Experimental study on the performance of DOA estimation algorithm using a coprime acoustic sensor array without a priori knowledge of the source number

Abstract

Coprime acoustic sensor arrays have been recently developed to estimate the direction-of-arrival (DOA) of multiple sound sources and may be needed in many acoustic applications because they can provide greater degrees of freedom and better estimation performance. However, most existing DOA estimation algorithms are derived under the assumption that the number of sources is known and have poor robustness due to unknown noise. This paper proposes a robust DOA estimation algorithm without estimating the number of sources using a coprime acoustic sensor array. The solution is based on the multiple signal classification (MUSIC)-like DOA estimation algorithm framework, in which a new spatial covariance model via spatial smoothing of the coprime array output signal is designed. The proposed spatial smoothing generalized MUSIC-like (SS-G-MUSIC-like) algorithm utilizes the diagonal loading technique to reconstruct the spatial smoothed covariance matrix. Results related to one-sound source and two-sound sources DOA estimation experiments show that the proposed algorithm can provide more focused source tracks over the entire data segment and better clutter suppression.