analysis of time series data can involve the inversion of large covariance matrices. for theclass of arma (p, q) processes there are no exact explicit expressions for these inverses, except for thema (1) process. in practice, the sample covariance matrix can be very large and inversion can becomputationally time consuming and so approximate explicit expressions for the inverse are desirable.thi...

polarimetric synthetic aperture radar (pol.-sar) allows us to implement the recognition and classification of radar targets. this article investigates the arrangement of scatterers by sar data and proposes a new look-up table of region (ltr). this look-up table is based on the combination of (entropy h/anisotropy a) and (anisotropy a/scattering mechanism α), which has not been reported up now. ...

the principle of dimensionality reduction with pca is the representation of the dataset ‘x’in terms of eigenvectors ei ∈ rn  of its covariance matrix. the eigenvectors oriented in the direction with the maximum variance of x in rn carry the most      relevant information of x. these eigenvectors are called principal components [8]. assume that n images in a set are originally represented in mat...

Covariance matrix tapers have a long history in signal processing and related fields. Examples of applications include autoregressive models (promoting banded structure) or beamforming (widening the spectral null width associated with an interferer). In this paper, focus is on high-dimensional setting where dimension <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="ht...