List of references on spectral line analysis

Abstract

The spectral line analysis (SLA), also called line spectrum analysis, deals with the problem of extracting information on sinusoidal signals from noise-corrupted measurements. It has numerous applications in sonar, radar, underwater surveillance, communications, geophysical exploration, speech analysis, nuclear physics and other fields. The sinusoids-in-noise model, called the hidden periodicity model in the time series literature, also has a significant theoretical relevance as a typical example of a composite spectrum consisting of both continuous and discrete (or line) components, and it is frequently used in theoretical and empirical performance studies of spectral estimation methods. Owing to its practical and theoretical interest, the SLA is a ubiquituous topic in the signal processing literature. When seen from outside by a newcomer, the SLA may appear as a rather narrow field of research. Actually, it contains a myriad of techniques and analysis methods, many of them borrowed from other fields, such as time-series analysis, system identification, matrix algebra, multivariate statistical analysis, complexity theory and others. In the following, we provide a comprehensive list of basic and recent references on SLA. While this list is not, and cannot be, exhaustive, it is believed that it includes most of the key references. The list should be useful to all those interested in the SLA problem, particularly to the newcomers but also to the researchers trying to find the roots of a specific result or recent references for a particular topic. The field of SLA has many ramifications in other fields of signal processing. Many SLA methods can, for example, be used to estimate the parameters of a transient signal, such as a damped sine wave, or the direction-of-arrival of the plane waves impinging on an array of sensors. In general, references on the methods used to solve signal processing problems related to SLA are not provided, unless those methods are strictly relevant to the SLA problem.