Blind Estimation Using Higher-Order StatisticsAsoke Nandi In the signal-processing research community, a great deal of progress in higher-order statistics (HOS) began in the mid-1980s. These last fifteen years have witnessed a large number of theoretical developments as well as real applications. Blind Estimation Using Higher-Order Statistics focuses on the blind estimation area and records some of the major developments in this field. Blind Estimation Using Higher-Order Statistics is a welcome addition to the few books on the subject of HOS and is the first major publication devoted to covering blind estimation using HOS. The book provides the reader with an introduction to HOS and goes on to illustrate its use in blind signal equalisation (which has many applications including (mobile) communications), blind system identification, and blind sources separation (a generic problem in signal processing with many applications including radar, sonar and communications). There is also a chapter devoted to robust cumulant estimation, an important problem where HOS results have been encouraging. Blind Estimation Using Higher-Order Statistics is an invaluable reference for researchers, professionals and graduate students working in signal processing and related areas. |
Contents
II | 2 |
IV | 4 |
V | 12 |
VI | 14 |
VII | 19 |
VIII | 21 |
IX | 23 |
| 25 | |
| 167 | |
XXVII | 169 |
XXVIII | 171 |
XXIX | 185 |
XXX | 188 |
XXXI | 189 |
XXXII | 203 |
XXXIII | 220 |
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Common terms and phrases
adaptive adaptive equalisation AGTM estimates algorithm AR(p ARMA ARMA(p,q array autocorrelation bispectrum blind equalisation blind system identification Bussgang channel computational convergence convolution cost function covariance matrix cumulant estimates cyclostationary DCQK defined diagonal equaliser filter equaliser input equation error exponential Figure filter coefficients fourth-order cumulants fractionally spaced Gaussian distribution Gaussian noise gradient gradient descent higher order statistics higher-order statistics HOS-based ICA-HOEVD IEEE Transactions impulse response independent input distribution iterations kurtosis linear MA(q mean estimator minimum phase mixing matrix model order Monte Carlo runs multichannel non-Gaussian nonlinear Normalized frequency Nyquist number of samples observations obtained order cumulants order statistics orthogonal parameter estimates performance power spectrum random scatter diagram second-order statistics sequence Signal Processing simulations singular value solution source signals spectral standard deviation statistical independence symmetric system identification Table third-order cumulants tion Transactions on Signal variance vector zero zero-mean