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Selén, Y

Open this publication in new window or tab >>Automatic robust adaptive beamforming via ridge regression### Selén, Yngve

### Abrahamsson, Richard

### Stoica, Peter

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt184_0_j_idt188_some",{id:"formSmash:j_idt184:0:j_idt188:some",widgetVar:"widget_formSmash_j_idt184_0_j_idt188_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt184_0_j_idt188_otherAuthors",{id:"formSmash:j_idt184:0:j_idt188:otherAuthors",widgetVar:"widget_formSmash_j_idt184_0_j_idt188_otherAuthors",multiple:true}); 2008 (English)In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 88, no 1, p. 33-49Article in journal (Refereed) Published
##### National Category

Signal Processing
##### Identifiers

urn:nbn:se:uu:diva-104184 (URN)10.1016/j.sigpro.2007.07.003 (DOI)000250237100003 ()
#####

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Available from: 2009-05-27 Created: 2009-05-27 Last updated: 2018-10-01Bibliographically approved

Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.

Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.

Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.

Open this publication in new window or tab >>Empirical Bayes linear regression with unknown model order### Selén, Yngve

Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.### Larsson, Erik G.

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt184_1_j_idt188_some",{id:"formSmash:j_idt184:1:j_idt188:some",widgetVar:"widget_formSmash_j_idt184_1_j_idt188_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt184_1_j_idt188_otherAuthors",{id:"formSmash:j_idt184:1:j_idt188:otherAuthors",widgetVar:"widget_formSmash_j_idt184_1_j_idt188_otherAuthors",multiple:true}); 2008 (English)In: Digital signal processing (Print), ISSN 1051-2004, E-ISSN 1095-4333, Vol. 18, no 2, p. 236-248Article in journal (Refereed) Published
##### Abstract [en]

##### National Category

Signal Processing Information Systems
##### Identifiers

urn:nbn:se:uu:diva-10384 (URN)10.1016/j.dsp.2007.03.005 (DOI)000254781300013 ()
#####

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt184_1_j_idt188_j_idt359",{id:"formSmash:j_idt184:1:j_idt188:j_idt359",widgetVar:"widget_formSmash_j_idt184_1_j_idt188_j_idt359",multiple:true});
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##### Note

Conference Information: 14th European Signal Processing Conference
Florence, ITALY, SEP 04-08, 2006Available from: 2007-03-21 Created: 2007-03-21 Last updated: 2018-01-12Bibliographically approved

We study maximum a posteriori probability model order selection for linear regression models, assuming Gaussian distributed noise and coefficient vectors. For the same data model, we also derive the minimum mean-square error coefficient vector estimate. The approaches are denoted BOSS (Bayesian order selection strategy) and BPM (Bayesian parameter estimation method), respectively. In their simplest form, both BOSS and BPM require a priori knowledge of the distribution of the coefficients. However, under the assumption that the coefficient variance profile is smooth, we derive "empirical Bayesian" versions of our algorithms which estimate the coefficient variance profile from the observations and thus require little or no information from the user. We show in numerical examples that the estimators can outperform several classical methods, including the well-known AICc and BIC for model order selection.

Open this publication in new window or tab >>Estimation of semi-sparse radar range profiles### Selén, Yngve

Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.### Stoica, Peter

Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt184_2_j_idt188_some",{id:"formSmash:j_idt184:2:j_idt188:some",widgetVar:"widget_formSmash_j_idt184_2_j_idt188_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt184_2_j_idt188_otherAuthors",{id:"formSmash:j_idt184:2:j_idt188:otherAuthors",widgetVar:"widget_formSmash_j_idt184_2_j_idt188_otherAuthors",multiple:true}); 2008 (English)In: Digital signal processing (Print), ISSN 1051-2004, E-ISSN 1095-4333, Vol. 18, no 4, p. 543-560Article in journal (Refereed) Published
##### National Category

Signal Processing
##### Identifiers

urn:nbn:se:uu:diva-104407 (URN)10.1016/j.dsp.2007.09.004 (DOI)000256820200007 ()
#####

