Partial Angular Sparse Representation Based DOA Estimation Using Sparse Separate Nested Acoustic Vector Sensor Array

Abstract

In this paper, the issue of direction of arrival (DOA) estimation is discussed, and a partial angular sparse representation (SR)-based method using a sparse separate nested acoustic vector sensor (SSN-AVS) array is developed. Traditional AVS array is improved by separating the pressure sensor array and velocity sensor array into two different sparse array geometries with nested relationship. This improved array geometry can achieve large degrees of freedom (DOF) after the extended vectorization of the cross-covariance matrix, and only partial SR of the angle is required by exploiting the cyclic phase ambiguity caused by the large inter-element spacing of the virtual array. Joint sparse recovery is developed to amend the grid offset and unitary transformation is utilized to transform the complex atoms into real-valued ones. After sparse recovery, the sparse vector can simultaneously provide high-resolution but ambiguous angle estimation and unambiguous reference angle estimation embedded in the AVS array, and they are combined to obtain unique and high-resolution DOA estimation. Compared to other state-of-the-art DOA estimation methods using the AVS array, the proposed algorithm can provide better DOA estimation performance while requiring lower complexity. Multiple simulation results verify the effectiveness of the approach.

Partial Dictionary Based Off‐Grid DOA Estimation Using Combined Coprime and Nested Array

“…Compared to conventional subspace based methods, sparse representation based DOA estimation methods have been attractive since they can provide higher resolution and require fewer samples [6], and many effective sparse representation based methods have been proposed. The greedy methods [7,8] require the prior information of source number and are sensitive to the noise, and the l 1 -norm based algorithms, such as the l 1 -norm singular value decomposition (l 1 -SVD) method [9], sparse recovery using weighted subspace fitting (SRWSF) method [10], sparse representation of array covariance vector (SRACV) method [11], and sparse iterative covariance-based estimation (SPICE) method [12], can reduce the sensitivity to noise and estimate the angles via convex optimization. However, these methods discretize the whole spatial range into a grid, which will result in performance degradation when the sources are not exactly located on the grid, i.e., the grid mismatch problem [13].…”

Frequency Invariant Beamforming for a Small-Sized Bi-Cone Acoustic Vector–Sensor Array

“…In order to obtain a higher gain, an array composed of many pressure sensors is usually used in the sonar system, which leads to a high cost. Acoustic vector sensors (AVSs) can simultaneously measure the acoustic pressure as well as two or three orthogonal particle velocity components at a single spatial point [2][3][4][5][6][7][8][9]. As the AVS can make use of the extra available acoustic particle velocity information, the arrays composed of AVSs have some attractive advantages compared to the pressure sensor arrays with the same configuration, such as higher spatial gain [10], stronger ability to suppress noise [11], lower direction-of-arrival (DOA) estimation error [3,12], etc.…”

A Data Fusion Orientation Algorithm Based on the Weighted Histogram Statistics for Vector Hydrophone Vertical Array

“…References [ 13 , 14 ] enriched ESPRIT technology and applied it in practice. Researchers have also adopted a vector sensor array to the experiment for DOA estimation and verified its availability [ 15 , 16 , 17 , 18 ].…”