Wideband Direction Finding via Spatial Decimation and Coupled Canonical Polyadic Decomposition

Gui Chen Yu; Xiao Feng Gong; Jia Cheng Jiang; Zhi Wen Liu; You Gen Xu

doi:10.1007/978-3-030-22808-8_58

Wideband Direction Finding via Spatial Decimation and Coupled Canonical Polyadic Decomposition

Gui Chen Yu^*, Xiao Feng Gong, Jia Cheng Jiang, Zhi Wen Liu, You Gen Xu

^*Corresponding author for this work

Dalian University of Technology

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

Abstract

We propose an algorithm for direction of arrival (DOA) estimation of wideband array signals based on spatial decimation and coupled canonical polyadic decomposition. First, the wide-band array out is converted to several frequency bins. Second, we choose the bins that have exponential relationship to do spatial decimation. Finally, we use the coupled canonical polyadic decomposition to identify the steering vector and calculate the DOA. The proposed algorithm makes use of the coupled structures between frequency bins and is the deterministic method, which do not depend on statistical properties. We have compared the proposed algorithm with existing wideband DOA estimation algorithms. The numerical results are provided to demonstrate the performance of the proposed algorithm.

Original language	English
Title of host publication	Advances in Neural Networks – ISNN 2019 - 16th International Symposium on Neural Networks, ISNN 2019, Proceedings
Editors	Huchuan Lu, Huajin Tang, Zhanshan Wang
Publisher	Springer Verlag
Pages	585-594
Number of pages	10
ISBN (Print)	9783030228071
DOIs	https://doi.org/10.1007/978-3-030-22808-8_58
Publication status	Published - 2019
Externally published	Yes
Event	16th International Symposium on Neural Networks, ISNN 2019 - Moscow, Russian Federation Duration: 10 Jul 2019 → 12 Jul 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11555 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th International Symposium on Neural Networks, ISNN 2019
Country/Territory	Russian Federation
City	Moscow
Period	10/07/19 → 12/07/19

Keywords

Array signal processing
Coupled canonical polyadic decomposition
Direction of arrival estimation
Spatial decimation

Access to Document

10.1007/978-3-030-22808-8_58

Cite this

Yu, G. C., Gong, X. F., Jiang, J. C., Liu, Z. W., & Xu, Y. G. (2019). Wideband Direction Finding via Spatial Decimation and Coupled Canonical Polyadic Decomposition. In H. Lu, H. Tang, & Z. Wang (Eds.), Advances in Neural Networks – ISNN 2019 - 16th International Symposium on Neural Networks, ISNN 2019, Proceedings (pp. 585-594). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11555 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-22808-8_58

Yu, Gui Chen ; Gong, Xiao Feng ; Jiang, Jia Cheng et al. / Wideband Direction Finding via Spatial Decimation and Coupled Canonical Polyadic Decomposition. Advances in Neural Networks – ISNN 2019 - 16th International Symposium on Neural Networks, ISNN 2019, Proceedings. editor / Huchuan Lu ; Huajin Tang ; Zhanshan Wang. Springer Verlag, 2019. pp. 585-594 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{f09deb552bee47d5aeb6f387c7552ac9,

title = "Wideband Direction Finding via Spatial Decimation and Coupled Canonical Polyadic Decomposition",

abstract = "We propose an algorithm for direction of arrival (DOA) estimation of wideband array signals based on spatial decimation and coupled canonical polyadic decomposition. First, the wide-band array out is converted to several frequency bins. Second, we choose the bins that have exponential relationship to do spatial decimation. Finally, we use the coupled canonical polyadic decomposition to identify the steering vector and calculate the DOA. The proposed algorithm makes use of the coupled structures between frequency bins and is the deterministic method, which do not depend on statistical properties. We have compared the proposed algorithm with existing wideband DOA estimation algorithms. The numerical results are provided to demonstrate the performance of the proposed algorithm.",

keywords = "Array signal processing, Coupled canonical polyadic decomposition, Direction of arrival estimation, Spatial decimation",

