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- Stoica, P, et al.
(author)
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Maximum likelihood array processing for stochastic coherent sources
- 1996
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In: IEEE TRANSACTIONS ON SIGNAL PROCESSING. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1053-587X. ; 44:1, s. 96-105
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Journal article (other academic/artistic)abstract
- Maximum likelihood (ML) estimation in array signal processing for the stochastic noncoherent signal case is well documented in the literature, Herein, we focus on the equally relevant case of stochastic coherent signals, Explicit large-sample realizations
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| 4. |
- Stoica, P, et al.
(author)
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Maximum-likelihood bearing estimation with partly calibrated arrays in spatially correlated noise fields
- 1996
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In: IEEE TRANSACTIONS ON SIGNAL PROCESSING. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1053-587X. ; 44:4, s. 888-899
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Journal article (other academic/artistic)abstract
- The problem of using a partly calibrated array for maximum likelihood (ML) bearing estimation of possibly coherent signals buried in unknown correlated noise fields is shown to admit a neat solution under fairly general conditions, More exactly, this pape
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| 6. |
- Stoica, P, et al.
(author)
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Maximum likelihood parameter and rank estimation in reduced-rank multivariate linear regressions
- 1996
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In: IEEE TRANSACTIONS ON SIGNAL PROCESSING. - 1053-587X. ; 44:12, s. 3069-3078
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Journal article (peer-reviewed)abstract
- This paper considers the problem of maximum likelihood (ML) estimation for reduced-rank linear regression equations with noise of arbitrary covariance. The rank-reduced matrix of regression coefficients is parameterized as the product of two full-rank factor matrices. This parameterization is essentially constraint free, but it is not unique, which renders the associated ML estimation problem rather nonstandard. Nevertheless, the problem turns out to be tractable, and the following results are obtained. An explicit expression is derived for the ML estimate of the regression matrix in terms of the data covariances and their eigenelements. Furthermore, a detailed analysis of the statistical properties of the ML parameter estimate is performed. Additionally, a generalized likelihood ratio test (GLRT) is proposed for estimating the rank of the regression matrix. The paper also presents the results of some simulation exercises, which lend empirical support to the theoretical findings
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| 7. |
- STOICA, P, et al.
(author)
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ON MAXIMUM-LIKELIHOOD-ESTIMATION OF DIFFERENCE EQUATION PARAMETERS
- 1995
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In: IEEE TRANSACTIONS ON SIGNAL PROCESSING. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. ; 43:8
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Other publication (other academic/artistic)abstract
- This correspondence is prompted by a recent paper in the TRANSACTIONS ON SIGNAL PROCESSING, which discussed the maximum likelihood (ML) estimation of difference equation parameters in a flawed manner. The correct ML parameter estimate is derived herein by
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| 9. |
- STOICA, P, et al.
(author)
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WEIGHTED LS AND TLS APPROACHES YIELD ASYMPTOTICALLY EQUIVALENT RESULTS
- 1995
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In: SIGNAL PROCESSING. - : ELSEVIER SCIENCE BV. - 0165-1684. ; 45:2, s. 255-259
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Journal article (other academic/artistic)abstract
- The weighted least-squares (LS) and total-least-squares (TLS) solutions to a perturbed system of linear equations are shown to be asymptotically equivalent (as the perturbation level goes to zero). An immediate consequence of this fact is that an optimall
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- Viberg, M, et al.
(author)
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Maximum likelihood array processing in spatially correlated noise fields using parameterized signals
- 1997
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In: IEEE TRANSACTIONS ON SIGNAL PROCESSING. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1053-587X. ; 45:4, s. 996-1004
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Journal article (other academic/artistic)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 i
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