Tunable line spectral estimators based on state-covariance subspace analysis

@article{Amini2006TunableLS,
  title={Tunable line spectral estimators based on state-covariance subspace analysis},
  author={Ali Nasiri Amini and Tryphon T. Georgiou},
  journal={IEEE Transactions on Signal Processing},
  year={2006},
  volume={54},
  pages={2662-2671}
}
Subspace methods for spectral analysis can be adapted to the case where state covariance of a linear filter replaces the traditional Toeplitz matrix formed out of a partial autocorrelation sequence of a time series. This observation forms the basis of a new framework for spectral analysis. The goal of this paper is to quantify potential advantages in working with state-covariance data instead of the autocorrelation sequence. To this end, we identify tradeoffs between resolution and robustness… CONTINUE READING

References

Publications referenced by this paper.
Showing 1-10 of 13 references

State covariance spectral estimation,

  • A. N. Amini
  • Master’s thesis, Elect. and Computer Eng. Dept…
  • 2003
Highly Influential
3 Excerpts

Optimum Arrray Processing

  • H.L.V. Trees
  • New York: Wiley
  • 2002

Introduction to Spectral Analysis

  • P. Stoica, R. Moses
  • Upper Saddle River, NJ: Prentice-Hall
  • 1997
2 Excerpts

Söderstörm, “Statistical analysis of MUSIC and subspace rotation estimates of sinusoidal frequencies,

  • T. P. Stoica
  • IEEE Trans. Signal Process.,
  • 1991
1 Excerpt

Statistical analysis of MUSIC and subspace rotation estimates of sinusoidal frequencies

  • Stoica, T. Söderstörm
  • Matrix Perturbation Theory
  • 1990

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