Sinusoidal Order Estimation using Angles between Subspaces

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Abstract

Abstract in Undetermined
We consider the problem of determining the order of a parametric model from a noisy signal based on the geometry of the space. More specifically, we do this using the nontrivial angles between the candidate signal subspace model and the noise subspace. The proposed principle is closely related to the subspace orthogonality property known from the MUSIC algorithm, and we study its properties and compare it to other related measures. For the problem of estimating the number of complex sinusoids in white noise, a computationally efficient implementation exists, and this problem is therefore considered in detail. In computer simulations, we compare the proposed method to various well-known methods for order estimation. These show that the proposed method outperforms the other previously published subspace methods and that it is more robust to the noise being colored than the previously published methods. Copyright (C) 2009 Mads Graesboll Christensen et al.

Detaljer

Författare
Enheter & grupper
Forskningsområden

Ämnesklassifikation (UKÄ) – OBLIGATORISK

  • Sannolikhetsteori och statistik
Originalspråkengelska
Artikelnummer948756
TidskriftEurasip Journal on Advances in Signal Processing
StatusPublished - 2009
PublikationskategoriForskning
Peer review utfördJa

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