145 / 2019-12-15 08:43:00
Underdetermined DOA Estimation based on Original Covariance Matrix using Sparse Array
Underdetermined DOA estimation; sparse array; original covariance matrix estimation(OCME); covariance matrix Toeplitz reconstruction (CMTR); sparse covariance matrix reconstruction(SCMR)
全文被拒
Wang Geng / Air Force Early Warning Academy, China
He Minghao / Air Force Early Warning Academy, China
Han Jun / Air Force Early Warning Academy, China
In this paper, we provide a novel insight over the DOA estimation problem for sparse array with accurate covariance matrix estimation of the equal aperture uniform linear array (ULA), called original uniform linear array (OULA). Specifically, superior performance estimation of covariance matrix of the original uniform linear array is derived from exploiting the Toeplitz structure of the covariance matrix. Meanwhile, covariance matrix estimation can be transformed as convex optimization problem and can even find more signals than DOFs of sparse array, such as coprime array. By implementing numerical simulation experiments, we demonstrate that our proposed algorithm can outperform other existing methods in terms of DOA underdetermined estimation performance, estimation accuracy, spend time and resolution ability.
重要日期
  • 会议日期

    06月08日

    2020

    06月11日

    2020

  • 01月12日 2020

    初稿截稿日期

  • 04月15日 2020

    提前注册日期

  • 12月31日 2020

    注册截止日期

主办单位
IEEE Signal Processing Society
承办单位
Zhejiang University
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