Skip to main content
placeholder image

Multi-source DOA estimation using an acoustic vector sensor array under a spatial sparse representation framework

Journal Article


Abstract


  • This paper investigates the estimation of the two-dimensional direction of arrival (2D-DOA) of sound sources using an acoustic vector sensor array (AVSA) within a spatial sparse representation (SSR) framework (AVS-SSR-DOA). SSR-DOA estimation methods rely on a pre-defined grid of possible source DOAs and essentially suffer from the grid-effect problem: Reducing the size of the grid spacing leads to increased computational complexity. In this paper, we propose a two-step approach to tackle the grid-effect problem. Specifically, omnidirectional sensor array-based SSR-DOA estimation firstly provides initial low-cost DOA estimates using a coarse grid spacing. Secondly, a closed-form solution is derived by exploring the unique subarray manifold matrix correlation and subarray signal correlation of the AVSA, which allows for DOA estimates between the pre-defined angles of the grid and potentially achieves higher DOA estimation accuracy. To further alleviate the estimation bias due to noise and sparse representation model errors, line-fitting (LF) techniques and subspace techniques (ST) are employed to develop two novel DOA estimation algorithms, referred to as AVS-SSR-LF and AVS-SSR-ST, respectively. Extensive simulations validate the effectiveness of the proposed algorithms when estimating the DOAs of multiple sound sources. The proposed AVS-SSR-ST algorithm achieves high DOA estimation accuracy and is robust to various noise levels and source separation angles

Authors


Publication Date


  • 2016

Citation


  • Y. Zou, B. Li & C. H. Ritz, "Multi-source DOA estimation using an acoustic vector sensor array under a spatial sparse representation framework,"^^ Circuits, Systems, and Signal Processing, vol. 35, pp. 993-1020, 2016.

Scopus Eid


  • 2-s2.0-84958292370

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5147

Number Of Pages


  • 27

Start Page


  • 993

End Page


  • 1020

Volume


  • 35

Abstract


  • This paper investigates the estimation of the two-dimensional direction of arrival (2D-DOA) of sound sources using an acoustic vector sensor array (AVSA) within a spatial sparse representation (SSR) framework (AVS-SSR-DOA). SSR-DOA estimation methods rely on a pre-defined grid of possible source DOAs and essentially suffer from the grid-effect problem: Reducing the size of the grid spacing leads to increased computational complexity. In this paper, we propose a two-step approach to tackle the grid-effect problem. Specifically, omnidirectional sensor array-based SSR-DOA estimation firstly provides initial low-cost DOA estimates using a coarse grid spacing. Secondly, a closed-form solution is derived by exploring the unique subarray manifold matrix correlation and subarray signal correlation of the AVSA, which allows for DOA estimates between the pre-defined angles of the grid and potentially achieves higher DOA estimation accuracy. To further alleviate the estimation bias due to noise and sparse representation model errors, line-fitting (LF) techniques and subspace techniques (ST) are employed to develop two novel DOA estimation algorithms, referred to as AVS-SSR-LF and AVS-SSR-ST, respectively. Extensive simulations validate the effectiveness of the proposed algorithms when estimating the DOAs of multiple sound sources. The proposed AVS-SSR-ST algorithm achieves high DOA estimation accuracy and is robust to various noise levels and source separation angles

Authors


Publication Date


  • 2016

Citation


  • Y. Zou, B. Li & C. H. Ritz, "Multi-source DOA estimation using an acoustic vector sensor array under a spatial sparse representation framework,"^^ Circuits, Systems, and Signal Processing, vol. 35, pp. 993-1020, 2016.

Scopus Eid


  • 2-s2.0-84958292370

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5147

Number Of Pages


  • 27

Start Page


  • 993

End Page


  • 1020

Volume


  • 35