Linearly constrained minimum-"Geometric power" adaptive beamforming using logarithmic moments of data containing Heavy-Tailed noise of unknown statistics

Jin He, Zhong Liu, Kainam Thomas Wong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

14 Citations (Scopus)

Abstract

This letter presents a new adaptive beamforming approach, against arbitrary algebraically tailed impulse noise of otherwise unknown statistics. (This includes all symmetric Q-stable noises with infinite variance or even infinite mean.) This new beamformer iteratively minimizes the "geometric power" of the beamformer's output Y, subject to a prespecified set of linear constraints. This geometric power is defined in terms of the "logarithmic moment" .E{log|Y|}, as an alternative to the customary "fractional lower order moments" (FLOM). This logarithmic-moment beamformer offers these advantages over the FLOM beamformer: 1) simpler computationally in general, 2) needing no prior information nor estimation of the numerical value of the impulse noise's effective characteristic exponent, and 3) applicable to a wider class of heavy-tailed Impulse noises.
Original languageEnglish
Article number910928
Pages (from-to)600-603
Number of pages4
JournalIEEE Antennas and Wireless Propagation Letters
Volume6
DOIs
Publication statusPublished - 1 Dec 2007

Keywords

  • Adaptive arrays
  • Array signal processing
  • Beam steering
  • Focusing
  • Impulse noise
  • Parameter estimation

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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