A Comprehensive Multisnapshot Joint Estimation Algorithm for Sound Source Localization

Fully utilizing measurement data can enhance the sound source localization performance, in addition to increasing observation dimensions. However, most existing studies simply use the entire measurement data once for localization, which obviously fails to fully exploit the valuable information contained in each measurement data. To address these issues, this article proposes a comprehensive multisnapshot joint Newtonized orthogonal matching pursuit (COMP-MJNOMP) algorithm. We first enhance the fault tolerance of atom selection by the comprehensive orthogonal matching pursuit (COMP) algorithm to maximize the likelihood of ensuring that all sound sources fall in a significantly reduced reconstruction target area, thus overcoming the issues of excessive computational resources and correlation confusion caused by finer grid spacing in the original space. Subsequently, we implement the proposed multisnapshot joint Newtonized orthogonal matching pursuit (MJNOMP) algorithm for joint estimation of sound sources based on the data of multiple random subarrays, thereby fully leveraging each measurement data to enhance the localization performance. Simulation and experimental results show that the proposed algorithm significantly outperforms the original greedy algorithms [multisnapshot orthogonal matching pursuit (MOMP) and multisnapshot Newtonized orthogonal matching pursuit (MNOMP)] and achieves more efficient localization compared to the advanced deconvolution Newtonized orthogonal matching pursuit deconvolution approach for the mapping of acoustic sources (NOMP-DAMAS) algorithm. The proposed algorithm exhibits a notable improvement in localization precision while also demonstrating superior robustness against noise. Furthermore, it can maintain excellent localization capability across a wide frequency range.