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Sökning: swepub > Chalmers tekniska högskola > (1990-1999) > Viberg Mats 1961

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  • Viberg, Mats, 1961, et al. (författare)
  • Maximum Likelihood Array Processing in Spatially Correlated Noise Fields Using Parameterized Signals
  • 1997
  • Ingår i: IEEE Transactions on Signal Processing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1941-0476 .- 1053-587X. ; 45, s. 996-1004
  • Tidskriftsartikel (refereegranskat)abstract
    • This paper deals with the problem of estimating signal parameters using an array of sensors. This problem is of interest in a variety of applications, such as radar and sonar source localization. A vast number of estimation techniques have been proposed in the literature during the past two decades. Most of these can deliver consistent estimates only if the covariance matrix of the background noise is known. In many applications, the aforementioned assumption is unrealistic. Recently, a number of contributions have addressed the problem of signal parameter estimation in unknown noise environments based on various assumptions on the noise. Herein, a different approach is taken. We assume instead that the signals are partially known. The received signals are modeled as linear combinations of certain known basis functions. The exact maximum likelihood (ML) estimator for the problem at hand is derived, as well as a computationally more attractive approximation. The Cramer Rao lower bound (CRB) on the estimation error variance is also derived and found to coincide with the CRB, assuming an arbitrary deterministic model and known noise covariance.
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  • Ngia, Lester S.H. 1965, et al. (författare)
  • Adaptive neural nets filter using a recursive Levenberg-Marquardt search direction
  • 1998
  • Ingår i: Proc. Asilomar Conf. Signals, Systems, Computers, 01 Nov 1998-04 Nov 1998. ; 1, s. 697-701
  • Konferensbidrag (refereegranskat)abstract
    • This paper proposes a recursive Levenberg-Marquardt (LM) search direction as the training algorithm for non-linear adaptive filters, which use multi-layer feed forward neural nets as the filter structures. The neural nets can be considered as a class of non-linear adaptive filters with transversal or recursive filter structures. In the off-line training, the LM method is regarded as an intermediate method between the steepest descent (SD) and Gauss-Newton (GN) methods, and it has better convergence properties than the other two methods. In the echo cancellation experiments, the recursive LM algorithm converges faster and gives higher echo return loss enhancement (ERLE) than the recursive SD and GN algorithms.
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  • Resultat 1-10 av 38

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