A tutorial review of both linear and quadratic representations is given. The linear representations discussed are the short-time Fourier transform and the wavelet transform. The discussion of quadratic representations concentrates on the Wigner distribution, the ambiguity function, smoothed versions of the Wigner distribution, and various classes of quadratic time-frequency representations. Examples of the application of these representations to typical problems encountered in time-varying signal processing are provided. >

Time-frequency signal processing algorithms: analysis and synthesis using wigner distributions

Linear and quadratic time-frequency signal representations

Time Frequency Signal Analysis and Processing covers fundamental concepts, principles and techniques, treatment of specialised and advanced topics, methods and applications, including results of recent research. This book deals with the modern methodologies, key techniques and concepts that form the core of new technologies used in IT, multimedia, telecommunications as well as most fields of engineering, science and technology. It focuses on advanced techniques and methods that allow a refined extraction and processing of information, allowing efficient and effective decision making that would not be possible with classical techniques.

Time-Frequency Signal Analysis and Processing: A Comprehensive Reference

Time-Frequency Signal Analysis and Processing

The short-time Fourier transform (STFT), the ambiguity function (AF), and the Wigner distribution (WD) are mixed time-frequency signal representations that use Fourier transform techniques to map a one-dimensional function of time into a two-dimensional function of time and frequency. These mixed time-frequency mappings have been used to analyze the local frequency characteristics of a variety of signals and systems. Although much work has also been done to develop STFT and AF synthesis algorithms that can be used to implement a variety of time-varying signal processing operations, no such synthesis techniques have thus far been developed for the WD. In this paper, a signal synthesis algorithm that works directly with the real-valued high-resolution WD will be derived. Examples of how this WD synthesis procedure can be used to perform time-varying filtering operations or signal separation will be given.

Time-varying filtering and signal estimation using Wigner distribution synthesis techniques

It is shown that a correct use of the Wigner distribution (WD) for time-frequency signal analysis requires use of the analytic signal. This version, often referred to as the Wigner-Ville distribution (WVD), is straightforward to compute, does not exhibit any aliasing problem, and introduces no frequency artifacts. The problems introduced by the use of the Wigner distribution with a real signal are clarified. >

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Time-frequency signal processing algorithms: analysis and synthesis using wigner distributions

Citations

Linear and quadratic time-frequency signal representations

Time-Frequency Signal Analysis and Processing: A Comprehensive Reference

Time-Frequency Signal Analysis and Processing

Time-varying filtering and signal estimation using Wigner distribution synthesis techniques

Note on the use of the Wigner distribution for time-frequency signal analysis

Related Papers (5)

The wigner distribution - a tool for time-frequency signal analysis part ii: discrete-time signals

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Improved time-frequency representation of multicomponent signals using exponential kernels

On the Quantum Correction For Thermodynamic Equilibrium