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From: kay@orca.NoSubdomain.NoDomain (Steve Kay)
Subject: Course Announcement
Keywords: data processing, spectral estimation, high resolution
Sender: kay@orca (Steve Kay)
Organization: URI Department of Electrical Engineering
Message-ID: <D6KCz6.L7w@egr.uri.edu>
Date: Wed, 5 Apr 1995 13:18:41 GMT
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Xref: glinda.oz.cs.cmu.edu sci.nonlinear:2828 sci.stat.math:4845 sci.image.processing:13792


*****************SHORT COURSE ANNOUNCEMENT*********************

                   MODERN SPECTRAL ANALYSIS

TIME: May 31-June 2, 1995,  8:30-4:00 PM

PLACE:  Holiday Inn
        College Park, Maryland

INSTRUCTOR:  Dr. Steven Kay          Email: kay@ele.uri.edu

TUITION:   $990

                        Course Outline

The Spectral Estimation Problem - Review of random process theory, 
spectral estimation and filtering, applications to sonar, speech, data 
processing, etc., parametric vs. nonparametric approaches, concept of 
resolution, comparison of estimators 

Classical (Fourier Methods) - Periodogram, averaged periodogram, bias-
variance tradeoffs, computation of periodogram, Blackman-Tukey, 
correlation estimation, resolution comparisons 

Time Series Modeling - Time series model definitions -  AR/MA/ARMA, 
Yule-Walker equations, Levinson algorithm, model fitting of 
empirical data, effect of observation noise 

Statistical estimation review - maximum likelihood estimation (MLE), 
Cramer-Rao lower bound 

Autoregressive Spectral Estimation - Autocorrelation, covariance, 
modified covariance (forward/backward), Burg, and recursive maximum 
likelihood methods, properties of estimators, linear 
prediction/maximum entropy/autoregressive modeling relationships, 
reflection coefficients and lattice filters, autocorrelation matching, 
model order selection - Akaike and MDL, observation noise effects, 
performance for sinusoids in noise 

Autoregressive Moving Average Spectral Estimation - Akaike MLE and 
iterative implementations, modified Yule-Walker, least squares 
modified Yule-Walker, input/output identification approaches (two 
stage least squares), choosing the best method for your application 

Moving Average spectral estimation - Durbin's method and performance

Capon's Method (MLM) - Filtering interpretation, comparison to 
periodogram, resolution vs. conventional and autoregressive estimators 

Sinusoidal Frequency Estimation - signal and noise subspaces, 
eigenanalysis of covariance matrix, MLE, 
periodogram, principal components/SVD approaches, MUSIC, Pisarenko 
methods, iterative filtering method, statistical performance vs. CRLB 
for all methods 

Empirical Spectral Estimation - Examples using the Modern Interactive 
Spectral Analysis software package, hands-on experience by student 
analysis of mystery data sets, evaluation of student-supplied data 
sets


                        Course Material

Each participant will receive a copy of the text Modern Spectral 
Estimation: Theory and Application by Steven Kay (Prentice-Hall 1988), 
which includes Fortran subroutines for all the basic methods, 
suggested problems and solutions, extensive handouts, including notes 
and copies of all lecture slides, and a copy of the menu-driven 
software package, Modern Interactive Spectral Analysis (MISA), for use 
on a PC as well as an instructional laboratory notebook.   Attendees 
are encouraged to bring their own laptop PCs to perform the lab 
experiments or to analyze their own data sets. 

For more information please call or fax the Applied Technology 
Institute.  PHONE: 410-531-6034       FAX: 410-531-1013





Path: orca!kay
Newsgroups: comp.dsp, sci.nonlinear, sci.stat.math, sci.chaos,
sci.image.processing
Distribution: world
Followup-To: 
From: kay@orca.NoSubdomain.NoDomain (Steve Kay)
Organization: URI Department of Electrical Engineering
Subject: Course Announcement
Keywords: data processing, spectral estimation, high resolution



*****************SHORT COURSE ANNOUNCEMENT*********************

                   MODERN SPECTRAL ANALYSIS

TIME: May 31-June 2, 1995,  8:30-4:00 PM

PLACE:  Holiday Inn
        College Park, Maryland

INSTRUCTOR:  Dr. Steven Kay          Email: kay@ele.uri.edu

TUITION:   $990

                        Course Outline

The Spectral Estimation Problem - Review of random process theory, 
spectral estimation and filtering, applications to sonar, speech, data 
processing, etc., parametric vs. nonparametric approaches, concept of 
resolution, comparison of estimators 

