Comparison of LCMV Beamforming and Second-Order Statistics BSS

Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 3)

Adaptive LCMV beamforming algorithms have been presented in Chap. 4. In Chaps. 5–7, we have examined NG-SOS-BSS algorithms. Both approaches pursue the same objective, namely to reduce interference signals. This chapter gives a side-by-side comparison of these two approaches.

Originally, convolutive blind signal separation (BSS) techniques have been developed within Widrow's adaptive identification framework [38, 90]. This forms a basis for a natural connection between blind source separation and beamforming. Moreover, as pointed out by Cardoso and Souloumiac in the context of narrowband array processing, BSS techniques achieve the separation by filtering the microphone signals spatially, hence the term “blind beamforming” [21]. This similarity is important to understand the theory. By extracting independent signals out of a mixture, BSS actually forms multiple null beams in the direction of interfering sources [10]. This “equivalence” clearly indicates that sources which are spatially close to each other (or aligned) are not any better separated by BSS-based array processing than by adaptive LCMV beamforming.


Cost Function Fast Fourier Transform Gradient Descent Blind Source Separation Interference Canceler 
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© Springer Science+Business Media, LLC 2009

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