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Available from: 2009-05-28 Created: 2009-05-28 Last updated: 2018-10-01Bibliographically approved

Open this publication in new window or tab >>Automatic robust adaptive beamforming via ridge regression### Selén, Yngve

### Abrahamsson, Richard

### Stoica, Peter

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt184_3_j_idt188_some",{id:"formSmash:j_idt184:3:j_idt188:some",widgetVar:"widget_formSmash_j_idt184_3_j_idt188_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt184_3_j_idt188_otherAuthors",{id:"formSmash:j_idt184:3:j_idt188:otherAuthors",widgetVar:"widget_formSmash_j_idt184_3_j_idt188_otherAuthors",multiple:true}); 2007 (English)In: 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol II, Pts 1-3, 2007, p. 965-968Conference paper, Published paper (Refereed)
##### Abstract [en]

##### Series

International Conference on Acoustics Speech and Signal Processing (ICASSP), ISSN 1520-6149 ; 2
##### Keywords

minimum variance beamforming, Capon beamforming, robust beamforming, ridge regression, regularization
##### National Category

Engineering and Technology
##### Identifiers

urn:nbn:se:uu:diva-18255 (URN)000248908100242 ()
##### Conference

32nd IEEE International Conference on Acoustics, Speech and Signal Processing Honolulu, HI, APR 15-20, 2007
#####

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##### Note

Student Paper Award Winner: Honorary MentionAvailable from: 2007-04-24 Created: 2007-04-24 Last updated: 2018-10-01Bibliographically approved

Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.

Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.

Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.

In this paper we derive a class of new parameter free robust adaptive beamformers using the generalized sidelobe canceler reparameterization of the Capon beamformer. In this parameterization the minimum variance beamformer is obtained as the solution of a linear least squares problem. In the case of an inaccurate steering vector and/or few data snapshots this marginally overdetermined system gives an ill fit causing signal cancellation in the standard minimum variance solution. By regularizing the problem using ridge regression techniques we get a whole class of robust adaptive beamformers, none of which requires the choice of a user parameter. We also propose a novel empirical Bayes-based ridge regression technique. The performance is compared to other robust adaptive beamformers.

Open this publication in new window or tab >>Empirical Bayes linear regression with unknown model order### Selén, Yngve

Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.### Larsson, Erik G.

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt184_4_j_idt188_some",{id:"formSmash:j_idt184:4:j_idt188:some",widgetVar:"widget_formSmash_j_idt184_4_j_idt188_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt184_4_j_idt188_otherAuthors",{id:"formSmash:j_idt184:4:j_idt188:otherAuthors",widgetVar:"widget_formSmash_j_idt184_4_j_idt188_otherAuthors",multiple:true}); 2007 (English)In: 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol III, Pts 1-3, Proceedings, 2007, p. 773-776Conference paper, Published paper (Refereed)
##### Abstract [en]

##### Series

International Conference on Acoustics Speech and Signal Processing (ICASSP), ISSN 1520-6149 ; 3
##### Keywords

linear systems, Bayes procedures, modeling, least mean square methods, parameter estimation
##### National Category

Engineering and Technology
##### Identifiers

urn:nbn:se:uu:diva-18253 (URN)10.1109/ICASSP.2007.366794 (DOI)000248906600194 ()1-4244-0727-3 (ISBN)
##### Conference

32nd IEEE International Conference on Acoustics, Speech and Signal Processing Honolulu, HI, APR 15-20, 2007
#####

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Available from: 2006-12-20 Created: 2006-12-20 Last updated: 2011-04-14Bibliographically approved

We study the maximum a posteriori probability model order selection algorithm for linear regression models, assuming Gaussian distributed noise and coefficient vectors. For the same data model, we also derive the minimum mean-square error coefficient vector estimate. The approaches are denoted BOSS (Bayesian Order Selection Strategy) and BPM (Bayesian Parameter estimation Method), respectively. Both BOSS and BPM require a priori knowledge on the distribution of the coefficients. However, under the assumption that the coefficient variance profile is smooth, we derive "empirical Bayesian" versions of our algorithms, which require little or no information from the user. We show in numerical examples that the estimators can outperform several classical methods, including the well-known AIC and BIC for order selection.

