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Träfflista för sökning "LAR1:cth ;pers:(Viberg Mats 1961);spr:eng"

Sökning: LAR1:cth > Viberg Mats 1961 > Engelska

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151.
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154.
  • Viberg, Mats, 1961 (författare)
  • Introduction to Array Processing
  • 2014
  • Ingår i: Academic Press Library in Signal Processing: Volume 3 Array and Statistical Signal Processing. - 9780124115972 ; , s. 463-502
  • Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
    • The purpose of this chapter is to give some background material on array signal processing, which serves as a more detailed introduction to the remaining chapters. The ideal data model is introduced and its properties are explored, with a special emphasis on the array response. The general concepts of beamforming and direction-of-arrival estimation are introduced, and exemplified by some well-known techniques. Although the focus is on traditional applications involving an array of coherent sensors, we also present some extensions to non-coherent and/or distributed sensors.
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155.
  • 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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