Dimension reduction for array processing with robust interference cancellation

M. Li, Yilong Lu

Research output: Contribution to journalArticle

33 Citations (Scopus)

Abstract

We present a robust solution for data reduction in array processing. The purpose is to reduce the computation and improve the performance of applied signal processing algorithms by mapping the data into a lower dimension beamspace (BS) through a transformation. Nulls steering to interference are incorporated into a transformation using the subspace projection technique, and the BS spatial spectrum estimation accuracy is evaluated and maximized with a measure. The derived transformation tries to preserve the full-dimension Cramer-Rao bounds (CRBs) for the parameters of interest while rejecting undesired signals effectively. When compared with an optimal method and an adaptive approach, simulation results show that significant improvements are obtained in terms of BS direction-of-arrival (DOA) estimation root-mean-squared error (RMSE), bias, and resolution probability.
Original languageEnglish
Pages (from-to)103-112
Number of pages10
JournalIEEE Transactions on Aerospace and Electronic Systems
Volume42
Issue number1
DOIs
Publication statusPublished - Jan 2006

Keywords

  • data reduction
  • array processing
  • applied signal processing
  • algorithms
  • beamspace
  • subspace projection
  • spatial spectrum estimation
  • Cramer-Rao bounds

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