Open this publication in new window or tab >>Enhanced covariance matrix estimators in adaptive beamforming### Abrahamsson, Richard

Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.### Selén, Yngve

Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.### Stoica, Peter

Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt184_5_j_idt188_some",{id:"formSmash:j_idt184:5:j_idt188:some",widgetVar:"widget_formSmash_j_idt184_5_j_idt188_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt184_5_j_idt188_otherAuthors",{id:"formSmash:j_idt184:5:j_idt188:otherAuthors",widgetVar:"widget_formSmash_j_idt184_5_j_idt188_otherAuthors",multiple:true}); 2007 (English)In: 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol II, Pts 1-3, 2007, p. 969-972Conference paper, Published paper (Refereed)
##### Abstract [en]

##### Series

International Conference on Acoustics Speech and Signal Processing (ICASSP), ISSN 1520-6149 ; 2
##### Keywords

array signal processing, covariance matrices, direction of arrival estimation
##### National Category

Engineering and Technology
##### Identifiers

urn:nbn:se:uu:diva-18247 (URN)000248908100243 ()
##### Conference

32nd IEEE International Conference on Acoustics, Speech and Signal Processing Honolulu, HI, APR 15-20, 2007
#####

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt184_5_j_idt188_j_idt359",{id:"formSmash:j_idt184:5:j_idt188:j_idt359",widgetVar:"widget_formSmash_j_idt184_5_j_idt188_j_idt359",multiple:true});
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Available from: 2006-12-20 Created: 2006-12-20 Last updated: 2018-10-01Bibliographically approved

In this paper a number of covariance matrix estimators suggested in the literature are compared in terms of their performance in the context of array signal processing. More specifically they are applied in adaptive beamforming which is known to be sensitive to errors in the covariance matrix estimate and where often only a limited amount of data is available for estimation. As many covariance matrix estimators have the form of diagonal loading or eigenvalue adjustments of the sample covariance matrix and as they sometimes offer robustness to array imperfections and finite sample error, they are compared to a recent robustified adaptive Capon beamforming (RCB) method which also has a diagonal loading interpretation. Some of the covariance estimators show a significant improvement over the sample covariance matrix and in some cases they match the performance of the RCB even when a priori knowledge, which is not available in practice, is used for choosing the user parameter of RCB.

Open this publication in new window or tab >>Linear Regression With a Sparse Parameter Vector### Larsson, Erik G.

### Selén, Yngve

Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt184_6_j_idt188_some",{id:"formSmash:j_idt184:6:j_idt188:some",widgetVar:"widget_formSmash_j_idt184_6_j_idt188_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt184_6_j_idt188_otherAuthors",{id:"formSmash:j_idt184:6:j_idt188:otherAuthors",widgetVar:"widget_formSmash_j_idt184_6_j_idt188_otherAuthors",multiple:true}); 2007 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 55, no 2, p. 451-460Article in journal (Refereed) Published
##### Abstract [en]

##### Keywords

Linear regression, sparse models, Bayesian inference, MMSE estimation, basis selection, model selection, variable selection, model averaging, Lasso
##### National Category

Engineering and Technology
##### Identifiers

urn:nbn:se:uu:diva-82080 (URN)10.1109/TSP.2006.887109 (DOI)000243952600005 ()
#####

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Available from: 2007-02-15 Created: 2007-02-15 Last updated: 2017-12-14Bibliographically approved

We consider linear regression under a model where the parameter vector is known to be sparse. Using a Bayesian framework, we derive the minimum mean-square error (MMSE) estimate of the parameter vector, and a computationally efficient approximation of it. We also derive an empirical-Bayesian version of the estimator, which does not need any a priori information, nor does it need the selection of any user parameters. As a byproduct, we obtain a powerful model (``basis'') selection tool for sparse models. The performance and robustness of our new estimators are illustrated via numerical examples.

