[1][3] Gardner's approach in this book is considered to be in keeping with the work of Norbert Wiener in his classic treatise Generalized Harmonic Analysis first published in 1930.

In 1982, while at University of California, Gardner founded the R&D firm Statistical Signal Processing, Inc. (SSPI), an engineering research services company working primarily with the national security sector but also the cellular RF communications industry.

The book covers all key aspects of second order statistics of random processes using many examples and without the theoretical trappings of other introductory texts on this subject.

"[15] James Massey wrote "I admire the scholarship of this book and its radical departure from the stochastic process bandwagon of the past 40 years.

He further gave the original definition and mathematical characterization of non-stochastic fraction-of-time (FOT) probabilistic models of CS, ACS, and poly-CS time-series.

This introduction reveals the central role played by Professor Gardner's just-published time-average theory in understanding the relationship between Einstein's and Norbert Wiener's (1930) contributions to statistical spectral analysis.

Together with his doctoral student Chi Kang Chen, he wrote the book of mathematical problem solving, The Random Processes Tutor: A Comprehensive Solutions Manual for Independent Study in 1989.

[18] Gardner, with the assistance of his doctoral student Chad Spooner, also generalized his theory from second-order to higher-order cyclostationarity in the early 1990s, and provided new insight into the statistical quantity called the cumulant.

[19] Later, he worked on cyclostationarity exploitation in the areas of enhanced radio reception for wireless communications and, more extensively, advanced RF signals intelligence.

Douglas Cochran wrote "this book is a timely contribution that should be a valuable reference for academic and industrial R&D engineers in signal processing and communication systems.

"[7] This book was an outgrowth of the first international Workshop on Cyclostationary Signals in 1992, which was funded jointly by the National Science Foundation and the Offices of Research of the US Army, Navy, and Air Force.

Gardner in 2016 developed the ad hoc concept of time de-warping into the basic theory of converting irregular cyclostationarity into regular cyclostationarity as a means for rendering the extensive and powerful tools of cyclostationary signal processing technology applicable to natural data exhibiting irregular cyclicity, which pervades essentially all fields of science as well as engineering.