An accurate perturbation analysis algorithm for music with Toeplitz covariance matrix

Yang Hu; Yimin Liu; Xiqin Wang

ICASSP 2017accepted0 citations

An accurate perturbation analysis algorithm for music with Toeplitz covariance matrix

Abstract

In this paper, a new perturbation analysis algorithm for the MUltiple SIgnal Classification (MUSIC) estimator applied to a Hermitian Toeplitz covariance matrix is presented. Inspired by the perspective that the MUSIC algorithm can be recognized as a structured matrix approximation, the perturbation of parameter estimates can be predicted more accurately by seeking the minimum of a Frobenius norm. The prediction results are analytically expressed through a weighted least squares method. The performance of the MUSIC estimators can also be predicted using our algorithm.

BibTeX

@inproceedings{icassp2017_anaccuratepertur,
  title = {An accurate perturbation analysis algorithm for music with Toeplitz covariance matrix},
  author = {Yang Hu and Yimin Liu and Xiqin Wang},
  booktitle = {ICASSP 2017},
  year = {2017}
}