9781848214590

Blind Identification and Separation of Complex-valued Signals

by ;
  • ISBN13:

    9781848214590

  • ISBN10:

    1848214596

  • Format: Hardcover
  • Copyright: 2013-10-07
  • Publisher: Iste/Hermes Science Pub

Note: Supplemental materials are not guaranteed with Rental or Used book purchases.

Purchase Benefits

  • Free Shipping On Orders Over $35!
    Your order must be $35 or more to qualify for free economy shipping. Bulk sales, PO's, Marketplace items, eBooks and apparel do not qualify for this offer.
  • Get Rewarded for Ordering Your Textbooks! Enroll Now
  • Write a Review
List Price: $59.00 Save up to $5.90
  • Rent Book $53.10
    Add to Cart Free Shipping

    TERM
    PRICE
    DUE
    USUALLY SHIPS IN 3-4 BUSINESS DAYS

Supplemental Materials

What is included with this book?

  • The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.
  • The Rental copy of this book is not guaranteed to include any supplemental materials. Typically, only the book itself is included. This is true even if the title states it includes any access cards, study guides, lab manuals, CDs, etc.

Summary

We consider the blind estimation of a multiple input/multiple output (MIMO) system that mixes a number of underlying signals of interest called sources. We also consider the case of direct estimation of the inverse system for the purpose of source separation. We will describe the estimation theory associated with the identifiability conditions and dedicated algebraic algorithms. The algorithms depend critically on (statistical and/or time-frequency) properties of complex sources that will be described precisely.

Table of Contents

1. Introduction:  A brief history, the importance of the problem, non-circular signals, applications

2. Model and useful statistics: Mixing model, assumptions about mixing matrix and sources, statistics matrices and non-circular statistics matrices, Maximum Likelihood framework, Identifiability conditions, CRLB.

3. Normalization: Whitening, the case with additive noise, the case of over-determined systems, advantages/drawbacks

4. Direct estimation; Algebraic tools, exact diagonalization of two matrices, identifiability issues, some illustrations by computer simulations

5. Unitary recursive estimation: Unitary joint diagonalization of a set of Hermitian matrices, unitary joint diagonalization of a set of complex symmetric matrices, illustrations by computer simulations

6. General recursjve estimation: General Joint diagonalization of a set of Hermitian matrices, General joint diagonalization of a set of complex symmetric matrices, some illustrations by computer simulations

7. Adaptive complex estimation:  ML and maximization of negentropy

8. Conclusion

Rewards Program

Write a Review