Titlebook: Blind Estimation Using Higher-Order Statistics; Asoke Kumar Nandi (David Jardine Chair of Electric Book 1999 Springer Science+Business Med

白楊魚(yú)

idiopathic

Book 1999communications). There is also a chapter devoted to robust cumulantestimation, an important problem where HOS results have beenencouraging. ..Blind Estimation Using Higher-Order Statistics. is an invaluablereference for researchers, professionals and graduate students workingin signal processing and

Gingivitis

sonar andcommunications). There is also a chapter devoted to robust cumulantestimation, an important problem where HOS results have beenencouraging. ..Blind Estimation Using Higher-Order Statistics. is an invaluablereference for researchers, professionals and graduate students workingin signal processing and978-1-4419-5078-9978-1-4757-2985-6

流動(dòng)性

Morphine

Asoke Kumar Nandi (David Jardine Chair of Electric

得罪

https://doi.org/10.1007/3-540-29252-7ignals of an array of microphones, as long as the signals to be extracted fulfil certain conditions [62, 63] . Wireless communications is another usual application field of signal separation techniques. In a CDMA (Code Division Multiple Access) environment several users share the same radio channel

拘留

Higher-Order and Cyclostationary Statistics,er statistical information. Autocorrelations and cross-correlations are examples of second-order statistics (SOS). The power spectrum which is widely used and contains useful information is again based on the second-order statistics in that the power spectrum is the one-dimensional Fourier transform

Galactogogue

Blind Signal Equalisation,his is in effect inverse system modelling, an architecture that is well-known in adaptive filtering theory. The cascade of channel and equaliser should constitute an identity operation, with the exception of a time delay and linear phase shift being allowed.

運(yùn)動(dòng)吧

GRIEF

Blind Source Separation,om another set of measurements which were generated as mixtures of the initial set. Since usually neither the original signals — called . — nor the mixing transformation are known, this is certainly a challenging problem of multichannel blind estimation. One of the most typical examples is the socal