Atomic Decomposition-based Sparse Recovery for Space-Time Adaptive Processing

Yujie Gu; Yimin D. Zhang

doi:10.1109/acssc.2018.8645270

Atomic Decomposition-based Sparse Recovery for Space-Time Adaptive Processing

Yujie Gu, Yimin D. Zhang

Temple University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Scopus citations

Abstract

The requirement for a high number of training samples is a key factor that limits the application of space-time adaptive processing (STAP) technique in practice. In this paper, by exploiting the low-rank property of the clutter covariance matrix, we formulate an atomic norm minimization problem for the sparse reconstruction of the clutter-plus-noise covariance matrix. By defining the corresponding atomic norm in the continuous angle-Doppler domain, the proposed technique not only substantially reduces the number of required training samples, but also avoids the intrinsic basis mismatch problem that is encountered in conventional sparse reconstruction methods. Simulation results verify the effectiveness of the proposed STAP method and its superiority over existing techniques.

Original language	English
Title of host publication	Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
Editors	Michael B. Matthews
Publisher	IEEE Computer Society
Pages	1116-1120
Number of pages	5
ISBN (Electronic)	9781538692189
DOIs	https://doi.org/10.1109/acssc.2018.8645270
State	Published - 2 Jul 2018
Externally published	Yes
Event	52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 - Pacific Grove, United States Duration: 28 Oct 2018 → 31 Oct 2018

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
Volume	2018-October
ISSN (Print)	1058-6393

Conference

Conference	52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
Country/Territory	United States
City	Pacific Grove
Period	28/10/18 → 31/10/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

Atomic norm minimization
low-rank signal
moving target indication
signal sparsity
space-time adaptive processing (STAP)

Access to Document

10.1109/acssc.2018.8645270

Cite this

Gu, Y., & Zhang, Y. D. (2018). Atomic Decomposition-based Sparse Recovery for Space-Time Adaptive Processing. In M. B. Matthews (Ed.), Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 (pp. 1116-1120). Article 8645270 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2018-October). IEEE Computer Society. https://doi.org/10.1109/acssc.2018.8645270

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Gu, Y & Zhang, YD 2018, Atomic Decomposition-based Sparse Recovery for Space-Time Adaptive Processing. in MB Matthews (ed.), Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018., 8645270, Conference Record - Asilomar Conference on Signals, Systems and Computers, vol. 2018-October, IEEE Computer Society, pp. 1116-1120, 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018, Pacific Grove, United States, 28/10/18. https://doi.org/10.1109/acssc.2018.8645270

Atomic Decomposition-based Sparse Recovery for Space-Time Adaptive Processing. / Gu, Yujie; Zhang, Yimin D.
Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018. ed. / Michael B. Matthews. IEEE Computer Society, 2018. p. 1116-1120 8645270 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2018-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Gu Y, Zhang YD. Atomic Decomposition-based Sparse Recovery for Space-Time Adaptive Processing. In Matthews MB, editor, Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018. IEEE Computer Society. 2018. p. 1116-1120. 8645270. (Conference Record - Asilomar Conference on Signals, Systems and Computers). doi: 10.1109/acssc.2018.8645270

Atomic Decomposition-based Sparse Recovery for Space-Time Adaptive Processing

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Keywords

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