Block time and frequency domain modified covariance algorithms for spectral analysis

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7 Citations (Scopus)

Abstract

Block modified covariance algorithms are proposed for autoregressive (AR) parametric spectral estimation. First, we develop the block modified covariance algorithm (BMCA) which can be implemented either in the time or in the frequency domain-with the latter being more efficient in high-order cases. A block algorithm is also developed for the energy weighted combined forward and backward prediction. This algorithm is called energy weighted BMCA (EWBMCA). In addition, three updating schemes are presented, namely block-by-block, sample-by-sample, and sample-by-sample with time-scale separation. The performance of the proposed algorithms is examined with stationary and nonstationary narrowband and broadband processes, and also with sinusoids in noise. Lastly, we discuss the computational complexity of the proposed algorithm and we give performance comparisons to existing modified covariance algorithms.

Original languageEnglish (US)
Pages (from-to)3138-3152
Number of pages15
JournalIEEE Transactions on Signal Processing
Volume41
Issue number11
DOIs
StatePublished - Nov 1993

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Spectrum analysis
Computational complexity

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Block time and frequency domain modified covariance algorithms for spectral analysis. / Spanias, Andreas.

In: IEEE Transactions on Signal Processing, Vol. 41, No. 11, 11.1993, p. 3138-3152.

Research output: Contribution to journalArticle

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