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Sökning: swepub > Ottersten Björn 1961 > Kungliga Tekniska Högskolan > Stoica Petre

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1.
  • Stoica, Petre, et al. (författare)
  • Maximum Likelihood Array Processing for Stochastic Coherent Sources
  • 1996
  • Ingår i: In IEEE Trans. on Signal Processing. - IEEE Signal Processing Society. - 1053-587X. ; 44:1, s. 96-105
  • Tidskriftsartikel (refereegranskat)abstract
    • Maximum likelihood (ML) estimation in array signal processing for the stochastic noncoherent signal case is well documented in the literature. We focus on the equally relevant case of stochastic coherent signals. Explicit large-sample realizations are derived for the ML estimates of the noise power and the (singular) signal covariance matrix. The asymptotic properties of the estimates are examined, and some numerical examples are provided. In addition, we show the surprising fact that the ML estimates of the signal parameters obtained by ignoring the information that the sources are coherent coincide in large samples with the ML estimates obtained by exploiting the coherent source information. Thus, the ML signal parameter estimator derived for the noncoherent case (or its large-sample realizations) asymptotically achieves the lowest possible estimation error variance (corresponding to the coherent Cramer-Rao bound).
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2.
  • Ottersten, Björn, 1961-, et al. (författare)
  • Exact and Large Sample ML Techniques for Parameter Estimation and Detection in Array Processing
  • 1993
  • Ingår i: Radar Array Processing. - Berlin ; New York : Springer Berlin/Heidelberg. - 3-540-55224-3 (Berlin : acid-free paper) - 978-3-540-55224-6 (Berlin : acid-free paper) - 0-387-55224-3 (New York : acid-free paper) - 978-0-387-55224-8 (New York : acid-free paper) ; s. 99-151
  • Bokkapitel (övrigt vetenskapligt)abstract
    • Sensor array signal processing deals with the problem of extracting information from a collection of measurements obtained from sensors distributed in space. The number of signals present is assumed to be finite, and each signal is parameterized by a finite number of parameters. Based on measurements of the array output, the objective is to estimate the signals and their parameters. This research area has attracted considerable interest for several years. A vast number of algorithms has appeared in the literature for estimating unknown signal parameters from the measured output of a sensor array.
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3.
  • Stoica, Petre, et al. (författare)
  • Comments on "Min-Norm interpretations and consistency of MUSIC, MODE, and ML"
  • 1998
  • Ingår i: IEEE Transactions on Signal Processing. - 1053-587X. ; 46:8, s. 2262-2263
  • Tidskriftsartikel (refereegranskat)abstract
    • The results and interpretations obtained in the above referred paper are shown to be well known or obvious. Additionally, corrections to some misleading statements in the aforementioned paper are presented.
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4.
  • Stoica, Petre, et al. (författare)
  • Instrumental Variable Approach to Array Processing in Spatially Correlated Noise Fields
  • 1994
  • Ingår i: IEEE Transactions on Signal Processing. - 1053-587X. ; 42:1, s. 121-133
  • Tidskriftsartikel (refereegranskat)abstract
    • High-performance signal parameter estimation from sensor array data is a problem which has received much attention. A number of so-called eigenvector (EV) techniques such as MUSIC, ESPRIT, WSF, and MODE have been proposed in the literature. The EV techniques for array processing require knowledge of the spatial noise correlation matrix that constitutes a significant drawback. A novel instrumental variable (IV) approach to the sensor array problem is proposed. The IV technique relies on the same basic geometric properties as the EV methods to obtain parameter estimates. However, by exploiting the temporal correlation of the source signals, no knowledge of the spatial noise covariance is required. The asymptotic properties of the IV estimator are examined and an optimal IV method is derived. Computer simulations are presented to study the properties of the IV estimators in samples of practical length. The proposed algorithm is also shown to perform better than MUSIC on a full-scale passive sonar experiment.
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5.
  • Stoica, Petre, et al. (författare)
  • The evil of superefficiency
  • 1996
  • Ingår i: Signal Processing. - 0165-1684. ; 55:1, s. 133-136
  • Tidskriftsartikel (refereegranskat)abstract
    • We discuss the intriguing notion of statistical superefficiency in a straightforward manner with a minimum of formality. We point out that for any given parameter estimator there exist other estimators which have a strictly lower asymptotic variance and hence are statistically more efficient than the former. In particular, if the former estimator was statistically efficient (in the sense that its asymptotic variance was equal to the Cramer-Rao bound) then the latter estimators could be called ''superefficient''. Among others, the phenomenon of superefficiency implies that asymptotically there exists no uniformly minimum-variance parameter estimator.
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6.
