A unique treatment of signal processing using a model-basedperspectiveSignal processing is primarily aimed at extracting usefulinformation, while rejecting the extraneous from noisy data. Ifsignal levels are high, then basic techniques can be applied.However, low signal levels require using the underlying physics tocorrect the problem causing these low levels and extracting thedesired information. Model-based signal processing incorporates thephysical phenomena, measurements, and noise in the form ofmathematical models to solve this problem. Not only does theapproach enable signal processors to work directly in terms of theproblem's physics, instrumentation, and uncertainties, but itprovides far superior performance over the standard techniques.Model-based signal processing is both a modeler's as well as asignal processor's tool.Model-Based Signal Processing develops the model-based approach ina unified manner and follows it through the text in the algorithms,examples, applications, and case studies. The approach, coupledwith the hierarchy of physics-based models that the authordevelops, including linear as well as nonlinear representations,makes it a unique contribution to the field of signalprocessing.The text includes parametric (e.g., autoregressive or all-pole),sinusoidal, wave-based, and state-space models as some of the modelsets with its focus on how they may be used to solve signalprocessing problems. Special features are provided that assistreaders in understanding the material and learning how to applytheir new knowledge to solving real-life problems.* Unified treatment of well-known signal processing modelsincluding physics-based model sets* Simple applications demonstrate how the model-based approachworks, while detailed case studies demonstrate problem solutions intheir entirety from concept to model development, throughsimulation, application to real data, and detailed performanceanalysis* Summaries provided with each chapter ensure that readersunderstand the key points needed to move forward in the text aswell as MATLAB(r) Notes that describe the key commands andtoolboxes readily available to perform the algorithmsdiscussed* References lead to more in-depth coverage of specializedtopics* Problem sets test readers' knowledge and help them put their newskills into practiceThe author demonstrates how the basic idea of model-based signalprocessing is a highly effective and natural way to solve bothbasic as well as complex processing problems. Designed as agraduate-level text, this book is also essential reading forpracticing signal-processing professionals and scientists, who willfind the variety of case studies to be invaluable. An Instructor's Manual presenting detailed solutions to all theproblems in the book is available from the Wiley editorialdepartment