Classical (Fourier Methods) - Periodogram, averaged periodogram, bias-
variance tradeoffs, computation of periodogram, Blackman-Tukey, 
correlation estimation, resolution comparisons 

Time Series Modeling - Time series model definitions -  AR/MA/ARMA, 
Yule-Walker equations, Levinson algorithm, model fitting of 
empirical data, effect of observation noise 

Statistical estimation review - maximum likelihood estimation (MLE), 
Cramer-Rao lower bound 

Autoregressive Spectral Estimation - Autocorrelation, covariance, 
modified covariance (forward/backward), Burg, and recursive maximum 
likelihood methods, properties of estimators, linear 
prediction/maximum entropy/autoregressive modeling relationships, 
reflection coefficients and lattice filters, autocorrelation matching, 
model order selection - Akaike and MDL, observation noise effects, 
performance for sinusoids in noise 

Autoregressive Moving Average Spectral Estimation - Akaike MLE and 
iterative implementations, modified Yule-Walker, least squares 
modified Yule-Walker, input/output identification approaches (two 
stage least squares), choosing the best method for your application 

Moving Average spectral estimation - Durbin's method and performance

Capon's Method (MLM) - Filtering interpretation, comparison to 
periodogram, resolution vs. conventional and autoregressive estimators 

Sinusoidal Frequency Estimation - signal and noise subspaces, 
eigenanalysis of covariance matrix, MLE, 
periodogram, principal components/SVD approaches, MUSIC, Pisarenko 
methods, iterative filtering method, statistical performance vs. CRLB 
for all methods 

Empirical Spectral Estimation - Examples using the Modern Interactive 
Spectral Analysis software package, hands-on experience by student 
analysis of mystery data sets, evaluation of student-supplied data 
sets


                        Course Material

Each participant will receive a copy of the text Modern Spectral 
Estimation: Theory and Application by Steven Kay (Prentice-Hall 1988), 
which includes Fortran subroutines for all the basic methods, 
suggested problems and solutions, extensive handouts, including notes 
and copies of all lecture slides, and a copy of the menu-driven 
software package, Modern Interactive Spectral Analysis (MISA), for use 
on a PC as well as an instructional laboratory notebook.   Attendees 
are encouraged to bring their own laptop PCs to perform the lab 
experiments or to analyze their own data sets. 

For more information please call or fax the Applied Technology 
Institute.  PHONE: 410-531-6034       FAX: 410-531-1013





Path: orca!kay
Newsgroups: comp.dsp, sci.nonlinear, sci.stat.math, sci.chaos,
sci.image.processing
Distribution: world
Followup-To: 
From: kay@orca.NoSubdomain.NoDomain (Steve Kay)
Organization: URI Department of Electrical Engineering
Subject: Course Announcement
Keywords: data processing, spectral estimation, high resolution



*****************SHORT COURSE ANNOUNCEMENT*********************

                   MODERN SPECTRAL ANALYSIS

TIME: May 31-June 2, 1995,  8:30-4:00 PM

PLACE:  Holiday Inn
        College Park, Maryland

INSTRUCTOR:  Dr. Steven Kay          Email: kay@ele.uri.edu

TUITION:   $990

                        Course Outline

The Spectral Estimation Problem - Review of random process theory, 
spectral estimation and filtering, applications to sonar, speech, data 
processing, etc., parametric vs. nonparametric approaches, concept of 
resolution, comparison of estimators 

Classical (Fourier Methods) - Periodogram, averaged periodogram, bias-
variance tradeoffs, computation of periodogram, Blackman-Tukey, 
correlation estimation, resolution comparisons 

Time Series Modeling - Time series model definitions -  AR/MA/ARMA, 
Yule-Walker equations, Levinson algorithm, model fitting of 
empirical data, effect of observation noise 

Statistical estimation review - maximum likelihood estimation (MLE), 
Cramer-Rao lower bound 

Autoregressive Spectral Estimation - Autocorrelation, covariance, 
modified covariance (forward/backward), Burg, and recursive maximum 
likelihood methods, properties of estimators, linear 
prediction/maximum entropy/autoregressive modeling relationships, 
reflection coefficients and lattice filters, autocorrelation matching, 
model order selection - Akaike and MDL, observation noise effects, 
performance for sinusoids in noise 

