Robust multiple signal classification via probability measure transformation

Koby Todros, Alfred O. Hero

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

Abstract

In this paper, we introduce a new framework for robust MUltiple SIgnal Classification (MUSIC). The proposed framework, called robust measure-transformed (MT) MUSIC, is based on applying a transform to the probability distribution of the received signals, i.e., transformation of the probability measure defined on the observation space. In robust MT-MUSIC, the sample covariance is replaced by the empirical MT-covariance. By judicious choice of the transform, we show that 1) the resulting empirical MT-covariance is B-robust, with bounded influence function that takes negligible values for large norm outliers, and 2) under the assumption of spherically contoured noise distribution, the noise subspace can be determined from the eigendecomposition of the MT-covariance. Furthermore, we derive a new robust measure-transformed minimum description length (MDL) criterion for estimating the number of signals, and extend the MT-MUSIC framework to the case of coherent signals. The proposed approach is illustrated in simulation examples that show its advantages as compared to other robust MUSIC and MDL generalizations.

Original languageEnglish
Article number7001666
Pages (from-to)1156-1170
Number of pages15
JournalIEEE Transactions on Signal Processing
Volume63
Issue number5
DOIs
StatePublished - 1 Mar 2015

Keywords

  • Array processing
  • DOA estimation
  • probability measure transform
  • robust estimation
  • signal subspace estimation

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