Skip to main content
eScholarship
Open Access Publications from the University of California

UC Riverside

UC Riverside Previously Published Works bannerUC Riverside

Blind system identification using minimum noise subspace

Abstract

Developing fast and robust methods for identifying multiple FIR channels driven by an unknown common source is important for wireless communications, speech reverberation cancellation, and other applications. In this correspondence, we present a new method that exploits a minimum noisesubspace (MNS). The MNS is computed from a set of channel output pairs that form a “tree”. The “tree” exploits, with minimum redundancy, the diversity among all channels. The MNS method is much more efficient in computation than a standard subspace method. The noise robustness of the MNS method is illustrated by simulation

Many UC-authored scholarly publications are freely available on this site because of the UC's open access policies. Let us know how this access is important for you.

Main Content
For improved accessibility of PDF content, download the file to your device.
Current View