Power spectrum estimation techniques have proved essential in many applications, such as communications, sonar, radar, speech=image processing, geophysics, and biomedical signal processing [22,25,26,35]. In power spectrum estimation, the process under consideration is treated as a superposition of statistically uncorrelated harmonic components. The distribution of power among these frequency components is the power spectrum. As such, phase relations between frequency components are suppressed. The information in the power spectrum is essentially present in the autocorrelation sequence, which would suffice for the complete statistical description of a Gaussian process of known mean. However, there are applications where there is a wealth of information in higher-order spectra (HOS) (of order greater than 2) . The third-order spectrum is commonly referred to as bispectrum, the fourthorder one as trispectrum, and in fact, the power spectrum is also a member of the HOS class; it is the second-order spectrum. HOS consist of higher-order moment spectra, which are defined for deterministic signals, and cumulant spectra, which are defined for random processes.
|Original language||English (US)|
|Title of host publication||The Digital Signal Processing Handbook, Second Edition|
|Subtitle of host publication||The Digital Signal Processing Handbook, Second Edition: Wireless, Networking, Radar, Sensor Array Processing, and Nonlinear Signal Processing|
|State||Published - Jan 1 2009|
All Science Journal Classification (ASJC) codes
- Computer Science(all)