Adaptive filtering is a classical branch of digital signal processing (DSP). Industrial interest in adaptive filtering grows continuously with the increase in computer performance that allows ever more conplex algorithms to be run in real-time. Change detection is a type of adaptive filtering for non-stationary signals and is also the basic tool in fault detection and diagnosis.

Often considered as separate subjects Adaptive Filtering and Change Detection bridges a gap in the literature with a unified treatment of these areas, emphasizing that change detection is a natural extension of adaptive filters, and adaptive filters are the basic building blocks in all change detectors.
By providing a timely and innovative resource, applied engineers, researchers and postgraduate students studying DSP will all find this an indispensable and enlightening read!

* Presents a comprehensive and complete treatment of adapative filtering

* Provides a unifying framework for the theory of change detection based on a filtering approach

* Combines mathematical tools (algebra, calculus, statistics) and applications areas (airborne, automotive, communication systems, standard signal processing and automatic control applications)

* Features extensive examples and case studies which can be reproduced and investigated in an accompanying Matlab demo toolbox.