Abstract
A new framework is presented for the analysis of the performance of detection methods, such as AIC and MDL, which are based on the eigenvalues of the sample covariance matrix. It is shown that theoretical analysis of the probabilities of overestimation and underestimation can be much more conveniently carried out via a proposed, particularly simple, sequence of statistics. Also, the breakdown of these detection methods in the presence of model nonidealities is explored by theory, simulations, and experimentation with real array data. For example, theoretical arguments are given to demonstrate the high degree of sensitivity of the detectors to unknown deviations of the noise from whiteness.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1413-1426 |
| Number of pages | 14 |
| Journal | IEEE Transactions on Signal Processing |
| Volume | 43 |
| Issue number | 6 |
| DOIs | |
| State | Published - Jun 1995 |
Bibliographical note
Funding Information:Manuscript received February 19, 1993; revised November 4, 1994. This work was supported in part by the National Science Foundation under Grant MIP-920208 1 and by the BMDOAST program managed by the Office of Naval Research under Contract "14-92-J-191 1. The associate editor coordinating the review of this paper and approving it for publication was Prof. Daniel Fuhrmann. The authors are with the Department of Electrical Engineering, University of Minnesota, Minneapolis, MN 55455 USA. IEEE Log Number 941 1199.