Nonlinear Digital Filtering with Python : An Introduction

This book discusses important structural filter classes including the median filter and a number of its extensions (e.g., weighted and recursive median filters), and Volterra filters based on polynomial nonlinearities. Using results from algebra and the theory of functional equations to construct and characterize behaviorally defined nonlinear filter classes, the text first introduces Python programming, and then proposes practical, bottom-up strategies for designing more complex and capable filters from simpler components in a way that preserves the key properties of these components.

discusses important structural filter classes including the median filter and a number of its extensions (e.g., weighted and recursive median filters), and Volterra filters based on polynomial nonlinearities. Adopting both structural and behavioral approaches in characterizing and designing nonlinear digital filters, this book:

• Begins with an expedient introduction to programming in the free, open-source computing environment of Python
• Uses results from algebra and the theory of functional equations to construct and characterize behaviorally defined nonlinear filter classes
• Analyzes the impact of a range of useful interconnection strategies on filter behavior, providing Python implementations of the presented filters and interconnection strategies
• Proposes practical, bottom-up strategies for designing more complex and capable filters from simpler components in a way that preserves the key properties of these components
• Illustrates the behavioral consequences of allowing recursive (i.e., feedback) interconnections in nonlinear digital filters while highlighting a challenging but promising research frontier

Nonlinear Digital Filtering with Python: An Introduction supplies essential knowledge useful for developing and implementing data cleaning filters for dynamic data analysis and time-series modeling.