Noise Suppression for Direction of Arrival Estimation in Co-located MIMO Sonar

• Published in: Sensors (Basel, Switzerland) Vol. 19; no. 6; p. 1325
• Main Authors: Cheng, Xue, Wang, Yingmin
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI 16.03.2019; MDPI AG
• Subjects: covariance matrix; direction-of-arrival estimation; MIMO sonar; noise suppression; Toeplitz; noise suppression; direction-of-arrival estimation; MIMO sonar; Toeplitz; covariance matrix
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s19061325

Noise suppression capacity in multiple-input multiple-output (MIMO) sonar signal processing is derived under the assumption of white Gaussian noise. However, underwater noise mainly includes white Gaussian noise and colored noise. There exists a certain correlation between the noise signals received by each MIMO sonar array element. The performance of traditional direction-of-arrival (DOA) estimation methods decreases obviously in complex marine noise. In this paper, we propose a marine environment noise suppression method for MIMO applied to multiple targets' DOA estimation. The noise field can be decomposed into a symmetric noise component and an asymmetric noise component. We use the covariance matrix imaginary component to pre-estimate the signal sources, then use the dimension reduction transformation to reconstruct the real component of the covariance matrix. The Toeplitz technique is utilized to reduce the correlation of the reconstructed covariance matrix. Thus, the subspace decomposition-based techniques such as multiple signal classification (MUSIC) can be used for multiple targets' DOA estimation. To reduce the computational complexity of the methods, search-free direction-finding techniques such as the estimation of signal parameters via rotational invariance techniques (ESPRIT) can be utilized. As a result, the proposed methods can achieve better direction-finding performance in the condition of limited snapshots with lower computational cost. The corresponding Cramer-Rao bound (CRB) is deduced and the signal-to-noise ratio (SNR) gain obtained by dimension reduction processing is discussed. Simulation results also show the superiority of the proposed method over the existing methods.