Autoregressive Moving Average Spectral Estimation - Akaike MLE and 
iterative implementations, modified Yule-Walker, least squares 
modified Yule-Walker, input/output identification approaches (two 
stage least squares), choosing the best method for your application 

Moving Average spectral estimation - Durbin's method and performance

Capon's Method (MLM) - Filtering interpretation, comparison to 
periodogram, resolution vs. conventional and autoregressive estimators 

Sinusoidal Frequency Estimation - signal and noise subspaces, 
eigenanalysis of covariance matrix, MLE, 
periodogram, principal components/SVD approaches, MUSIC, Pisarenko 
methods, iterative filtering method, statistical performance vs. CRLB 
for all methods 

Empirical Spectral Estimation - Examples using the Modern Interactive 
Spectral Analysis software package, hands-on experience by student 
analysis of mystery data sets, evaluation of student-supplied data 
sets


                        Course Material

Each participant will receive a copy of the text Modern Spectral 
Estimation: Theory and Application by Steven Kay (Prentice-Hall 1988), 
which includes Fortran subroutines for all the basic methods, 
suggested problems and solutions, extensive handouts, including notes 
and copies of all lecture slides, and a copy of the menu-driven 
software package, Modern Interactive Spectral Analysis (MISA), for use 
on a PC as well as an instructional laboratory notebook.   Attendees 
are encouraged to bring their own laptop PCs to perform the lab 
experiments or to analyze their own data sets. 

For more information please call or fax the Applied Technology 
Institute.  PHONE: 410-531-6034       FAX: 410-531-1013





Path: orca!kay
Newsgroups: comp.dsp, sci.nonlinear, sci.stat.math, sci.chaos,
sci.image.processing
Distribution: world
Followup-To: 
From: kay@orca.NoSubdomain.NoDomain (Steve Kay)
Organization: URI Department of Electrical Engineering
Subject: Course Announcement
Keywords: data processing, spectral estimation, high resolution



*****************SHORT COURSE ANNOUNCEMENT*********************

                   MODERN SPECTRAL ANALYSIS

TIME: May 31-June 2, 1995,  8:30-4:00 PM

PLACE:  Holiday Inn
        College Park, Maryland

INSTRUCTOR:  Dr. Steven Kay          Email: kay@ele.uri.edu

TUITION:   $990

                        Course Outline

The Spectral Estimation Problem - Review of random process theory, 
spectral estimation and filtering, applications to sonar, speech, data 
processing, etc., parametric vs. nonparametric approaches, concept of 
resolution, comparison of estimators 

Classical (Fourier Methods) - Periodogram, averaged periodogram, bias-
variance tradeoffs, computation of periodogram, Blackman-Tukey, 
correlation estimation, resolution comparisons 

Time Series Modeling - Time series model definitions -  AR/MA/ARMA, 
Yule-Walker equations, Levinson algorithm, model fitting of 
empirical data, effect of observation noise 

Statistical estimation review - maximum likelihood estimation (MLE), 
Cramer-Rao lower bound 

Autoregressive Spectral Estimation - Autocorrelation, covariance, 
modified covariance (forward/backward), Burg, and recursive maximum 
likelihood methods, properties of estimators, linear 
prediction/maximum entropy/autoregressive modeling relationships, 
reflection coefficients and lattice filters, autocorrelation matching, 
model order selection - Akaike and MDL, observation noise effects, 
performance for sinusoids in noise 

Autoregressive Moving Average Spectral Estimation - Akaike MLE and 
iterative implementations, modified Yule-Walker, least squares 
modified Yule-Walker, input/output identification approaches (two 
stage least squares), choosing the best method for your application 

Moving Average spectral estimation - Durbin's method and performance

Capon's Method (MLM) - Filtering interpretation, comparison to 
periodogram, resolution vs. conventional and autoregressive estimators 

Sinusoidal Frequency Estimation - signal and noise subspaces, 
eigenanalysis of covariance matrix, MLE, 
periodogram, principal components/SVD approaches, MUSIC, Pisarenko 
methods, iterative filtering method, statistical performance vs. CRLB 
for all methods 

Empirical Spectral Estimation - Examples using the Modern Interactive 
Spectral Analysis software package, hands-on experience by student 
analysis of mystery data sets, evaluation of student-supplied data 
sets


                        Course Material

Each participant will receive a copy of the text Modern Spectral 
Estimation: Theory and Application by Steven Kay (Prentice-Hall 1988), 
which includes Fortran subroutines for all the basic methods, 
suggested problems and solutions, extensive handouts, including notes 
and copies of all lecture slides, and a copy of the menu-driven 
software package, Modern Interactive Spectral Analysis (MISA), for use 
on a PC as well as an instructional laboratory notebook.   Attendees 
are encouraged to bring their own laptop PCs to perform the lab 
experiments or to analyze their own data sets. 

