Adaptive Eigensubspace Algorithms for Direction or Frequency Estimation and Tracking

Jar Ferr Yang, Mostafa Kaveh

Research output: Contribution to journalArticlepeer-review

201 Scopus citations

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

Bibliographical note

Funding Information:
Manuscript received February 26, 1987; revised September 27, 1987. This work was supported in part by the National Science Foundation under Grant ECS-8414316, and by the SDIO/IST, managed by the Office of Naval Research, under Contract N00014-86-k-0410. The authors are with the Department of Electrical Engineering, University of Minnesota, Minneapolis, MN 55455. IEEE Log Number 8718438.

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