Accession Number : ADA131209

Title :   Robust Linear Filtering for Multivariable Stationary Time Series.

Descriptive Note : Technical rept.,

Corporate Author : CONNECTICUT UNIV STORRS DEPT OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE

Personal Author(s) : Tsaknakis,Haralampos ; Papantoni-Kazakos,P

PDF Url : ADA131209

Report Date : Apr 1983

Pagination or Media Count : 69

Abstract : The problem of asymptotic, non-causal linear filtering for statistically contaminated multivariable stationary time series is considered. The spectra of both the signal and the noise components of the observation process are assumed to belong to certain convex and compact classes. The minimax criterion of optimality is adopted, and for some specific spectral classes the corresponding solutions are found. The performance of those solutions is studied, where the performance criteria used are efficiency, error variation within the classes and breakdown curves or points. Some examples are studied quantitatively. Author)

Descriptors :   *Mathematical filters, *Time series analysis, *Information theory, Stationary, Multivariate analysis, Eigenvectors, Optimization, Theorems, Computations, Tables(Data), Matrices(Mathematics), Asymptotic series, Contamination, Signal to noise ratio, Minimax technique

Subject Categories : Statistics and Probability
      Cybernetics

Distribution Statement : APPROVED FOR PUBLIC RELEASE