For more information please call or fax the Applied Technology 
Institute.  PHONE: 410-531-6034       FAX: 410-531-1013





Path: orca!kay
Newsgroups: comp.dsp, sci.nonlinear, sci.stat.math, sci.chaos,
sci.image.processing
Distribution: world
Followup-To: 
From: kay@orca.NoSubdomain.NoDomain (Steve Kay)
Organization: URI Department of Electrical Engineering
Subject: Course Announcement
Keywords: data processing, spectral estimation, high resolution



*****************SHORT COURSE ANNOUNCEMENT*********************

                   MODERN SPECTRAL ANALYSIS

TIME: May 31-June 2, 1995,  8:30-4:00 PM

PLACE:  Holiday Inn
        College Park, Maryland

INSTRUCTOR:  Dr. Steven Kay          Email: kay@ele.uri.edu

TUITION:   $990

                        Course Outline

The Spectral Estimation Problem - Review of random process theory, 
spectral estimation and filtering, applications to sonar, speech, data 
processing, etc., parametric vs. nonparametric approaches, concept of 
resolution, comparison of estimators 

Classical (Fourier Methods) - Periodogram, averaged periodogram, bias-
variance tradeoffs, computation of periodogram, Blackman-Tukey, 
correlation estimation, resolution comparisons 

Time Series Modeling - Time series model definitions -  AR/MA/ARMA, 
Yule-Walker equations, Levinson algorithm, model fitting of 
empirical data, effect of observation noise 

Statistical estimation review - maximum likelihood estimation (MLE), 
Cramer-Rao lower bound 

Autoregressive Spectral Estimation - Autocorrelation, covariance, 
modified covariance (forward/backward), Burg, and recursive maximum 
likelihood methods, properties of estimators, linear 
prediction/maximum entropy/autoregressive modeling relationships, 
reflection coefficients and lattice filters, autocorrelation matching, 
model order selection - Akaike and MDL, observation noise effects, 
performance for sinusoids in noise 

Autoregressive Moving Average Spectral Estimation - Akaike MLE and 
iterative implementations, modified Yule-Walker, least squares 
modified Yule-Walker, input/output identification approaches (two 
stage least squares), choosing the best method for your application 

Moving Average spectral estimation - Durbin's method and performance

Capon's Method (MLM) - Filtering interpretation, comparison to 
periodogram, resolution vs. conventional and autoregressive estimators 

Sinusoidal Frequency Estimation - signal and noise subspaces, 
eigenanalysis of covariance matrix, MLE, 
periodogram, principal components/SVD approaches, MUSIC, Pisarenko 
methods, iterative filtering method, statistical performance vs. CRLB 
for all methods 

Empirical Spectral Estimation - Examples using the Modern Interactive 
Spectral Analysis software package, hands-on experience by student 
analysis of mystery data sets, evaluation of student-supplied data 
sets


                        Course Material

Each participant will receive a copy of the text Modern Spectral 
Estimation: Theory and Application by Steven Kay (Prentice-Hall 1988), 
which includes Fortran subroutines for all the basic methods, 
suggested problems and solutions, extensive handouts, including notes 
and copies of all lecture slides, and a copy of the menu-driven 
software package, Modern Interactive Spectral Analysis (MISA), for use 
on a PC as well as an instructional laboratory notebook.   Attendees 
are encouraged to bring their own laptop PCs to perform the lab 
experiments or to analyze their own data sets. 

