| 1. |
- Gorokhov, Alexei, et al.
(author)
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Robust Blind Second-Order Deconvolution
- 1999
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In: IEEE Signal Processing Letters. - : IEEE Signal Processing Society. - 1070-9908 .- 1558-2361. ; 6:1, s. 13-16
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Journal article (peer-reviewed)abstract
- Second-order blind deconvolution of single input multiple output (SIMO) finite impulse response (FIR) channels is considered. A major drawback of several blind diversity techniques using antenna arrays/temporal-oversampling is high sensitivity to the choice of the model order. In this contribution, a robust method using only the second-order statistics is described. It provides high and robust 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.
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| 2. |
- Kristensson, Martin, et al.
(author)
-
Modified IQML and Weighted Subspace Fitting without Eigendecomposition
- 1999
-
In: Signal Processing. - : Elsevier North-Holland, Inc. Amsterdam, The Netherlands. - 0165-1684. ; 79:1, s. 29-44
-
Journal article (peer-reviewed)abstract
- This paper deals with direction estimation of signals impinging on a uniform linear sensor array. A well-known algorithm for this problem is IQML. However, estimates computed with IQML 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 (WSF-E) with an estimation error covariance which is asymptotically identical to the asymptotic Cramér–Rao lower bound is presented. The WSF-E algorithm resembles weighted subspace fitting (WSF) or MODE, but achieves optimal performance without having to compute an eigendecomposition of the sample covariance matrix.
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| 3. |
- Kristensson, Martin, et al.
(author)
-
On Subspace Based Sinusoidal Frequency Estimation
- 1999
-
In: International Conference on Acoustics, Speech, and Signal Processing. ; , s. 1565-1568
-
Conference paper (peer-reviewed)abstract
- Subspace based methods for frequency estimation rely on a low-rank system model that is obtained by collecting the observed scalar valued data samples into vectors. Estimators such as MUSIC and ESPRIT have for some time been applied to this vector model. Also, a statistically attractive Markov-like procedure for this class of methods has been proposed in the literature. Herein, the Markov estimator is re-investigated. Several results regarding rank, performance, and structure are given in a compact manner. The results are used to establish the large sample equivalence of the Markov estimator and the approximate maximum likelihood (AML) algorithm proposed by Stoica et al. (see Automatica, vol.30, no.1, p.131-45, 1994).
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