【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。更多还原

【Abstract】 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.更多还原