For more information please call or fax the Applied Technology 
Institute.  PHONE: 410-531-6034       FAX: 410-531-1013





Path: orca!kay
Newsgroups: comp.dsp, sci.nonlinear, sci.stat.math, sci.chaos,
sci.image.processing
Distribution: world
Followup-To: 
From: kay@orca.NoSubdomain.NoDomain (Steve Kay)
Organization: URI Department of Electrical Engineering
Subject: Course Announcement
Keywords: data processing, spectral estimation, high resolution



*****************SHORT COURSE ANNOUNCEMENT*********************

                   MODERN SPECTRAL ANALYSIS

TIME: May 31-June 2, 1995,  8:30-4:00 PM

PLACE:  Holiday Inn
        College Park, Maryland

INSTRUCTOR:  Dr. Steven Kay          Email: kay@ele.uri.edu

TUITION:   $990

                        Course Outline

The Spectral Estimation Problem - Review of random process theory, 
spectral estimation and filtering, applications to sonar, speech, data 
processing, etc., parametric vs. nonparametric approaches, concept of 
resolution, comparison of estimators 

Classical (Fourier Methods) - Periodogram, averaged periodogram, bias-
variance tradeoffs, computation of periodogram, Blackman-Tukey, 
correlation estimation, resolution comparisons 

Time Series Modeling - Time series model definitions -  AR/MA/ARMA, 
Yule-Walker equations, Levinson algorithm, model fitting of 
empirical data, effect of observation noise 

Statistical estimation review - maximum likelihood estimation (MLE), 
Cramer-Rao lower bound 

Autoregressive Spectral Estimation - Autocorrelation, covariance, 
modified covariance (forward/backward), Burg, and recursive maximum 
likelihood methods, properties of estimators, linear 
prediction/maximum entropy/autoregressive modeling relationships, 
reflection coefficients and lattice filters, autocorrelation matching, 
model order selection - Akaike and MDL, observation noise effects, 
performance for sinusoids in noise 

Autoregressive Moving Average Spectral Estimation - Akaike MLE and 
iterative implementations, modified Yule-Walker, least squares 
modified Yule-Walker, input/output identification approaches (two 
stage least squares), choosing the best method for your application 

Moving Average spectral estimation - Durbin's method and performance

Capon's Method (MLM) - Filtering interpretation, comparison to 
periodogram, resolution vs. conventional and autoregressive estimators 

Sinusoidal Frequency Estimation - signal and noise subspaces, 
eigenanalysis of covariance matrix, MLE, 
periodogram, principal components/SVD approaches, MUSIC, Pisarenko 
methods, iterative filtering method, statistical performance vs. CRLB 
for all methods 

Empirical Spectral Estimation - Examples using the Modern Interactive 
Spectral Analysis software package, hands-on experience by student 
analysis of mystery data sets, evaluation of student-supplied data 
sets


                        Course Material

Each participant will receive a copy of the text Modern Spectral 
Estimation: Theory and Application by Steven Kay (Prentice-Hall 1988), 
which includes Fortran subroutines for all the basic methods, 
suggested problems and solutions, extensive handouts, including notes 
and copies of all lecture slides, and a copy of the menu-driven 
software package, Modern Interactive Spectral Analysis (MISA), for use 
on a PC as well as an instructional laboratory notebook.   Attendees 
are encouraged to bring their own laptop PCs to perform the lab 
experiments or to analyze their own data sets. 

For more information please call or fax the Applied Technology 
Institute.  PHONE: 410-531-6034       FAX: 410-531-1013





Path: orca!kay
Newsgroups: comp.dsp, sci.nonlinear, sci.stat.math, sci.chaos,
sci.image.processing
Distribution: world
Followup-To: 
From: kay@orca.NoSubdomain.NoDomain (Steve Kay)
Organization: URI Department of Electrical Engineering
Subject: Course Announcement
Keywords: data processing, spectral estimation, high resolution



*****************SHORT COURSE ANNOUNCEMENT*********************

                   MODERN SPECTRAL ANALYSIS

TIME: May 31-June 2, 1995,  8:30-4:00 PM

PLACE:  Holiday Inn
        College Park, Maryland

INSTRUCTOR:  Dr. Steven Kay          Email: kay@ele.uri.edu

TUITION:   $990

                        Course Outline

The Spectral Estimation Problem - Review of random process theory, 
spectral estimation and filtering, applications to sonar, speech, data 
processing, etc., parametric vs. nonparametric approaches, concept of 
resolution, comparison of estimators 

Classical (Fourier Methods) - Periodogram, averaged periodogram, bias-
variance tradeoffs, computation of periodogram, Blackman-Tukey, 
correlation estimation, resolution comparisons 

Time Series Modeling - Time series model definitions -  AR/MA/ARMA, 
Yule-Walker equations, Levinson algorithm, model fitting of 
empirical data, effect of observation noise 

Statistical estimation review - maximum likelihood estimation (MLE), 
Cramer-Rao lower bound 

