, 2020, 39(5): 647–655.         146(1): 211–222.
                 Liu Yankun, Guo Yonggang, Li Zhenglin, et al. Depth  [16] Liu W, Yang Y, Xu M, et al. Source localization in the
                 estimation of moving underwater source based on routes  deep ocean using a convolutional neural network[J]. The
                 choosing[J]. Journal of Applied Acoustics, 2020, 39(5):  Journal of the Acoustical Society of America, 2020, 147(4):
                 647–655.                                          EL314–EL319.
              [4] Bucker H P. Use of calculated sound fields and matched-  [17] 张巧力, 刘福臣. 基于 FFNN 的垂直阵被动定位技术研究 [J].
                 field detection to locate sound sources in shallow water[J].  声学与电子工程, 2020(1): 32–36.
                 The Journal of the Acoustical Society of America, 1976,  [18] Niu H, Gerstoft P. Source localization in underwater
                 59(2): 368–373.                                   waveguides using machine learning[J]. The Journal of the
              [5] Baggeroer A B. Matched field processing: source localiza-  Acoustical Society of America, 2016, 140(4): 3232–3232.
                 tion in correlated noise as an optimum parameter estima-  [19] Ozanich E R, Gerstoft P, Purohit A. Ocean acoustic range
                 tion problem[J]. The Journal of the Acoustical Society of  estimation in noisy environments using convolutional net-
                 America, 1988, 83(2): 571–587.                    works[J]. The Journal of the Acoustical Society of Amer-
              [6] Michalopoulou Z H, Porter M B. Matched-field process-  ica, 2018, 144(3): 1743–1743.
                 ing for broad-band source localization[J]. IEEE Journal of  [20] Ozanich E, Gerstoft P, Niu H. A feedforward neural
                 Oceanic Engineering, 1996, 21(4): 384–392.        network for direction-of-arrival estimation[J]. The Jour-
              [7] Soares C, Jesus S M. Broadband matched-field processing:  nal of the Acoustical Society of America, 2020, 147(3):
                 coherent and incoherent approaches[J]. The Journal of the  2035–2048.
                 Acoustical Society of America, 2003, 113(5): 2587–2598.  [21] Muarry J, Ensberg D. The swellex-96 experiment [DB/
              [8] 杨坤德, 马远良, 邹士新, 等. 基于环境扰动的线性匹配场处                  OL]. [1996-05-31]. [2019-10-15]. http://www.swellex96.
                 理方法 [J]. 声学学报, 2006, 43(6): 496–505.              ucsd.edu/.
                 Yang Kunde, Ma Yuanliang, Zou Shixin, et al.Linear  [22] Porter M B. The KRAKEN normal mode program[R].
                 matched field processing based on environmental pertur-  Naval Research Lab Washington DC, 1992.
                 bation[J]. Acta Acustica, 2006, 43(6): 496–505.  [23] 杨坤德. 水声信号的匹配场处理技术研究 [D]. 西安: 西北工
              [9] 贾雨晴, 苏林, 郭圣明, 等. 浅海时变声速环境下的自适应匹                  业大学, 2003.
                 配场定位算法实现 [J]. 应用声学, 2018, 37(4): 518–527.      [24] Specht D F. A general regression neural network[J]. IEEE
                 Jia Yuqing, Su Lin, Guo Shengming, et al. An adaptive  Transactions on Neural Networks, 1991, 2(6): 568–576.
                 matched-field source localization algorithm in coastal wa-  [25] Niu H, Ozanich E, Gerstoft P. Ship localization in Santa
                 ter under the circumstances of time-evolving sound speed  Barbara Channel using machine learning classifiers[J]. The
                 profiles[J]. Journal of Applied Acoustics, 2018, 37(4):  Journal of the Acoustical Society of America, 2017, 142(5):
                 518–527.                                          EL455–EL460.
             [10] Steinberg B Z, Beran M J, Chin S H, et al. A neural net-  [26] He K, Zhang X, Ren S, et al.  Deep residual learning
                 work approach to source localization[J]. The Journal of the  for image recognition[C]. Proceedings of the IEEE Confer-
                 Acoustical Society of America, 1991, 90(4): 2081–2090.  ence on Computer Vision and Pattern Recognition, 2016:
             [11] Niu H, Reeves E, Gerstoft P. Source localization in an  770–778.
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