| 1. |
- Ling, Jun, et al.
(författare)
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Efficient channel equalization for MIMO underwater acoustic communications
- 2010
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Ingår i: 2010 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM). - Piscataway, NJ : IEEE Communications Society. - 9781424489787 - 9781424493951 ; , s. 73-76
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Konferensbidrag (refereegranskat)abstract
- Linear minimum mean-squared error (LMMSE)-based channel equalization is widely used in multi-input multioutput (MIMO) underwater acoustic communications (UAC). The practical challenge of LMMSE based schemes is the necessity of matrix inversion which generally imposes heavy computational burden on the receiver. To obtain the LMMSE filters efficiently, we exploit the conjugate gradient method and the diagonalization properties of circulant matrices. The proposed scheme is based on fast Fourier transform operations and can be implemented in parallel, which makes it a promising candidate for real-time MIMO underwater acoustic communications. Both numerical and SPACE'08 experimental examples are presented to demonstrate the effectiveness of the proposed approach.
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| 2. |
- Nordenvaad, Magnus Lundberg
(författare)
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A hierarchical approach to noise-adaptive estimation
- 2010
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Ingår i: 2010 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM). - Piscataway, NJ : IEEE Communications Society. - 9781424493951 - 9781424493951 ; , s. 161-164
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Konferensbidrag (refereegranskat)abstract
- This paper presents a noise-adaptive estimator for the linear model. The strategy is based on a hierarchical approach where in each step, a decreasing number of unbiased estimates for the parameter of interest is produced. In this way, the complexity is greatly reduced compared to standard estimators, like the adaptive maximum likelihood (AML) estimator. Also, since the method combines solutions to sub-problems of smaller dimensionality, the required size of the noise training data set is also reduced. As a result, the derived scheme performs better than AML for small sample support. The results are verified by simulations and show that the derived scheme is a very appropriate choice for a large class of problems with high dimensionality.
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| 3. |
- Wirfält, Petter, 1979-, et al.
(författare)
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On Toeplitz and Kronecker Structured Covariance Matrix Estimation
- 2010
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Ingår i: Sensor Array and Multichannel Signal Processing Workshop. - : IEEE. - 9781424493951 ; , s. 185-188
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Konferensbidrag (refereegranskat)abstract
- A number of signal processing applications require the estimation of covariance matrices. Sometimes, the particular scenario or system imparts a certain theoretical structure on the matrices that are to be estimated. Using this knowledge allows the design of algorithms exploiting such structure, resulting in more robust and accurate estimators, especially for small samples. We study a scenario with a measured covariance matrix known to be the Kronecker product of two other, possibly structured, covariance matrices that are to be estimated. Examples of scenarios in which such a problem occurs are MIMO-communications and EEG measurements. When the matrices that are to be estimated are Toeplitz structured, we show our algorithms to be able to achieve the Cramér-Rao Lower Bound already at very small sample sizes.
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