A regularized maximum likelihood estimator for the period of a cyclostationary process

Ramírez García, David; Schreier, Peter J.; Vía Rodríguez, Javier; Santamaría Caballero, Luis Ignacio; Scharf, Louis L.

doi:10.1109/ACSSC.2014.7094815

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Identificadores

URI: http://hdl.handle.net/10902/9442

DOI: 10.1109/ACSSC.2014.7094815

ISBN: 978-1-4799-8298-1

ISBN: 978-1-4799-8297-4

Fecha

2014

Derechos

© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Publicado en

48th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, California, 2014, 1972-1976

Editorial

IEEE

Enlace a la publicación

https://doi.org/10.1109/ACSSC.2014.7094815

Resumen/Abstract

We derive an estimator of the cycle period of a univariate cyclostationary process based on an information-theoretic criterion. Transforming the univariate cyclostationary process into a vector-valued wide-sense stationary process allows us to obtain the structure of the covariance matrix, which is block-Toeplitz, and its block size depends on the unknown cycle period. Therefore, we sweep the block size and obtain the ML estimate of the covariance matrix, required for the information-theoretic criterion. Since there are no closed-form ML estimates of block-Toeplitz matrices, we asymptotically approximate them as block-circulant. Finally, some numerical examples show the good performance of the proposed estimator.

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