Title :
Structured covariance estimation via maximal representation of convex sets
Author :
Robey, Frank C. ; Fuhrmann, Daniel R.
Author_Institution :
Electron. Syst. & Signals Res. Lab., Washington Univ., St. Louis, MO, USA
Abstract :
A method that utilizes the properties of the set of structured covariance matrices in order to perform covariance estimation is presented. The constraint space of allowable covariance matrices is characterized as a convex cone within the space of positive definite Hermitian matrices. The Caratheodory representation theorem is invoked to show that any member of this cone can be represented as a positive weighted sum of a small number of generating dyads. Simulation results confirm the computational efficiency of this approach and its significance in an adaptive beamforming application
Keywords :
computerised signal processing; matrix algebra; set theory; Caratheodory representation theorem; adaptive beamforming; computational efficiency; constraint space; convex cone; convex sets; maximal representation; positive definite Hermitian matrices; sensor array processing; structured covariance estimation; structured covariance matrices; Array signal processing; Covariance matrix; Equations; Interference; Sensor arrays; Sensor systems; Signal processing; Signal processing algorithms; Vectors; Working environment noise;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference on
Conference_Location :
Toronto, Ont.
Print_ISBN :
0-7803-0003-3
DOI :
10.1109/ICASSP.1991.150092
