基于源强先验引导的正交匹配追踪声源识别算法

压缩感知理论的出现为采用较少的传声器实现高分辨率声源识别与定位，提供了理论可能和实现途径。因此越来越多的学者将压缩感知方法应用到声源识别领域当中。在已有的诸多压缩感知重构算法中，正交匹配追踪（Orthogonal matching pursuit，OMP）算法具有旁瓣小、分辨率较高、算法过程简单、计算速度快、易于硬件实现等优点，具有广泛的应用潜力。但是OMP算法在实际应用场合中表现出的对低频声源定位效果差，聚焦面网格密集划分时易出现定位偏差的缺点限制了算法的应用范围。为此，通过对OMP算法关键步骤的理论分析，找出OMP算法上述缺陷的理论来源，在此基础上提出一种基于源强先验引导的OMP声源定位算法，该方法在OMP原子筛选过程中引入了源强先验信息，可以较好地克服由相邻声源距离较近或分析频率较低时原子间相关性增强引起的原子选择错误，从而进一步提高了算法的声源定位准确率，拓宽了算法适用的频率范围，在实际中可实现宽频带声源的高分辨率识别与定位。

Sound Source Identification Based on Orthogonal Matching Pursuit Algorithm Guided by Source Strengthen Prior

The theory of compressed sensing provides a theoretical possibility and a way to realize high-resolution sound source identification and localization with fewer microphones. Therefore, more and more scholars apply the compressed sensing method to solve sound source identification and location problems. Among the existing compressed sensing reconstruction algorithms, orthogonal matching pursuit (OMP) algorithm has the advantages of small sidelobe, high resolution, simple algorithm process, fast calculation speed, and easy hardware implementation, which has the wide application potential. However, the OMP algorithm shows poor positioning performance for low-frequency sound source, and is prone to positioning deviation when the focus plane is densely meshed, which limits the application scope of the algorithm. For this reason, an OMP algorithm based on prior of sound source strengthen is proposed. In this method, prior information of source strengthen is introduced into the atom selection process of OMP, which can better overcome the atomic selection error caused by the high correlation between atoms of sensing matrix when the analysis frequency is low or the focus plane is densely meshed. This algorithm further improves the spatial resolution of the sound source localization and broadens the frequency range to which the algorithm is applicable. In practical applications, it can help us achieve higher accuracy of sound source localization and wide-band sound sources identification.