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2 edition of Statistical signal processing using a class of iterativeestimation algorithms found in the catalog.

Statistical signal processing using a class of iterativeestimation algorithms

M. Feder

Statistical signal processing using a class of iterativeestimation algorithms

by M. Feder

  • 60 Want to read
  • 40 Currently reading

Published .
Written in English


Edition Notes

Thesis(D.Sc) - Massachusetts Institute of Technology, 1987.

StatementM. Feder.
SeriesAD-A189 050
ID Numbers
Open LibraryOL19299898M

The Complete, Modern Guide to Developing Well-Performing Signal Processing Algorithms. In Fundamentals of Statistical Signal Processing, Volume III: Practical Algorithm Development, author Steven M. Kay shows how to convert theories of statistical signal processing estimation and detection into software algorithms that can be implemented on digital computers.5/5(2). There is computational statistics and there is statistical then there is statistical algorithmic. Not the same thing, by far. This book by Weihs, Mersman and Ligges, from TU Dortmund, the later being also a member of the R Core team, stands at one end of this wide spectrum of techniques required by modern statistical analysis. In short, it provides the necessary skills to.

Statistical Signal Processing using a class of Iterative Estimation Algorithms Meir Feder , Tel-Aviv University () , Tel-Aviv University () Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Science at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution September. Mathematical Methods and Algorithms for Signal Processing tackles the challenge of providing readers and practitioners with the broad tools of mathematics employed in modern signal processing. Building from an assumed background in signals and stochastic processes, the book provides a solid foundation in analysis, linear algebra, optimization, and statistical signal processing/5(15).

Multipath time-delay estimation via the EM algorithm. We consider the application of the EM algorithm to the multipath time delay estimation problem. Statistical signal processing using a. According to our current on-line database, Meir Feder has 14 students and 20 descendants. We welcome any additional information. If you have additional information or corrections regarding this mathematician, please use the update submit students of this mathematician, please use the new data form, noting this mathematician's MGP ID of for the advisor ID.


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Statistical signal processing using a class of iterativeestimation algorithms by M. Feder Download PDF EPUB FB2

These algorithms are potentially useful in a variety of application areas including digital transmission systems incorporating power amplifier(s) along with multiple antennas, cognitive processing Author: Meir Feder.

Algorithms for Statistical Signal Processing: Proakis, John G., Rader, Charles M., Ling, Fuyun, Moonen, Marc, Proudler, Ian K., Nikias, Chrysostomos L.: : by: Statistical Signal Processing using a class of Iterative Estimation Algorithms by Meir Feder Submitted in partial fulfillment of the requirements for the degree of Doctor of Science at the Massachusetts Institute of Technology.

Aug Abstract. However, in many practical situations, the original signal processing problem may generate a complicated optimization problem, e.g., when the observed signals are noisy and incomplete. A framework of iterative procedures for maximizing the likelihood, the Estimate Maximize (EM) algorithm, is widely used in statistics.

Keeping pace with the expanding, ever more complex applications of DSP, this authoritative presentation of computational algorithms for statistical signal processing focuses on advanced topics ignored by other books on the subject. Algorithms for Convolution and DFT.

Linear Prediction and Optimum Linear Filters. Least-Squares Methods for System Modeling and Filter Design. STATISTICAL METHODS FOR SIGNAL PROCESSING Alfred O. Hero Aug This set of notes is the primary source material for the course EECS “Estimation, filtering and detection” used over the period at the University of Michigan Ann Arbor.

The author can be reached at Dept. EECS, University of Michigan, Ann Arbor, MI Statistical signal processing algorithms work to extract the good despite the “efforts” of the bad. This course covers the two basic approaches to statistical signal processing: estimation and detection.

of Statistical Signal Processing: Detection Theory", S. Kay. The function subprograms Q.m and Qinv.m are required. Fig77new - computes Figure in "Fundamentals of Statistical Signal Processing: Detection Theory", S.

Kay. gendata - generates a complex or real AR, MA, or ARMA time series given the filter parameters and. Statistical Signal Processing.

The author points out that the text title is not unique, in fact A Second Course in Discrete-Time Signal Processing is also appropriate The Hayes text covers: – Review of discrete-time signal processing and matrix the-ory for statistical signal processing – Discrete-time random processes – Signal modeling.

Concepts of signal processing using random signals; random vectors, random processes, signal modeling, Levinson recursion, Wiener filtering, spectrum estimation, and detection theory. Prerequisite: ECE / or equivalent and ECE or. Coverage is equally divided between the theory and philosophy of statistical signal processing, and the algorithms that are used to solve related problems.

The text reflects the author's philosophy that a deep understanding of signal processing is accomplished best through working by: Statistical signal processing algorithms work to extract the good despite the “efforts” of the bad This course covers the two basic approaches to statistical signal processing: estimation and.

For Senior/Graduate Level Signal Processing courses. The book is also suitable for a course in advanced signal processing, or for self-study.

Mathematical Methods and Algorithms for Signal Processing tackles the challenge of providing students and practitioners with the broad tools of mathematics employed in modern signal processing. Fundamentals of Statistical Signal Processing: Practical Algorithm Development is the third volume in a series of textbooks by the same name.

Previous volumes described the underlying theory of estimation and detection algorithms. In con-trast, the current volume addresses the practice of. Keeping pace with the expanding, ever more complex applications of DSP, this authoritative presentation of computational algorithms for statistical signal processing focuses on advanced topics ignored by other books on the subject.

Algorithms for Convolution and DFT. Linear Prediction and. Statistical signal processing using a class of iterative estimation algorithms By Download PDF (8 MB).

IEEE Signal Processing Society has an MLSP committee IEEE Workshop on Machine Learning for Signal Processing Held this year in Santander, Spain. Several special interest groups IEEE: multimedia and audio processing, machine learning and speech processing ACM ISCA Books In work: MLSP, P.

Smaragdisand B. Raj Courses ( was one of the first). Mathematics of Signal Processing: A First Course Charles L. Byrne Department of Mathematical Sciences University of Massachusetts Lowell Lowell, MA IEEE Trans. Signal Processing, July (with S.

Saha) (PDF Format KB) "Sufficiency, classification, and the class-specific feature theorem'', IEEE Trans. on Information Theory, July (PDF Format KB) "Chirp Estimation using Importance Sampling'', Int. Conf. Acoustics,Speech, and Signal Processing, Salt Lake City, (with S.

Saha). The most comprehensive overview of signal detection available. This is a thorough, up-to-date introduction to optimizing detection algorithms for implementation on digital computers.

It focuses extensively on real-world signal processing applications, including state-of-the-art speech and communications technology as well as traditional sonar/radar systems. Fundamentals of Statistical Signal Processing (https: it is an amazing book, if u r doing statistical processing u can't achieve anything without it.

SRIKANTH KADIYALA. detection algorithms digital computers signal processing statistical signa Cancel. Discover Live s: 5.Statistical Signal Processing involves processing these signals and forms the backbone of modern communication and signal processing course will the three broad components of statistical signal processing: random signal modelling, estimation theory and detection theory.Digital Signal Processing (see Reserve Book #2) Fourier Transform for Discrete-Time Signals ; Discrete-Time Filters (Mostly FIR - not design, but operation via convolution) Textbook.

Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory by Steven Kay (Published by Prentice Hall) Other Books of Interest.