Modeling, Estimation and Optimal Filtration in Signal Processing
Modeling, Estimation and Optimal Filtration in Signal Processing

Author: Mohamed Najim

Publisher: John Wiley & Sons

Published: 2010-01-05

Total Pages: 410

ISBN-13: 0470393688

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The purpose of this book is to provide graduate students and practitioners with traditional methods and more recent results for model-based approaches in signal processing. Firstly, discrete-time linear models such as AR, MA and ARMA models, their properties and their limitations are introduced. In addition, sinusoidal models are addressed. Secondly, estimation approaches based on least squares methods and instrumental variable techniques are presented. Finally, the book deals with optimal filters, i.e. Wiener and Kalman filtering, and adaptive filters such as the RLS, the LMS and their variants.

Optimal Filtering
Optimal Filtering

Author: Brian D. O. Anderson

Publisher: Courier Corporation

Published: 2012-05-23

Total Pages: 370

ISBN-13: 0486136892

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Graduate-level text extends studies of signal processing, particularly regarding communication systems and digital filtering theory. Topics include filtering, linear systems, and estimation; discrete-time Kalman filter; time-invariant filters; more. 1979 edition.

Modeling, Estimation and Optimal Filtering in Signal Processing
Modeling, Estimation and Optimal Filtering in Signal Processing

Author: Mohamed Najim

Publisher:

Published: 2008

Total Pages: 424

ISBN-13:

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The purpose of this book is to provide graduate students and practitioners with traditional methods and more recent results for model-based approaches in signal processing. Firstly, discrete-time linear models such as AR, MA and ARMA models, their properties and their limitations are introduced. In addition, sinusoidal models are addressed. Secondly, estimation approaches based on least squares methods and instrumental variable techniques are presented. Finally, the book deals with optimal filters, i.e. Wiener and Kalman filtering, and adaptive filters such as the RLS, the LMS and their variants.

Statistical Digital Signal Processing and Modeling
Statistical Digital Signal Processing and Modeling

Author: Monson H. Hayes

Publisher: John Wiley & Sons

Published: 1996-04-19

Total Pages: 629

ISBN-13: 0471594318

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The main thrust is to provide students with a solid understanding of a number of important and related advanced topics in digital signal processing such as Wiener filters, power spectrum estimation, signal modeling and adaptive filtering. Scores of worked examples illustrate fine points, compare techniques and algorithms and facilitate comprehension of fundamental concepts. Also features an abundance of interesting and challenging problems at the end of every chapter.

Digital Signal Processing (DSP) with Python Programming
Digital Signal Processing (DSP) with Python Programming

Author: Maurice Charbit

Publisher: John Wiley & Sons

Published: 2017-02-13

Total Pages: 309

ISBN-13: 1786301261

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The parameter estimation and hypothesis testing are the basic tools in statistical inference. These techniques occur in many applications of data processing., and methods of Monte Carlo have become an essential tool to assess performance. For pedagogical purposes the book includes several computational problems and exercices. To prevent students from getting stuck on exercises, detailed corrections are provided.

Architecture-Aware Optimization Strategies in Real-time Image Processing
Architecture-Aware Optimization Strategies in Real-time Image Processing

Author: Chao Li

Publisher: John Wiley & Sons

Published: 2017-10-30

Total Pages: 120

ISBN-13: 1119467144

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In the field of image processing, many applications require real-time execution, particularly those in the domains of medicine, robotics and transmission, to name but a few. Recent technological developments have allowed for the integration of more complex algorithms with large data volume into embedded systems, in turn producing a series of new sophisticated electronic architectures at affordable prices. This book performs an in-depth survey on this topic. It is primarily written for those who are familiar with the basics of image processing and want to implement the target processing design using different electronic platforms for computing acceleration. The authors present techniques and approaches, step by step, through illustrative examples. This book is also suitable for electronics/embedded systems engineers who want to consider image processing applications as sufficient imaging algorithm details are given to facilitate their understanding.

Bayesian Filtering and Smoothing
Bayesian Filtering and Smoothing

Author: Simo Särkkä

Publisher: Cambridge University Press

Published: 2013-09-05

Total Pages: 255

ISBN-13: 110703065X

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A unified Bayesian treatment of the state-of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state space models.

Tracking with Particle Filter for High-dimensional Observation and State Spaces
Tracking with Particle Filter for High-dimensional Observation and State Spaces

Author: Séverine Dubuisson

Publisher: John Wiley & Sons

Published: 2015-01-05

Total Pages: 222

ISBN-13: 1119054052

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This title concerns the use of a particle filter framework to track objects defined in high-dimensional state-spaces using high-dimensional observation spaces. Current tracking applications require us to consider complex models for objects (articulated objects, multiple objects, multiple fragments, etc.) as well as multiple kinds of information (multiple cameras, multiple modalities, etc.). This book presents some recent research that considers the main bottleneck of particle filtering frameworks (high dimensional state spaces) for tracking in such difficult conditions.

Matrix and Tensor Decompositions in Signal Processing, Volume 2
Matrix and Tensor Decompositions in Signal Processing, Volume 2

Author: Gérard Favier

Publisher: John Wiley & Sons

Published: 2021-08-31

Total Pages: 386

ISBN-13: 1786301555

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The second volume will deal with a presentation of the main matrix and tensor decompositions and their properties of uniqueness, as well as very useful tensor networks for the analysis of massive data. Parametric estimation algorithms will be presented for the identification of the main tensor decompositions. After a brief historical review of the compressed sampling methods, an overview of the main methods of retrieving matrices and tensors with missing data will be performed under the low rank hypothesis. Illustrative examples will be provided.