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| 2. |
- Gorokhov, Alexei, et al.
(författare)
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Robust Blind Second Order Deconvolution of Multiple FIR Channels
- 1998
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Ingår i: Proceedings IEEE Global Telecommunications Conference, 1998. GLOBECOM 98. The Bridge to Global Integration.. - : IEEE. ; , s. 2062-2067
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Konferensbidrag (refereegranskat)abstract
- Second order blind deconvolution of single input multiple output (SIMO) FIR channels is considered herein. A major drawback of several blind diversity techniques using antenna arrays/temporal-oversampling is high sensitivity to the choice of model order. In this article, a robust method using only the second order statistics is described. It provides high estimation accuracy even for a limited sample size and an unknown model order. In contrast to other suggested approaches, that often exploit properties valid only in the large sample case, the proposed method is applicable both in the large sample and high signal-to-noise ratio (SNR) scenario; it also enjoys a simple implementation.
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| 3. |
- Kristensson, Martin, et al.
(författare)
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A statistical approach to subspace based blind identification
- 1998
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Ingår i: IEEE Transactions on Signal Processing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1053-587X .- 1941-0476. ; 46:6, s. 1612-1623
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Tidskriftsartikel (refereegranskat)abstract
- Blind identification of single input multiple output systems is considered herein. The low-rank structure of the output signal is exploited to blindly identify the channel using a subspace fitting framework. Two approaches based on a minimal linear parameterization of a subspace are presented and analyzed. The asymptotically best consistent estimate is derived for the class of blind subspace-based techniques. The asymptotic estimation error covariance of the subspace estimates is derived, and the corresponding covariance of the statistically optimal estimates provides a lower bound on the estimation error covariance of subspace methods. A two-step procedure involving only linear systems of equations is presented that asymptotically achieves the bound. Simulations and numerical examples are provided to compare the two approaches.
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| 4. |
- Kristensson, Martin, et al.
(författare)
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Further Results on Optimally Weighted Subspace Based Blind Channel Estimation
- 1998
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Ingår i: Proceedings of the 32th Asilomar Conference on Signals, Systems and Computers. - : IEEE. ; , s. 1579-1583
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Konferensbidrag (refereegranskat)abstract
- Subspace based identification of model parameters requires a low rank data model. To obtain such a model it is common to collect several snapshots of the observed vector valued sequence in a larger vector. In many cases, this “window” procedure results in a new vector valued process which can be viewed as originating from a low rank data model. When studying the performance of weighted subspace based techniques this window procedure complicates both the analysis and the implementation. In this paper an attempt is made to clarify the theoretical and numerical difficulties when applying weighting in such scenarios
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| 5. |
- Kristensson, Martin, et al.
(författare)
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Modified IQML and a Statistically Efficient Method for Direction Estimation without Eigendecomposition
- 1998
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Ingår i: Proceedings IEEE International Conference on Acoustics, Speech, and Signal Processing. - : IEEE. ; , s. 2069-2072
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Konferensbidrag (refereegranskat)abstract
- This paper deals with direction estimation of signals impinging on a uniform linear sensor array. A well known algorithm for this problem is iterative quadratic maximum likelihood (IQML). Unfortunately, the IQML estimates are in general biased, especially in noisy scenarios. We propose a modification of IQML (MIQML) that gives consistent estimates at approximately the same computational cost. In addition, an algorithm with an estimation error covariance which is asymptotically identical to the asymptotic Cramer-Rao lower bound is presented. The optimal algorithm resembles weighted subspace fitting or MODE, but achieves optimal performance without having to compute an eigendecomposition of the sample covariance matrix.
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