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  • 1.
    Trinh-Hoang, Minh
    et al.
    Technische Universität Darmstadt, DEU.
    Viberg, Mats
    Blekinge Institute of Technology, Management Team.
    Pesavento, Marius
    Technische Universität Darmstadt, DEU.
    CramÉr-rao Bound for DOA Estimators under the Partial Relaxation Framework2019In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, Institute of Electrical and Electronics Engineers Inc. , 2019, p. 4469-4473Conference paper (Refereed)
    Abstract [en]

    In this paper, the Cramér-Rao Bound for the Direction-ofArrival parameter under the partial relaxation framework is derived. We introduce a non-redundant parameterization of the signal model corresponding to the partial relaxation framework, in which the array structure in part of the steering matrix is neglected while the rank of the relaxed steering matrix is maintained. We prove that the stochastic Cramér-Rao Bound for the Direction-of-Arrival parameter under the partial relaxation signal model is lower-bounded by that of the conventional stochastic Cramér-Rao Bound. Furthermore, we prove that the partial relaxation estimator for the Weighted Subspace Fitting criterion asymptotically achieves the conventional Cramér-Rao Bound in the case of uncorrelated source signals. © 2019 IEEE.

  • 2.
    Trinh-Hoang, Minh
    et al.
    Tech Univ Darmstadt, DEU.
    Viberg, Mats
    Blekinge Institute of Technology, Management Team.
    Pesavento, Marius
    Tech Univ Darmstadt, DEU.
    CramÉr-rao Bound for DOA Estimators under the Partial Relaxation Framework2019In: 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), IEEE , 2019, p. 4469-4473Conference paper (Refereed)
    Abstract [en]

    In this paper, the Cramer-Rao Bound for the Direction-of-Arrival parameter under the partial relaxation framework is derived. We introduce a non-redundant parameterization of the signal model corresponding to the partial relaxation framework, in which the array structure in part of the steering matrix is neglected while the rank of the relaxed steering matrix is maintained. We prove that the stochastic Cramer-Rao Bound for the Direction-of-Arrival parameter under the partial relaxation signal model is lower-bounded by that of the conventional stochastic Cramer-Rao Bound. Furthermore, we prove that the partial relaxation estimator for the Weighted Subspace Fitting criterion asymptotically achieves the conventional Cramer-Rao Bound in the case of uncorrelated source signals.

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