  • Viberg, Mats, et al. (författare)
  • Array Processing in Correlated Noise Fields Based on Instrumental Variables and Subspace Fitting
  • 1995
  • Ingår i: IEEE Transactions on Signal Processing. - 1053-587X. ; 43:5, s. 1187-1199
  • Tidskriftsartikel (refereegranskat)abstract
    • Accurate signal parameter estimation from sensor array data is a problem which has received much attention in the last decade. A number of parametric estimation techniques have been proposed in the literature. In general, these methods require knowledge of the sensor-to-sensor correlation of the noise, which constitutes a significant drawback. This difficulty can be overcome only by introducing alternative assumptions that enable separating the signals from the noise. In some applications, the raw sensor outputs can be preprocessed so that the emitter signals are temporally correlated with correlation length longer than that of the noise. An instrumental variable (IV) approach can then be used for estimating the signal parameters without knowledge of the spatial color of the noise. A computationally simple IV approach has recently been proposed by the authors. Herein, a refined technique that can give significantly better performance is derived. A statistical analysis of the parameter estimates is performed, enabling optimal selection of certain user-specified quantities. A lower bound on the attainable error variance is also presented. The proposed optimal IV method is shown to attain the bound if the signals have a quasideterministic character
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7.
  • Ottersten, Björn, 1961-, et al. (författare)
  • Covariance matching estimation techniques for array signal processing applications
  • 1998
  • Ingår i: Digital signal processing (Print). - Academic Press. - 1051-2004. ; 8:3, s. 185-210
  • Tidskriftsartikel (refereegranskat)abstract
    • A class of covariance matching estimation techniques (COMET) has recently attracted interest in the signal processing community. These techniques have their roots in the statistical literature where they are sometimes referred to as generalized least squares methods. Covariance matching is an alternative to maximum likelihood estimation, providing the same large sample properties often at a lower computational cost. Herein, we present a general framework. for covariance matching techniques and show that they are well suited to solve several problems arising in array signal processing. A straightforward derivation of the COMET criterion from first principles is presented, which also establishes the large sample properties of the estimator. Closed form compact expressions for the asymptotic covariance of the estimates of the parameters of interest are also derived. Some detection schemes are reviewed and two COMET-based detection schemes are proposed. The main part of the paper treats three applications where the COMET approach proves interesting. First, we consider the localization of underwater sources using a hydro-acoustic array. The background noise is often spatially correlated in such an application and this must be taken into account in the estimation procedure. Second, the problem of channel estimation in wireless communications is treated. In digital communications, an estimate of the channel is often required to perform accurate demodulation as well as spatially selective transmission. Finally, a radar detection problem is formulated and the proposed detection schemes are evaluated.
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8.
  • Stoica, Petre, et al. (författare)
  • Optimal Array Signal Processing in the Presence of Coherent Wavefronts
  • 1996
  • Ingår i: Proceedings IEEE International Conference on Acoustics, Speech, and Signal Processing.  ICASSP-96. - IEEE. ; s. 2904-2907
  • Konferensbidrag (refereegranskat)abstract
    • The problem of estimating the parameters of several wavefronts from the measurements of multiple sensors is often referred to as array signal processing. The maximum likelihood (ML) estimator in array signal processing forthe case of non-coherent signals has been studied extensively. The focus here is on the ML estimator for thecase of stochastic coherent signals which arises due to, for example, specular multipath propagation. We showthe very surprising fact that the ML estimates of the signal parameters obtained by ignoring the information thatthe sources are coherent, coincide in large samples with the ML estimates obtained by exploiting the coherentsource information. Thus, the ML signal parameter estimator derived for the non-coherent case (or its large-sample realizations such as MODE os WSF) asymptotically achieves the lowest possible estimation error variance (corresponding to the coherent Cramer-Rao bound)
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9.
  • Stoica, Petre, et al. (författare)
  • Optimal Localization of Partially Known Signals in Unknown Noise Fields
  • 1994
  • Ingår i: Proc.  IEEE International Conference on Acoustics, Speech, and Signal Processing ICASSP-94. - IEEE. ; s. 217-220
  • Konferensbidrag (refereegranskat)abstract
    • Most methods for sensor array signal processing require the covariance matrix of the background noise to beknown. Various techniques for overcoming this limitation have recently been proposed. While most of these are based on assumptions on the noise, we present herein an alternative approach based on partial knowledge of thesignals. Methods yielding minimum variance estimates for the model in question are presented and analyzed.
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10.
  • Viberg, Mats, et al. (författare)
  • Array Processing in Correlated Noise Fields Using Instrumental Variables and Subspace Fitting
  • 1992
  • Ingår i: The Twenty-Sixth Asilomar Conference on Signals, Systems and Computers. - IEEE. ; s. 1147-1151
  • Konferensbidrag (refereegranskat)abstract
    • An improved technique for direction-of-arrival estimation of temporally correlated signals in the presence of spatially colored, but temporally uncorrelated, noise is presented. The method is particularly suited to applications in which the receiver bandwidth exceeds that of the emitter signals. A statistical performance analysis shows that the method nearly achieves the deterministic Cramer-Rao bound if the signals are sufficiently predictable. A Monte Carlo experiment suggests that the theoretical estimation error variance well predicts the empirical mean square error down to the threshold region.
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