Shift-invariant Subspace Tracking with Missing Data

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Subspace tracking is an important problem in signal processing that finds applications in wireless communications, video surveillance, and source localization in radar and sonar. In recent years, it is recognized that a low-dimensional subspace can be estimated and tracked reliably even when the data vectors are partially observed with many missing entries, which is greatly desirable when processing high-dimensional and high-rate data to reduce the sampling requirement. This paper is motivated by the observation that the underlying low-dimensional subspace may possess additional structural properties induced by the physical model of data, which if harnessed properly, can greatly improve subspace tracking performance. As a case study, this paper investigates the problem of tracking direction-of-arrivals from subsampled observations in a unitary linear array, where the signals lie in a subspace spanned by columns of a Vandermonde matrix. We exploit the shift-invariant structure by mapping the data vector to a latent Hankel matrix, and then perform tracking over the Hankel matrices by exploiting their low-rank properties. Numerical simulations are conducted to validate the superiority of the proposed approach over existing subspace tracking methods that do not exploit the additional shift-invariant structure in terms of tracking speed and agility.

Original languageEnglish (US)
Title of host publication2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages8222-8225
Number of pages4
ISBN (Electronic)9781479981311
DOIs
StatePublished - May 1 2019
Event44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom
Duration: May 12 2019May 17 2019

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2019-May
ISSN (Print)1520-6149

Conference

Conference44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
CountryUnited Kingdom
CityBrighton
Period5/12/195/17/19

Fingerprint

Direction of arrival
Sonar
Structural properties
Signal processing
Radar
Sampling
Communication
Computer simulation
Processing

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Cho, M. M., & Chi, Y. (2019). Shift-invariant Subspace Tracking with Missing Data. In 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings (pp. 8222-8225). [8683025] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2019-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2019.8683025
Cho, Myung (Michael) ; Chi, Yuejie. / Shift-invariant Subspace Tracking with Missing Data. 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 8222-8225 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).
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Cho, MM & Chi, Y 2019, Shift-invariant Subspace Tracking with Missing Data. in 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings., 8683025, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2019-May, Institute of Electrical and Electronics Engineers Inc., pp. 8222-8225, 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019, Brighton, United Kingdom, 5/12/19. https://doi.org/10.1109/ICASSP.2019.8683025

Shift-invariant Subspace Tracking with Missing Data. / Cho, Myung (Michael); Chi, Yuejie.

2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 8222-8225 8683025 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2019-May).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - Subspace tracking is an important problem in signal processing that finds applications in wireless communications, video surveillance, and source localization in radar and sonar. In recent years, it is recognized that a low-dimensional subspace can be estimated and tracked reliably even when the data vectors are partially observed with many missing entries, which is greatly desirable when processing high-dimensional and high-rate data to reduce the sampling requirement. This paper is motivated by the observation that the underlying low-dimensional subspace may possess additional structural properties induced by the physical model of data, which if harnessed properly, can greatly improve subspace tracking performance. As a case study, this paper investigates the problem of tracking direction-of-arrivals from subsampled observations in a unitary linear array, where the signals lie in a subspace spanned by columns of a Vandermonde matrix. We exploit the shift-invariant structure by mapping the data vector to a latent Hankel matrix, and then perform tracking over the Hankel matrices by exploiting their low-rank properties. Numerical simulations are conducted to validate the superiority of the proposed approach over existing subspace tracking methods that do not exploit the additional shift-invariant structure in terms of tracking speed and agility.

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Cho MM, Chi Y. Shift-invariant Subspace Tracking with Missing Data. In 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. p. 8222-8225. 8683025. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2019.8683025