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基于Group Lasso的多重信号分类声源 定位优化算法*

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吴江涛,胡定玉,方宇,朱文发.基于Group Lasso的多重信号分类声源 定位优化算法*[J].,2019,38(2):261-267
基于Group Lasso的多重信号分类声源 定位优化算法*
An optimized multiple signal classification algorithm based on Group Lasso for sound localization
投稿时间：2018-05-21修订日期：2019-02-28
中文摘要:
MUSIC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MUSIC优化算法。该算法将MUSIC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MUSIC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。
英文摘要:
MUSIC algorithm is widely used in the field of sound source localization due to its robustness to noise, computation efficiency. However, this algorithm has poor resolution and focusing performance in the low and medium frequency bands. Aiming at 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.
DOI：10.11684/j.issn.1000-310X.2019.02.016
中文关键词:MUSIC算法，Group Lasso，声源定位
英文关键词:MUSIC algorithm, Group Lasso, Source location
基金项目:国家自然科学基金青年基金项目 (51605274), 上海工程技术大学展翅计划项目 (RC152017), 上海工程技术大学研究生科研创新项目 (17KY1012)
作者单位E-mail
吴江涛上海工程技术大学392105916@qq.com
胡定玉上海工程技术大学dyhu1987@163.com
方宇上海工程技术大学fangyu_hit@126.com
朱文发上海工程技术大学zhuwenfa1986@163.com
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