Autoregressive Spectral Estimation - Autocorrelation, covariance, 
modified covariance (forward/backward), Burg, and recursive maximum 
likelihood methods, properties of estimators, linear 
prediction/maximum entropy/autoregressive modeling relationships, 
reflection coefficients and lattice filters, autocorrelation matching, 
model order selection - Akaike and MDL, observation noise effects, 
performance for sinusoids in noise 

Autoregressive Moving Average Spectral Estimation - Akaike MLE and 
iterative implementations, modified Yule-Walker, least squares 
modified Yule-Walker, input/output identification approaches (two 
stage least squares), choosing the best method for your application 

Moving Average spectral estimation - Durbin's method and performance

Capon's Method (MLM) - Filtering interpretation, comparison to 
periodogram, resolution vs. conventional and autoregressive estimators 

Sinusoidal Frequency Estimation - signal and noise subspaces, 
eigenanalysis of covariance matrix, MLE, 
periodogram, principal components/SVD approaches, MUSIC, Pisarenko 
methods, iterative filtering method, statistical performance vs. CRLB 
for all methods 

Empirical Spectral Estimation - Examples using the Modern Interactive 
Spectral Analysis software package, hands-on experience by student 
analysis of mystery data sets, evaluation of student-supplied data 
sets


                        Course Material

Each participant will receive a copy of the text Modern Spectral 
Estimation: Theory and Application by Steven Kay (Prentice-Hall 1988), 
which includes Fortran subroutines for all the basic methods, 
suggested problems and solutions, extensive handouts, including notes 
and copies of all lecture slides, and a copy of the menu-driven 
software package, Modern Interactive Spectral Analysis (MISA), for use 
on a PC as well as an instructional laboratory notebook.   Attendees 
are encouraged to bring their own laptop PCs to perform the lab 
experiments or to analyze their own data sets. 

For more information please call or fax the Applied Technology 
Institute.  PHONE: 410-531-6034       FAX: 410-531-1013





Path: orca!kay
Newsgroups: comp.dsp, sci.nonlinear, sci.stat.math, sci.chaos,
sci.image.processing
Distribution: world
Followup-To: 
From: kay@orca.NoSubdomain.NoDomain (Steve Kay)
Organization: URI Department of Electrical Engineering
Subject: Course Announcement
Keywords: data processing, spectral estimation, high resolution



*****************SHORT COURSE ANNOUNCEMENT*********************

                   MODERN SPECTRAL ANALYSIS

TIME: May 31-June 2, 1995,  8:30-4:00 PM

PLACE:  Holiday Inn
        College Park, Maryland

INSTRUCTOR:  Dr. Steven Kay          Email: kay@ele.uri.edu

TUITION:   $990

                        Course Outline

The Spectral Estimation Problem - Review of random process theory, 
spectral estimation and filtering, applications to sonar, speech, data 
processing, etc., parametric vs. nonparametric approaches, concept of 
resolution, comparison of estimators 

Classical (Fourier Methods) - Periodogram, averaged periodogram, bias-
variance tradeoffs, computation of periodogram, Blackman-Tukey, 
correlation estimation, resolution comparisons 

Time Series Modeling - Time series model definitions -  AR/MA/ARMA, 
Yule-Walker equations, Levinson algorithm, model fitting of 
empirical data, effect of observation noise 

Statistical estimation review - maximum likelihood estimation (MLE), 
Cramer-Rao lower bound 

Autoregressive Spectral Estimation - Autocorrelation, covariance, 
modified covariance (forward/backward), Burg, and recursive maximum 
likelihood methods, properties of estimators, linear 
prediction/maximum entropy/autoregressive modeling relationships, 
reflection coefficients and lattice filters, autocorrelation matching, 
model order selection - Akaike and MDL, observation noise effects, 
performance for sinusoids in noise 

Autoregressive Moving Average Spectral Estimation - Akaike MLE and 
iterative implementations, modified Yule-Walker, least squares 
modified Yule-Walker, input/output identification approaches (two 
stage least squares), choosing the best method for your application 

Moving Average spectral estimation - Durbin's method and performance

Capon's Method (MLM) - Filtering interpretation, comparison to 
periodogram, resolution vs. conventional and autoregressive estimators 

Sinusoidal Frequency Estimation - signal and noise subspaces, 
eigenanalysis of covariance matrix, MLE, 
periodogram, principal components/SVD approaches, MUSIC, Pisarenko 
methods, iterative filtering method, statistical performance vs. CRLB 
for all methods 

