Abstract / truncated to 115 words (read the full abstract)

The theory of statistical signal processing finds a wide variety of applications in the fields of data communications, such as in channel estimation, equalization and symbol detection, and sensor array processing, as in beamforming, and radar systems. Indeed, a large number of these applications can be interpreted in terms of a parametric estimation problem, typically approached by a linear filtering operation acting upon a set of multidimensional observations. Moreover, in many cases, the underlying structure of the observable signals is linear in the parameter to be inferred. This dissertation is devoted to the design and evaluation of statistical signal processing methods under realistic implementation conditions encountered in practice. Traditional statistical signal processing techniques intrinsically provide ... toggle 17 keywords

random matrix theory – stieltjes transform – general statistical analysis – g-estimation – stochastic analysis – estimation theory – consistent estimator – sample covariance matrix – sample eigenvalues – sample eigenvectors – statistical signal processing – asymptotic analysis – large-system performance – array processing – reduced-rank filtering – krylov subspace – signal power estimation

Follow @eurasip