Adaptive Eigensubspace Algorithms for Direction or Frequency Estimation and Tracking

Jar Ferr Yang, Mostafa Kaveh

Research output: Contribution to journalArticlepeer-review

208 Citations (Scopus)

Abstract

This paper presents an adaptive estimator, and its practical implementations, of the complete noise or signal subspace of a sample covariance matrix. The general formulation of the proposed estimator results from an asymptotic argument which shows the signal or noise subspace computation to be equivalent to a constrained gradient search procedure. A highly parallel algorithm, denoted the inflation method, is introduced for the estimation of the noise subspace. The simulation results of these adaptive estimators show that the adaptive subspace algorithms perform substantially better than Thompson’s adaptive version of Pisarenko’s technique [1] in estimating frequencies or directions of arrival (DOA) of plane waves. For tracking nonstationary parameters, the simulation results also show that the adaptive subspace algorithms are better than direct eigendecomposition methods for which computational complexity is much higher than the adaptive versions.

Original languageEnglish
Pages (from-to)241-251
Number of pages11
JournalIEEE Transactions on Acoustics, Speech, and Signal Processing
Volume36
Issue number2
DOIs
Publication statusPublished - 1988 Feb

All Science Journal Classification (ASJC) codes

  • Signal Processing

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