Title :
Fast recursive subspace updating for detection and angle estimation of multiple sources
Author_Institution :
GE Res. & Dev. Center, Schenectady, NY, USA
Abstract :
This paper is concerned with detection of a number of sources and estimation of their corresponding direction-of-arrival using eigenvalue decomposition updating with subspace structure constraint. The covariance matrix is a low rank matrix plus a noise diagonal matrix. In addition to cleaning up the data, the subspace constraint enables the author to develop an efficient algorithm for updating the principal subspace and the noise eigenvalue
Keywords :
array signal processing; eigenvalues and eigenfunctions; matrix algebra; parameter estimation; signal detection; algorithm; angle estimation; covariance matrix; direction-of-arrival; eigenvalue decomposition; multiple sources; noise diagonal matrix; recursive subspace updating; source detection; subspace structure constraint; Cleaning; Covariance matrix; Eigenvalues and eigenfunctions; Matrix decomposition; Monitoring; Multiple signal classification; Noise level; Recursive estimation; Sensor arrays; Subspace constraints;
Conference_Titel :
Statistical Signal and Array Processing, 1992. Conference Proceedings., IEEE Sixth SP Workshop on
Conference_Location :
Victoria, BC
Print_ISBN :
0-7803-0508-6
DOI :
10.1109/SSAP.1992.246772
