Signal processing is the discipline of extracting information from
collections of measurements. To be effective, the measurements must be
organized and then filtered, detected, or transformed to expose the
desired information. Distortions caused by uncertainty, noise, and
clutter degrade the performance of practical signal processing systems.
In aggressively uncertain situations, the full truth about an underlying
signal cannot be known. This book develops the theory and practice of
signal processing systems for these situations that extract useful,
qualitative information using the mathematics of topology -- the study
of spaces under continuous transformations. Since the collection of
continuous transformations is large and varied, tools which are
topologically-motivated are automatically insensitive to substantial
distortion. The target audience comprises practitioners as well as
researchers, but the book may also be beneficial for graduate students.