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  • 1.
    Schenck, David
    et al.
    Technische Universität Darmstadt, DEU.
    Hoang, Minhtrinh
    Technische Universität Darmstadt, DEU.
    Mestre, Xavier
    Centre Tecnológic de Telecomunicacions de Catalunya, ESP.
    Viberg, Mats
    Blekinge Institute of Technology, Management Team.
    Pesavento, Marius
    Technische Universität Darmstadt, DEU.
    Full covariance fitting DOA estimation using partial relaxation framework2019In: European Signal Processing Conference, European Signal Processing Conference, EUSIPCO , 2019Conference paper (Refereed)
    Abstract [en]

    The so-called Partial Relaxation approach has recently been proposed to solve the Direction-of-Arrival estimation problem. In this paper, we extend the previous work by applying Covariance Fitting with a data model that includes the noise covariance. Instead of applying a single source approximation to multi-source estimation criteria, which is the case for MUSIC, the conventional beamformer, or the Capon beamformer, the Partial Relaxation approach accounts for the existence of multiple sources using a non-parametric modification of the signal model. In the Partial Relaxation framework, the structure of the desired direction is kept, whereas the sensor array manifold corresponding to the remaining signals is relaxed [1], [2]. This procedure allows to compute a closed-form solution for the relaxed signal part and to come up with a simple spectral search with a significantly reduced computational complexity. Unlike in the existing Partial Relaxed Covariance Fitting approach, in this paper we utilize more prior-knowledge on the structure of the covariance matrix by also considering the noise covariance. Simulation results show that, the proposed method outperforms the existing Partial Relaxed Covariance Fitting method, especially in difficult conditions with small sample size and low Signal-to-Noise Ratio. Its threshold performance is close to that of Deterministic Maximum Likelihood, but at significantly lower cost. © 2019 IEEE

  • 2.
    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.

  • 3.
    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.

1 - 3 of 3
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