Page 48 - 应用声学2019年第4期
P. 48

508                                                                                  2019 年 7 月


                 tive filtering framework[J]. IEEE Journal of Selected Top-  tistical Society: Series B (Statistical Methodology), 2006,
                 ics in Signal Processing, 2010, 4(2): 409–420.    68(1): 49–67.
              [9] 伍飞云, 周跃海, 童峰. 引入梯度导引似 p 范数约束的稀疏信              [14] Negahban S, Wainwright M J. Joint support recovery un-
                 道估计算法 [J]. 通信学报, 2014, 35(7): 172–177.            der high dimensional scaling: benefits and perils of l 1,∞ -
                 Wu Feiyun, Zhou Yuehai, Tong Feng. Estimation algo-  regularization[C]. Advances in Neural Information Pro-
                 rithm for sparse channels with gradient guided p-norm like  cessing Systems, 2008: 1161–1168.
                 constraints[J]. Journal on Communications, 2014, 35(7):  [15] BachF R. Consistency of the group Lasso and multiple
                 172–177.                                          kernel learning[J]. Journal of Machine Learning Research
             [10] Pelekanakis K, Chitre M. New sparse adaptive algorithms  2008, 9: 1179–1225.
                 based on the natural gradient and the l 0 -norm[J]. IEEE  [16] Eksioglu E M. Recursive l 1,∞ group Lasso[J]. IEEE
                 Journal of Oceanic Engineering, 2013, 38: 323–332.  Transactions  on  Signal  Processing,  2012,  60(8):
             [11] Geng X, Zielinski A. An eigenpath underwater acoustic  3978–3987.
                 communication channel model[C]. In Proc. MTS/IEEE  [17] Eksioglu E M. Group sparse RLS algorithms[J]. Interna-
                 OCEANS Conf, SanDiego, CA, Oct. 1995: 1189–1196.  tional Journal of Adaptive Control and Signal Processing,
             [12] Wang Z, Zhou S, Presig J C, et al. Clustered adaptation  2014, 28(12): 1398–1412.
                 for estimation of time-varying underwater acoustic chan-  [18] Widrow B, Stearns S D. Adaptive signal processing[M].
                 nels[J]. IEEE Transactions on Signal Processing, 2012,  Englewood Cliffs, New Jersey, USA: Prentice-Hall, 1985:
                 60(6): 3079–3091.                                 110–111.
             [13] Yuan M, Lin Y. Model selection and estimation in regres-  [19] 吴丽丽. 深海远程脉冲声传播特性研究 [D]. 北京: 中国科学
                 sion with grouped variables[J]. Journal of the Royal Sta-  院声学研究所, 2019: 32.
   43   44   45   46   47   48   49   50   51   52   53