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The asymptotic Cramer-Rao lower bound for blind signal separation

Authors: H. Sahlin; U. Lindgren;

The asymptotic Cramer-Rao lower bound for blind signal separation

Abstract

This paper considers some aspects of the source separation problem. Unmeasurable source signals are assumed to be mixed by means of a channel system resulting in measurable output signals. These output signals can be used to determine a separation structure in order to extract the sources. When solving the source separation problem the channel filter parameters have to be estimated. This paper presents a compact and computationally appealing formula for computing a lower bound for the variance of these parameters, in a general many inputs many outputs scenario. This lower bound is the asymptotic (assuming the number of data samples to be large) Cramer-Rao lower bound. The CRLB formula is developed further for the two-input two-output system and compared with the results from a recursive prediction error method.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
5
Average
Top 10%
Top 10%
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