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Separate signals from noise with this valuable introduction to signal processing by applied decomposition The decomposition of complex signals into their sub-signals or individual components is a crucial tool in signal processing. It allows each component of a signal to be analyzed individually and enables the signal to be isolated from noise and processed in full. Decomposition processes have not always been widely adopted due to the difficult underlying mathematics and complex applications. This text simplifies these obstacles. Signal Processing: An Applied Decomposition Approach demystifies these tools with a model-based perspective. Addressing each major decomposition approach in turn, it offers a mathematically-informed step-by-step analysis of the process by which it breaks a composite signal / system down into its constituent parts. Introducing both fundamental concepts and advanced applications, it is an indispensable addition to any library regarding signal processing. Signal Processing readers will find: Signal decomposition techniques developed from the data-based, spectral-based and model-based perspectives incorporate: statistical approaches (PCA, ICA, Singular Spectrum); spectral approaches (MTM, PHD, MUSIC); and model-based approaches (EXP, LATTICE, SSP)In depth discussion of topics includes signal / system estimation and decomposition, time domain and frequency domain techniques, systems theory, modal decompositions, applications and many moreNumerous figures, examples, and tables illustrating key concepts and algorithms are developed throughout the textIncludes problem sets, case studies, real-world applications as well as MATLAB notes highlighting applicable commands Signal Processing is ideal for engineering and scientific professionals, as well as graduate students seeking a focused text on signal / system decomposition with performance metrics and real-world applications.