Reconstructing analytic dinosaurs : polynomial eigenvalue decomposition for eigenvalues with unmajorised ground truth
Schlecht, Sebastian J. and Weiss, Stephan; (2024) Reconstructing analytic dinosaurs : polynomial eigenvalue decomposition for eigenvalues with unmajorised ground truth. In: 32nd European Signal Processing Conference. IEEE, FRA, pp. 1287-1291. ISBN 9789464593617
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Abstract
This paper proposes a novel method for accurately estimating the ground truth analytic eigenvalues from estimated space-time covariance matrices, where the estimation process obscures any intersection of eigenvalues with probability one. The approach involves grouping sufficiently separated, bin-wise eigenvalues into segments that belong to analytic functions and then solves a permutation problem to align these segments. By leveraging an inverse partial discrete Fourier transform and a linear assignment algorithm, the proposed EigenBone method retrieves analytic eigenvalues efficiently and accurately. Experimental results demonstrate the effectiveness of this approach in accurately reconstructing eigenvalues from noisy estimates. Overall, the proposed method offers a robust solution for approximating analytic eigenvalues in scenarios where state-of-the-art methods may fail.
ORCID iDs
Schlecht, Sebastian J. and Weiss, Stephan ORCID: https://orcid.org/0000-0002-3486-7206;-
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Item type: Book Section ID code: 89655 Dates: DateEvent30 August 2024Published22 May 2024AcceptedSubjects: Science > Mathematics
Technology > Electrical engineering. Electronics Nuclear engineering > TelecommunicationDepartment: Faculty of Engineering > Electronic and Electrical Engineering
Technology and Innovation Centre > Sensors and Asset ManagementDepositing user: Pure Administrator Date deposited: 18 Jun 2024 15:32 Last modified: 01 Oct 2024 13:02 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/89655