β Scribed by Jerry M. Mendel (auth.)
No coin nor oath required. For personal study only.
Convolution is the most important operation that describes the behavior of a linear time-invariant dynamical system. Deconvolution is the unraveling of convolution. It is the inverse problem of generating the system's input from knowledge about the system's output and dynamics. Deconvolution requires a careful balancing of bandwidth and signal-to-noise ratio effects. Maximum-likelihood deconvolution (MLD) is a design procedure that handles both effects. It draws upon ideas from Maximum Likelihood, when unknown parameters are random. It leads to linear and nonlinear signal processors that provide high-resolution estimates of a system's input. All aspects of MLD are described, from first principles in this book. The purpose of this volume is to explain MLD as simply as possible. To do this, the entire theory of MLD is presented in terms of a convolutional signal generating model and some relatively simple ideas from optimization theory. Earlier approaches to MLD, which are couched in the language of state-variable models and estimation theory, are unnecessary to understand the essence of MLD. MLD is a model-based signal processing procedure, because it is based on a signal model, namely the convolutional model. The book focuses on three aspects of MLD: (1) specification of a probability model for the system's measured output; (2) determination of an appropriate likelihood function; and (3) maximization of that likelihood function. Many practical algorithms are obtained. Computational aspects of MLD are described in great detail. Extensive simulations are provided, including real data applications.
Front Matter....Pages i-xiv
Introduction....Pages 1-6
Convolutional Model....Pages 7-23
Likelihood....Pages 25-30
Maximizing Likelihood....Pages 31-59
Properties and Performance....Pages 61-76
Examples....Pages 77-125
Mathematical Details for Chapter 4....Pages 127-171
Mathematical Details for Chapter 5....Pages 173-185
Computational Considerations....Pages 187-208
Back Matter....Pages 209-227
Communications Engineering, Networks
π SIMILAR VOLUMES
Π‘ΡΠ°ΡΡΡ ΠΎΠΏΡΠ±Π»ΠΈΠΊΠΎΠ²Π°Π½Π° Π² Journal of the Acoustical Society of America - 1973 - vol. 54 - β 2, Ρ. 499-503.<div class="bb-sep"></div>Introduction<br/>Sturm-Liouville problem<br/>Estimating the source weights<br/>Estimating the source depth<br/>A simple example<br/>Conclusion
A unique treatment of signal processing using a model-based perspective Signal processing is primarily aimed at extracting useful information, while rejecting the extraneous from noisy data. If signal levels are high, then basic techniques can be applied. However, low signal levels require
A unique treatment of signal processing using a model-based perspective<br /> <br /> Signal processing is primarily aimed at extracting useful information, while rejecting the extraneous from noisy data. If signal levels are high, then basic techniques can be applied. However, low signal levels requ
Model-Based Signal Processing develops the "model-based approach" to signal processing for a variety of useful model sets including the popularly termed "physics-based" models. It presents a unique viewpoint of signal processing from the model-based perspective. A wide variety of case studies are in