A Sound Source Localization Method Based on Multipath Combined Matching Pursuit
Shuhai Wang, Lin Geng, Ge Zhang · 2023
Sound source localization technology has broader application prospects. High spatial resolution and high accuracy for sound source localization is essential to noise control and fault diagnosis. Beamforming is a kind of efficient technique to identify the acoustic source by microphone array measurement. However, conventional beamforming is difficult to identify sound sources accurately due to its inherent drawbacks, including low spatial resolution and small dynamic ranges. Compressive sensing beamforming is a potential method to achieve a high-quality acoustic localization map. In this paper, a novel sound source localization method based on multipath combined matching pursuit (MCMP) is proposed, which is a greedy iterative algorithm that based on sparse reconstruction compressive sensing theory. After extensive simulation verification, the proposed method has good localization performance with high resolution compared to traditional methods. Compared with the typical compressive sensing matching pursuit algorithm, MCMP has a more stable and higher accuracy of sound source localization. And the proposed method is more efficient, that is, it has lower comprehensive computing cost.