Second-Order Cyclostationary Statistics-Based Blind Source Extraction From Convolutional Mixtures

Blind source extraction (BSE) aims to extract the source of interest (SOI) from the outputs of a mixing system, which is a challenging problem. A property existing in many signals is cyclostationarity and this property has been widely exploited in BSE. While various cyclostationarity-based BSE methods have been reported in the literature, they usually require the mixing system to be instantaneous. In this paper, we address BSE in the context that the mixing system is convolutional. Specifically, a new BSE method is developed to extract cyclostationary source signal from the outputs of a multiple-input-multiple-output finite-impulse-response mixing system. It is shown that if the SOI has a unique cyclostationary frequency, it can be recovered from the measured data. The effectiveness of the proposed BSE method is demonstrated by simulation results.

Abstract licence: CC BY-NC-ND

25 more references, whose wording the paper’s licence keeps private.

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.