1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.
10 years of news and resources for members of the IEEE Signal Processing Society
Edited by Garry A. Einicke, InTech, 2012
Description from the publisher: This book describes the classical smoothing, filtering and prediction techniques together with some more recently developed embellishments for improving performance within applications. It aims to present the subject in an accessible way, so that it can serve as a practical guide for undergraduates and newcomers to the field. The material is organized as a ten-lecture course. The foundations are laid in Chapters 1 and 2, which explain minimum-mean-square-error solution construction and asymptotic behavior. Chapters 3 and 4 introduce continuous-time and discrete-time minimum-variance filtering. Generalizations for missing data, deterministic inputs, correlated noises, direct feedthrough terms, output estimation and equalization are described. Chapter 5 simplifies the minimum-variance filtering results for steady-state problems. Observability, Riccati equation solution convergence, asymptotic stability and Wiener filter equivalence are discussed. Chapters 6 and 7 cover the subject of continuous-time and discrete-time smoothing. The main fixed-lag, fixed-point and fixed-interval smoother results are derived. It is shown that the minimum-variance fixed-interval smoother attains the best performance. Chapter 8 attends to parameter estimation. As the above-mentioned approaches all rely on knowledge of the underlying model parameters, maximum-likelihood techniques within expectation-maximisation algorithms for joint state and parameter estimation are described. Chapter 9 is concerned with robust techniques that accommodate uncertainties within problem specifications. An extra term within Riccati equations enables designers to trade-off average error and peak error performance. Chapter 10 rounds off the course by applying the afore-mentioned linear techniques to nonlinear estimation problems. It is demonstrated that step-wise linearization can be used within predictors, filters and smoothers, albeit by forsaking optimal performance guarantees.
Please visit the book’s website for more information.
|Call for Nominations: IEEE Technical Field Awards||15 January 2022|
|Call for Officer Nominations: Vice President-Education and Vice President-Membership||21 February 2022|
|Nominate an IEEE Fellow today!||1 March 2022|
|Call for Nominations for Editor-in-Chief, IEEE Signal Processing Letters||4 April 2022|
|Call for Nominations: IEEE Medals & Recognitions||15 June 2022|
© Copyright 2022 IEEE – All rights reserved. Use of this website signifies your agreement to the IEEE Terms and Conditions.
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.