This text provides a thorough explanation of the underlying principles of spectral analysis and the full range of estimation techniques used in engineering. The applications of these techniques are demonstrated in numerous case studies, illustrating the approach required and the compromises to be made when solving real engineering problems. The principles outlined in these case studies are applicable over the full range of engineering disciplines and all the reader requires is an understanding of elementary calculus and basic statistics. The realistic approach and comprehensive nature of this text will provide undergraduate engineers and physicists of all disciplines with an invaluable introduction to the subject and the detailed case studies will interest the experienced professional. No more than a knowledge of elementary calculus, and basic statistics and probability is needed Accessible to undergraduates at any stage of their courses Easy and clear to follow

This book is an up-to-date introduction to univariate spectral analysis at the graduate level, which reflects a new scientific awareness of spectral complexity, as well as the widespread use of spectral analysis on digital computers with considerable computational power. The text provides theoretical and computational guidance on the available techniques, emphasizing those that work in practice. Spectral analysis finds extensive application in the analysis of data arising in many of the physical sciences, ranging from electrical engineering and physics to geophysics and oceanography. A valuable feature of the text is that many examples are given showing the application of spectral analysis to real data sets. Special emphasis is placed on the multitaper technique, because of its practical success in handling spectra with intricate structure, and its power to handle data with or without spectral lines. The text contains a large number of exercises, together with an extensive bibliography.

Spectral analysis is a data analysis tool that lends itself well to examining signals that contain regular, repeating patterns. This book outlines the theory behind, and examples for, a range of spectral analysis techniques including: auto spectral analysis, cross spectral analysis, rotary spectra, harmonic analysis, the short time Fourier transform and the Morlet wavelet. It is a commonly used tool in physical oceanography, but also has applications in other sciences. This book is intended as a resource tool for those starting out their journey into spectral analysis. Not every aspect of spectral analysis is covered. Many examples are included that demonstrate the technique being considered. The focus here is on time series analysis, although there are some examples that use data from a spatial array of instruments. Fourier theory underpins much of spectral analysis and it is from there that the theoretical development commences. Hence, there is a considerable amount of mathematics for those that are inclined to understand the origin of the different techniques and how they are developed. Much of the early sections in this book discuss the work that needs to be undertaken and questions that need to be asked before implementing spectral analysis. Some of this preliminary work involves designing data collection experiments, patching data gaps, filtering the data (removing trends and known signals), testing for stationarity and the generation of confidence limits surrounding the results. A number of tips and tricks that the authors have learnt over many years of using spectral analysis are detailed. Some of these are in the form of tacit questions that are not clearly addressed in many similar books. The book concludes with two appendices: one detailing the implementation of the 'radix-2' Fast Fourier Transform (perhaps the most popular method to attack spectral analysis) and the second detailing principal component analysis (a way in which we can reduce the number of spectral analyses that need to be undertaken).