The optimization techniques are explored in the direction of arrival (DOA) estimation based on single acoustic pressure gradient vector sensor (APGVS). By analyzing the working principle and measurement errors of the APGVS, acoustic intensity approaches (AI) and the minimum variance distortionless response beamforming approach based on single APGVS (VMVDR) are deduced. The radius to wavelength ratio of the APGVS must be not bigger than 0.1 in the actual application, otherwise its DOA estimation performance will degrade significantly. To improve the ro-bustness and estimation performance of the DOA estimation approaches based on single APGVS, two modified proce-ssing approaches based on single APGVS are presented. Simulation and lake trial results indicate that the performance of the modified approaches based on single APGVS are better than AI and VMVDR approaches based on single APGVS when the radius to wavelength ratio is not bigger than 0.1, and the two modified DOA estimation methods have excel¬lent estimation performance when the radius to wavelength ratio is bigger than 0.1.


    Access

    Download


    Export, share and cite



    Title :

    Optimization for the direction of arrival estimation based on single acoustic pressure gradient vector sensor


    Contributors:
    Wang Xu-hu (author) / Chen Jian-feng (author) / Han Jing (author) / Jiao Ya-meng (author)


    Publication date :

    2014




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown





    Optimization for the direction of arrival estimation based on single acoustic pressure gradient vector sensor

    Wang, Xu-hu / Chen, Jian-feng / Han, Jing et al. | Online Contents | 2014


    Direction of Arrival Estimation Using a Single Vector Hydrophone

    Daxin, D. / Chunsheng, L. / Shenguang, G. | British Library Online Contents | 2005



    Direction-of-Arrival Estimation With A Vector Sensor Using Deep Neural Networks

    Yu, Jianyuan / Howard, William W. / Tait, Daniel et al. | IEEE | 2021