Open this publication in new window or tab >>RAKE Receiver for Channels with a Sparse Impulse Response### Selén, Yngve

Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.### Larsson, Erik G.

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt184_7_j_idt188_some",{id:"formSmash:j_idt184:7:j_idt188:some",widgetVar:"widget_formSmash_j_idt184_7_j_idt188_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt184_7_j_idt188_otherAuthors",{id:"formSmash:j_idt184:7:j_idt188:otherAuthors",widgetVar:"widget_formSmash_j_idt184_7_j_idt188_otherAuthors",multiple:true}); 2007 (English)In: IEEE Transactions on Wireless Communications, ISSN 1536-1276, E-ISSN 1558-2248, Vol. 6, no 9, p. 3175-3180Article in journal (Refereed) Published
##### Abstract [en]

##### Keywords

RAKE receiver, diversity combining, optimal receiver, empirical Bayes
##### National Category

Computer and Information Sciences
##### Identifiers

urn:nbn:se:uu:diva-82079 (URN)10.1109/TWC.2007.06030033 (DOI)000249309000008 ()
#####

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Available from: 2007-09-05 Created: 2007-09-05 Last updated: 2018-01-13Bibliographically approved

We derive the optimal receiver for RAKE diversity combining on channels with a sparse impulse response. The receiver is based on the Bayesian philosophy and thus it requires the knowledge of certain a priori parameters. However, we also derive an empirical Bayesian version of our receiver, which does not require any a priori knowledge, nor the choice of any user parameters. We show that both versions of our detector can outperform a classical training-based maximum-ratio-combining detector.

Open this publication in new window or tab >>An approach to sparse model selection and averaging### Selén, Yngve

### Gudmundson, Erik

### Stoica, Peter

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt184_8_j_idt188_some",{id:"formSmash:j_idt184:8:j_idt188:some",widgetVar:"widget_formSmash_j_idt184_8_j_idt188_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt184_8_j_idt188_otherAuthors",{id:"formSmash:j_idt184:8:j_idt188:otherAuthors",widgetVar:"widget_formSmash_j_idt184_8_j_idt188_otherAuthors",multiple:true}); 2006 (English)In: Conference Record of the 2006 IEEE Instrumentation and Measurement Technology Conference (IMTC 2006): Sorrento, Italy 24-27 April 2006, 2006Conference paper, Published paper (Refereed)
##### Abstract [en]

##### Keywords

linear systems, model reduction, channel measurement, least squares estimation, parameter estimation, signal processing, system identification
##### National Category

Signal Processing
##### Identifiers

urn:nbn:se:uu:diva-79396 (URN)
#####

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Available from: 2006-12-20 Created: 2006-12-20 Last updated: 2018-10-01

Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Automatic control.

Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Automatic control.

Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Automatic control.

Parameter estimation when the true model structure is unknown is a commonly occurring task in measurement problems. In a sparse modeling scenario, the number of possible models grows exponentially with the total number of parameters. The full set of models therefore becomes computationally infeasible to handle. We propose a method, based on successive model reduction, for finding a sound and computationally feasible set of sparse linear regression models. Once this set of models has been found, standard model selection or model averaging techniques can be applied. We demonstrate the performance of our method by some numerical examples.

Open this publication in new window or tab >>Linear regression with a sparse parameter vector### Larsson, Erik G.

### Selén, Yngve

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##### Abstract [en]

##### Keywords

Linear regression, sparse models, Bayesian inference, MMSE estimation, variable selection, model averaging
##### National Category

Signal Processing
##### Identifiers

urn:nbn:se:uu:diva-79395 (URN)
#####

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Available from: 2006-04-07 Created: 2006-04-07

Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Automatic control.

We consider linear regression under a model where the parameter vector is known to be sparse. Using a Bayesian framework, we derive a computationally efficient approximation to the minimum mean-square error (MMSE) estimate of the parameter vector. The performance of the so-obtained estimate is illustrated via numerical examples.