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Sökning: swepub > Ottersten Björn 1961 > (2000-2004)

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1.
  • Kristensson, Martin, et al. (författare)
  • Further Results and Insights on Subspace Based Sinusoidal Frequency Estimation
  • 2001
  • Ingår i: IEEE Transactions on Signal Processing. - IEEE Signal Processing Society. - 1053-587X. ; 49:12, s. 2962-2974
  • Tidskriftsartikel (refereegranskat)abstract
    • Subspace-based methods for parameter identification have received considerable attention in the literature. Starting with a scalar-valued process, it is well known that subspace-based identification of sinusoidal frequencies is possible if the scalar valued data is windowed to form a low-rank vector-valued process. MUSIC and ESPRIT-like estimators have, for some time, been applied to this vector model. In addition, a statistically attractive Markov-like procedure for this class of methods has been proposed. Herein, the Markov-like procedure is reinvestigated. Several results regarding rank, performance, and structure are given in a compact manner. The large sample equivalence with the approximate maximum likelihood method by Stoica et al. (1988) is also established
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2.
  • Bengtsson, Mats, 1967-, et al. (författare)
  • A generalization of weighted subspace fitting to full-rank models
  • 2001
  • Ingår i: IEEE Transactions on Signal Processing. - 1053-587X. ; 49:5, s. 1002-1012
  • Tidskriftsartikel (refereegranskat)abstract
    • The idea of subspace fitting provides a popular framework for different applications of parameter estimation and system identification. Previously, some algorithms have been suggested based on similar ideas, for a sensor array processing problem where the underlying data model is not low rank. We show that two of these algorithms (DSPE and DISPARE) fail to give consistent estimates and introduce a general class of subspace fitting-like algorithms for consistent estimation of parameters from a possibly full-rank data model. The asymptotic performance is analyzed, and an optimally weighted algorithm is derived. The result gives a lower bound on the estimation performance for any estimator based on a low-rank approximation of the linear space spanned by the sample data. We show that in general, for full-rank data models, no subspace-based method can reach the Cramer-Rao lower bound (CRB)
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4.
  • Hyberg, Per, et al. (författare)
  • Array Mapping : Optimal Transformation Matrix Design
  • 2002
  • Ingår i: Proceedings IEEE International Conference on Acoustics, Speech, and Signal Processing. - IEEE. ; s. 2905-2908
  • Konferensbidrag (refereegranskat)abstract
    • Mapping of the data output vector from an existing antenna array onto the data vector of an imaginary array of more suitable configuration is well known in array signal processing. By mapping onto an array manifold of lower dimension or uniform structure for example., processing speed can be improved. Conditions for the retaining of DOA error variance under such mapping have been formulated by several authors but the equally important systematic mapping errors, the bias, has been less treated to date. This paper uses a geometrical interpretation of a Taylor expansion of the DOA estimator cost function to derive an alternative design of the mapping matrix that almost completely removes the bias. The key feature of the proposed design is that it takes the orthogonality between the manifold mapping errors and certain gradients of the estimator cost function into account.
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7.
  • Hybergand, Per, et al. (författare)
  • Sector Array Mapping : Transformation Matrix Design for Minimum MSE
  • 2002
  • Ingår i: Proceedings Asilomar Conference on Signals, Systems & Computers. - IEEE. ; s. 1288-1292
  • Konferensbidrag (refereegranskat)abstract
    • The paper treats mapping (interpolation) of the output vector from an existing antenna array onto the output vector of an imaginary array when the directions of arrival (DOA) are known only to within a sector. The problem of constructing a mapping matrix, common to the sector, that minimizes DOA mean square error (MSE) across the sector, is analyzed. We derive a general condition on the mapping errors that prevents them from affecting the calculated DOAs. Thereafter, we propose a design algorithm for the transformation matrix that generates mapping errors fulfilling this condition. Simulations show conspicuous MSE improvements in relevant scenarios.
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8.
  • Jaldén, Joakim, 1976-, et al. (författare)
  • An Exponential Lower Bound on the Expected Complexity of Sphere Decoding
  • 2004
  • Ingår i: Proceedings IEEE International Conference on Acoustics, Speech, and Signal Processing. ; s. 393-396
  • Konferensbidrag (refereegranskat)abstract
    • The sphere decoding algorithm is an efficient algorithm used to solve the maximum likelihood detection problem in several digital communication systems. The sphere decoding algorithm has previously been claimed to have polynomial expected complexity. While it is true that the algorithm has an expected complexity comparable to that of other polynomial time algorithms for problems of moderate size it is a misconception that the expected number of operations asymptotically grow as a polynomial function of the problem size. In order to illustrate this point we derive an exponential lower bound on the expected complexity of the sphere decoder.
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9.
  • Jaldén, Joakim, 1976-, et al. (författare)
  • Semidefinite Programming for Detection in Linear Systems – Optimality Conditions and Space-Time Decoding
  • 2003
  • Ingår i: IEEE International Conference on Acoustics, Speech, and Signal Processing. - IEEE. ; s. 9-12
  • Konferensbidrag (refereegranskat)abstract
    • Optimal maximum likelihood detection of finite alphabet symbols in general requires time consuming exhaustive search methods. The computational complexity of such techniques is exponential in the size of the problem and for large problems sub-optimal algorithms are required. In this paper, to find a solution in polynomial time, a semidefinite programming approach is taken to estimate binary symbols in a general linear system. A condition under which the proposed method provides optimal solutions is derived. As an application, the proposed algorithm is used as a decoder for a linear space-time block coding system and the results are illustrated with numerical examples.
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10.
  • Jansson, Magnus, et al. (författare)
  • Structured Covariance Matrix Estimation : A Parametric Approach
  • 2000
  • Ingår i: Proceedings IEEE International Conference on Acoustics, Speech, and Signal Processing. - IEEE. ; s. 3172-3175
  • Konferensbidrag (refereegranskat)abstract
    • The problem of estimating a positive semi-definite Toeplitz covariance matrix consisting of a low rank matrix plus a scaled identity from noisy data arises in many applications. We propose a computationally attractive (noniterative) covariance matrix estimator with certain optimality properties. For example, under suitable assumptions the proposed estimator achieves the Cramer-Rao lower bound on the covariance matrix parameters. The resulting covariance matrix estimate is also guaranteed to possess all of the structural properties of the true covariance matrix. Previous approaches to this problem have either resulted in computationally unattractive iterative solutions or have provided estimates that only satisfy some of the structural relations
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  • Resultat 1-10 av 42
  • [1]2345Nästa
 
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