Efficient robust adaptive beamforming by the Adaptive Projected Subgradient Method : A set-theoretic time-varying approach over multiple a priori constraints

  • SLAVAKIS Konstantinos
    Tokyo Institute of Technology, Dept. of Communications & Integrated Systems
  • YUKAWA Masahiro
    Tokyo Institute of Technology, Dept. of Communications & Integrated Systems
  • YAMADA Isao
    Tokyo Institute of Technology, Dept. of Communications & Integrated Systems

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Other Title
  • 適応射影劣勾配法によるアレーアンテナのロバスト適応ビーム形成法 : 多重凸制約条件付き非負値凸関数列漸近的最小化問題の応用

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This paper introduces a set theoretic approach to adaptive array beamforming in the presence of imprecise knowledge of the steering vectors. At first, we demonstrate that in such erroneous situations, a robust data-independent beamformer can be viewed as a solution to a convex feasibility problem; find a point that belongs to the intersection of a finite number of closed convex sets in a Euclidean space. If we also elaborate the instantaneous data in the design, an infinite number of closed convex sets is introduced as additional constraints on the desired beamformer. Overall, robust data-dependent adaptive beamforming is reformulated as a time-varying convex feasibility problem with multiple convex constraints : find a point that belongs to an infinite sequence of closed convex sets and in the meantime satisfies a finite number of fixed in time closed convex constraints in a Euclidean space. An algorithmic solution to this problem has been recently given in a more general setting by the Adaptive Projected Subgradient Method over the fixed point set of strongly attracting nonexpansive mappings in a real Hilbert space [Slavakis & Yamada, Technical Report of IEICE-SIP, Jan. 2005]. This paper briefly exhibits the efficient application of the APSM to robust adaptive beamforming. Numerical results reveal the great potential of the APSM for adaptive array signal processing applications.

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Details 詳細情報について

  • CRID
    1570572702454385280
  • NII Article ID
    110003281702
  • NII Book ID
    AA11943613
  • ISSN
    09135685
  • Text Lang
    ja
  • Data Source
    • CiNii Articles

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