Subspace-based signal analysis using singular value decomposition
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A unified approach is presented to the related problems of recovering signal parameters from noisy observations and identifying linear system model parameters from observed input/output signals, both using singular value decomposition (SVD) techniques. Both known and new SVD-based identification methods are classified in a subspace-oriented scheme. The SVD of a matrix constructed from the observed signal data provides the key step in a robust discrimination between desired signals and disturbing signals in terms of signal and noise subspaces. The methods that are presented are distinguished by the way in which the subspaces are determined and how the signal or system model parameters are extracted from these subspaces. Typical examples, such as the direction-of-arrival problem and system identification from input/output measurements, are elaborated upon, and some extensions to time-varying systems are given.>
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- Authors
- Van Der Veen, A.-J., Deprettere, E. F., & Swindlehurst, A. L.
- Affiliations
- Delft University of Technology (NL)
- Brigham Young University (US)
- ORCID
- Swindlehurst, A.L.
- Venue
- Proceedings of the IEEE
- Published
- 1993
- Volume
- 81
- Issue
- 9
- Pages
- 1277-1308
- ISSN
- 0018-9219 (ISSN-L)
- OpenAlex
- W2130739159
- Cited by
- 348
- References
- 147
- Citations by year
- 2025: 5 · 2024: 7 · 2023: 8 · 2022: 11 · 2021: 15 · 2020: 11 · 2019: 13 · 2018: 13 · 2017: 17 · 2016: 13 · 2015: 12 · 2014: 20 · 2013: 13 · 2012: 8
- FWCI
- 16.34
- Type
- journal article
- Language
- en
- Open access
- green
- resolver.tudelft.nl
- Locations
- Proceedings of the IEEE (publishedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Research Repository (Delft University of Technology) (submittedVersion)
- Funders
- National Science Foundation
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
- Physical Sciences › Mathematics › Applied Mathematics › Statistical and numerical algorithms
- Physical Sciences › Computer Science › Signal Processing › Direction-of-Arrival Estimation Techniques
- Keywords
- singular value decomposition, system identification, signal subspace, noise subspace, direction of arrival estimation, time-varying systems, input-output measurements
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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