基于Group Lasso的多重信号分类声源定位优化算法
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摘要
多重信号分类算法因其抑制噪声能力强、计算速度快等优点,在声源定位领域得到广泛应用。但该算法在中低频段分辨率及聚焦性能较差。针对该问题,提出一种基于Group Lasso的多重信号分类优化算法。该算法将多重信号分类算法输出值作为初始值,并在Group Lasso算法组间计算时对目标信号进行稀疏、在组内计算时对该组信号进行平滑及阈值截断。仿真结果表明:该优化算法在中低频段可明显提高多重信号分类算法分辨率,同时改善因扫描位置与声源面位置不重合引起的聚焦性能下降问题。
Multiple signal classification(MUSIC) algorithm is widely used in the field of sound source localization due to its robustness to noise and computation efficiency. However, this algorithm has poor resolution and focusing performance in the low and medium frequency bands. In view of this problem, a MUSIC algorithm optimized by Group Lasso algorithm is proposed. The output of MUSIC algorithm is used as the initial value.When the Group Lasso algorithm group is calculated, the target signal is sparse and calculated in the group.The set of signals is smoothed and the threshold is truncated. The simulation results show that the optimized algorithm can significantly improve the resolution of the MUSIC algorithm in the middle and low frequency bands, and at the same time, the problem of degraded focusing performance caused by the non-coincidence of scanning position and sound source surface position is improved.
Multiple signal classification(MUSIC) algorithm is widely used in the field of sound source localization due to its robustness to noise and computation efficiency. However, this algorithm has poor resolution and focusing performance in the low and medium frequency bands. In view of this problem, a MUSIC algorithm optimized by Group Lasso algorithm is proposed. The output of MUSIC algorithm is used as the initial value.When the Group Lasso algorithm group is calculated, the target signal is sparse and calculated in the group.The set of signals is smoothed and the threshold is truncated. The simulation results show that the optimized algorithm can significantly improve the resolution of the MUSIC algorithm in the middle and low frequency bands, and at the same time, the problem of degraded focusing performance caused by the non-coincidence of scanning position and sound source surface position is improved.
引文
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[2]肖栋,向阳,卓瑞岩,等.基于波束形成的多类型多声源定位研究[J].应用声学, 2017, 36(3):220–227.Xiao Dong, Xiang Yang, Zhuo Ruiyan, et al. Localization of multiple sound source with multi-type based on beamforming[J]. Journal of Applied Acoustics, 2017,36(3):220–227.
[3] Schmidt R. Multiple emitter location and signal parameter estimation[J]. IEEE Transactions on Antennas and Propagation, 1986, 34(3):276–280.
[4]居太亮.基于麦克风阵列的声源定位定位算法研究[D].成都:电子科技大学, 2006.
[5]居太亮,邵怀宗,彭启琮.近场声源三维定位MUSIC算法研究[J].电子测量与仪器学报, 2007, 21(1):44–48.Ju Tailiang, Shao Huaizong, Peng qizong. Speech source3D localization MUSIC algorithm in near?eld[J]. Journal of Electronic Measurement and Instrument, 2007, 21(1):44–48.
[6] Viberg M, Ottersten B, Kailath T. Detection and estimation in sensor arrays using weighted subspace?tting[J].IEEE Transactions on Signal Processing, 1991, 39(11):2436–2449.
[7]何子述,黄振兴,向敬成.修正MUSIC算法对相关信号源的DOA估计性能[J].通信学报, 2000, 21(10):14–17.He Zishu, Huang Zhenxing, Xiang Jingcheng. The performance of DOA estimation for correlated signals by modi?ed MUSIC algorithm[J]. Journal on Communications,2000, 21(10):14–17.
[8]石新智,王高峰,文必洋.修正MUSIC算法对非线性阵列适用性的讨论[J].电子学报, 2004, 32(1):147–149.Shi Xinzhi, Wang Gaofeng, Wen Biyang. Discussion of the application for the monopole, Cross-Loops array based on MUSIC algorithm[J]. Acta Electronica Sinica, 2004, 32(1):147–149.
[9] Gardner W A. Signal interception:a unifying theoretical framework for feature detection[J]. IEEE Transactions on Communications, 1988, 36(8):897–906
[10]黄知涛,王炜华,姜文利,等.一种基于循环互相关的非相干源信号方向估计方法[J].通信学报, 2003, 24(2):108–113.Huang Zhitao, Wang Weihua, Jiang Wenli, et al. A cyclic cross-correlation based direction-of-arrival estimation approach[J]. Journal on Communications, 2003,24(2):108–113.
[11]王超,笪良龙,韩梅,等.单矢量水听器的高分辨目标方位跟踪算法研究[J].应用声学, 2017, 36(1):59–66.Wang Chao, Da Lianglong, Han Mei, et al. Highresolution target azimuth tracking for a single vector hydrophone[J]. Journal of Applied Acoustics, 2017, 36(1):59–66.
[12] Simon N, Friedman J, Hastie T, et al. A sparse-Group Lasso[J]. Journal of Computationaland Graphical Statistics, 2013, 22(2):231–245.
[13] Chu N. Bayesian approach in acoustic source localization and imaging[D]. University Paris Sud–Paris XI, 2013:46–71.
[14] Berry A, Grand jean P, Gauthier P A. Investigation of the group lasso algorithm for sound?eld reproduction:comparison with the lasso and elastic-net algorithms[C]. Inter nation Congress on Sound&Vibration, 2017:23–27.
[15] Lilis G N, Angelosante D, Giannakis G B. Sound?eld reproduction using the Lasso[J]. IEEE Transactions on Audio, Speech, and Language Processing, 2010, 18(8):1902–1912.