Statistical analysis of state-covariance subspace-estimation methods

Ali Nasiri Amini, Tryphon T. Georgiou

Research output: Contribution to journalConference article

3 Scopus citations

Abstract

We present a statistical analysis of subspace-based methods for the retrieval of sinusoids, in the style of [3], applied to the general framework introduced in [1,2]. The formalism in [1,2] encompasses the modern nonlinear methods of MUSIC and ESPRIT and yields methods of even higher resolution. These rely on eigen decomposition of state-covariances of linear systems, as opposed to eigen decomposition of Toeplitz matrices (originating from antenna arrays or tapped delay-lines and treated, e.g., in [3]). We focus on the variability of estimates when the theory is applied to sampled covariances obtained from finite observation records, and in particular, we provide an expression for the variance of the angle operator between estimated and exact signal subspaces.

Original languageEnglish (US)
Pages (from-to)2633-2638
Number of pages6
JournalProceedings of the IEEE Conference on Decision and Control
Volume3
StatePublished - Dec 1 2002
Event41st IEEE Conference on Decision and Control - Las Vegas, NV, United States
Duration: Dec 10 2002Dec 13 2002

Keywords

  • Harmonic retrieval
  • Identification
  • Modeling
  • Spectral analysis
  • Subspace methods

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