Low‐complexity 2D DOA estimator for multiple coherently distributed sources

• Zhi Zheng ^[1] ; Guangjun Li ^[1] ; Yunlong Teng ^[1]
  1. [1] University of Electronic Science and Technology of China

     University of Electronic Science and Technology of China

     China

     
• Localización: Compel: International journal for computation and mathematics in electrical and electronic engineering, ISSN 0332-1649, Vol. 31, Nº 2 (Special Issue: ARWtr conference Spain), 2012, págs. 443-459
• Idioma: inglés
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• Resumen

  • Purpose – The purpose of this paper is to develop a new method for the two‐dimensional direction‐of‐arrival (DOA) estimation of multiple coherently distributed (CD) sources, which can provide lower computational complexity while sustaining the estimation performance within a tolerable level.

    Design/methodology/approach – Using three parallel uniform linear arrays (ULAs), a new method for parametric estimation of multiple coherently distributed sources is proposed. The proposed method is based on the Taylor approximation to the generalized steering vectors (GSVs) of shifted ULAs, and utilizes the special array geometry. In addition, a simple parameter matching procedure is also given in this paper.

    Findings – Several numerical experiments have been designed. The experiments are based on coherently distributed source model, and the noise is assumed to be zero‐mean and spatially and temporally white and Gaussian. Numerical results show that the proposed method can exhibit good estimation performance under small angular spread and be applicable to the multisource scenario with different angular distributions.

    Research limitations/implications – This research is limited to CD sources. Furthermore, the proposed method is based on the small angular approximation to GSV. Hence, it is fitter for the case of small angular extension.

    Originality/value – Without any spectrum‐peak searching, the proposed method provides lower computational cost compared to the classical spectrum‐based methods. Moreover, it does not depend on the prior knowledge about angular distribution shape and is hence robust to mismodeling.