Empirical Spectral Estimation - Examples using the Modern Interactive 
Spectral Analysis software package, hands-on experience by student 
analysis of mystery data sets, evaluation of student-supplied data 
sets


                        Course Material

Each participant will receive a copy of the text Modern Spectral 
Estimation: Theory and Application by Steven Kay (Prentice-Hall 1988), 
which includes Fortran subroutines for all the basic methods, 
suggested problems and solutions, extensive handouts, including notes 
and copies of all lecture slides, and a copy of the menu-driven 
software package, Modern Interactive Spectral Analysis (MISA), for use 
on a PC as well as an instructional laboratory notebook.   Attendees 
are encouraged to bring their own laptop PCs to perform the lab 
experiments or to analyze their own data sets. 

For more information please call or fax the Applied Technology 
Institute.  PHONE: 410-531-6034       FAX: 410-531-1013





Newsgroups: comp.dsp,sci.nonlinear,sci.stat.math,sci.chaos,
Sender: kay@orca (Steve Kay)
From: kay@orca.NoSubdomain.NoDomain (Steve Kay)
Path: orca!kay
sci.image.processing
Distribution: world
Followup-To: 
Organization: URI Department of Electrical Engineering
Subject: Course Announcement
Keywords: data processing, spectral estimation, high resolution



*****************SHORT COURSE ANNOUNCEMENT*********************

                   MODERN SPECTRAL ANALYSIS

TIME: May 31-June 2, 1995,  8:30-4:00 PM

PLACE:  Holiday Inn
        College Park, Maryland

INSTRUCTOR:  Dr. Steven Kay          Email: kay@ele.uri.edu

TUITION:   $990

                        Course Outline

The Spectral Estimation Problem - Review of random process theory, 
spectral estimation and filtering, applications to sonar, speech, data 
processing, etc., parametric vs. nonparametric approaches, concept of 
resolution, comparison of estimators 

Classical (Fourier Methods) - Periodogram, averaged periodogram, bias-
variance tradeoffs, computation of periodogram, Blackman-Tukey, 
correlation estimation, resolution comparisons 

Time Series Modeling - Time series model definitions -  AR/MA/ARMA, 
Yule-Walker equations, Levinson algorithm, model fitting of 
empirical data, effect of observation noise 

Statistical estimation review - maximum likelihood estimation (MLE), 
Cramer-Rao lower bound 

Autoregressive Spectral Estimation - Autocorrelation, covariance, 
modified covariance (forward/backward), Burg, and recursive maximum 
likelihood methods, properties of estimators, linear 
prediction/maximum entropy/autoregressive modeling relationships, 
reflection coefficients and lattice filters, autocorrelation matching, 
model order selection - Akaike and MDL, observation noise effects, 
performance for sinusoids in noise 

Autoregressive Moving Average Spectral Estimation - Akaike MLE and 
iterative implementations, modified Yule-Walker, least squares 
modified Yule-Walker, input/output identification approaches (two 
stage least squares), choosing the best method for your application 

Moving Average spectral estimation - Durbin's method and performance

Capon's Method (MLM) - Filtering interpretation, comparison to 
periodogram, resolution vs. conventional and autoregressive estimators 

Sinusoidal Frequency Estimation - signal and noise subspaces, 
eigenanalysis of covariance matrix, MLE, 
periodogram, principal components/SVD approaches, MUSIC, Pisarenko 
methods, iterative filtering method, statistical performance vs. CRLB 
for all methods 

Empirical Spectral Estimation - Examples using the Modern Interactive 
Spectral Analysis software package, hands-on experience by student 
analysis of mystery data sets, evaluation of student-supplied data 
sets


                        Course Material

Each participant will receive a copy of the text Modern Spectral 
Estimation: Theory and Application by Steven Kay (Prentice-Hall 1988), 
which includes Fortran subroutines for all the basic methods, 
suggested problems and solutions, extensive handouts, including notes 
and copies of all lecture slides, and a copy of the menu-driven 
software package, Modern Interactive Spectral Analysis (MISA), for use 
on a PC as well as an instructional laboratory notebook.   Attendees 
are encouraged to bring their own laptop PCs to perform the lab 
experiments or to analyze their own data sets. 

For more information please call or fax the Applied Technology 
Institute.  PHONE: 410-531-6034       FAX: 410-531-1013






