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Multi-Source DOA Estimation Using an Acoustic Vector Sensor Array Under a Spatial Sparse Representation Framework

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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.

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Acknowledgments

This work is supported by National Natural Science Foundation of China (No: 61271309) and the Shenzhen Science and Technology Fundamental Research Program (No: JCY201110006).

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Authors and Affiliations

  1. Advanced Digital Signal Processing Laboratory, School of Electronic Computer Engineering, Peking University, Shenzhen, 518055, China

    Yue-Xian Zou & Bo Li

  2. School of Electrical, Computer, and Telecommunications Engineering, University of Wollongong, Wollongong, Australia

    Christian H Ritz

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  1. Yue-Xian Zou
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  2. Bo Li
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  3. Christian H Ritz
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Correspondence to Yue-Xian Zou.

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Zou, YX., Li, B. & Ritz, C.H. Multi-Source DOA Estimation Using an Acoustic Vector Sensor Array Under a Spatial Sparse Representation Framework. Circuits Syst Signal Process 35, 993–1020 (2016). https://doi.org/10.1007/s00034-015-0102-9

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