New methods for computing the Pisarenko vector

Bassam R. Shaer, Mohammed A Hasan

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

Abstract

In this paper we show that the Pisarenko vector for harmonic retrieval problems can be obtained without explicit eigendecomposition. The smallest eigenvalue and corresponding eigenvector of a covariance matrix are computed using higher order convergent methods which include the Newton method as special case. An implementation that relies on QR factorization and less on matrix inversion is presented. This approach can also be used to compute the largest eigenpair by appropriately choosing the initial condition. Additionally, an approach is proposed to accelerate the developed methods considerably by using the double step Newton method. Several randomly generated test problems are used to evaluate the performance and the computational cost of the methods.

Original languageEnglish (US)
Pages (from-to)III/3033-III/3036
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume3
StatePublished - Jul 11 2002
Event2002 IEEE International Conference on Acoustic, Speech, and Signal Processing - Orlando, FL, United States
Duration: May 13 2002May 17 2002

Fingerprint Dive into the research topics of 'New methods for computing the Pisarenko vector'. Together they form a unique fingerprint.

Cite this