author = "Yu, \{Gui Chen\} and Gong, \{Xiao Feng\} and Jiang, \{Jia Cheng\} and Liu, \{Zhi Wen\} and Xu, \{You Gen\}",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 16th International Symposium on Neural Networks, ISNN 2019 ; Conference date: 10-07-2019 Through 12-07-2019",

year = "2019",

doi = "10.1007/978-3-030-22808-8\_58",

language = "English",

isbn = "9783030228071",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "585--594",

editor = "Huchuan Lu and Huajin Tang and Zhanshan Wang",

booktitle = "Advances in Neural Networks – ISNN 2019 - 16th International Symposium on Neural Networks, ISNN 2019, Proceedings",

address = "Germany",

}

Yu, GC, Gong, XF, Jiang, JC, Liu, ZW & Xu, YG 2019, Wideband Direction Finding via Spatial Decimation and Coupled Canonical Polyadic Decomposition. in H Lu, H Tang & Z Wang (eds), Advances in Neural Networks – ISNN 2019 - 16th International Symposium on Neural Networks, ISNN 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11555 LNCS, Springer Verlag, pp. 585-594, 16th International Symposium on Neural Networks, ISNN 2019, Moscow, Russian Federation, 10/07/19. https://doi.org/10.1007/978-3-030-22808-8_58

Wideband Direction Finding via Spatial Decimation and Coupled Canonical Polyadic Decomposition. / Yu, Gui Chen; Gong, Xiao Feng; Jiang, Jia Cheng et al.
Advances in Neural Networks – ISNN 2019 - 16th International Symposium on Neural Networks, ISNN 2019, Proceedings. ed. / Huchuan Lu; Huajin Tang; Zhanshan Wang. Springer Verlag, 2019. p. 585-594 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11555 LNCS).

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

TY - GEN

T1 - Wideband Direction Finding via Spatial Decimation and Coupled Canonical Polyadic Decomposition

AU - Yu, Gui Chen

AU - Gong, Xiao Feng

AU - Jiang, Jia Cheng

AU - Liu, Zhi Wen

AU - Xu, You Gen

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2019.

PY - 2019

Y1 - 2019

N2 - We propose an algorithm for direction of arrival (DOA) estimation of wideband array signals based on spatial decimation and coupled canonical polyadic decomposition. First, the wide-band array out is converted to several frequency bins. Second, we choose the bins that have exponential relationship to do spatial decimation. Finally, we use the coupled canonical polyadic decomposition to identify the steering vector and calculate the DOA. The proposed algorithm makes use of the coupled structures between frequency bins and is the deterministic method, which do not depend on statistical properties. We have compared the proposed algorithm with existing wideband DOA estimation algorithms. The numerical results are provided to demonstrate the performance of the proposed algorithm.

AB - We propose an algorithm for direction of arrival (DOA) estimation of wideband array signals based on spatial decimation and coupled canonical polyadic decomposition. First, the wide-band array out is converted to several frequency bins. Second, we choose the bins that have exponential relationship to do spatial decimation. Finally, we use the coupled canonical polyadic decomposition to identify the steering vector and calculate the DOA. The proposed algorithm makes use of the coupled structures between frequency bins and is the deterministic method, which do not depend on statistical properties. We have compared the proposed algorithm with existing wideband DOA estimation algorithms. The numerical results are provided to demonstrate the performance of the proposed algorithm.

KW - Array signal processing

KW - Coupled canonical polyadic decomposition

KW - Direction of arrival estimation

KW - Spatial decimation

UR - http://www.scopus.com/inward/record.url?scp=85068617323&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-22808-8_58

DO - 10.1007/978-3-030-22808-8_58

M3 - Conference contribution

AN - SCOPUS:85068617323

SN - 9783030228071

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 585

EP - 594

BT - Advances in Neural Networks – ISNN 2019 - 16th International Symposium on Neural Networks, ISNN 2019, Proceedings

A2 - Lu, Huchuan

A2 - Tang, Huajin

A2 - Wang, Zhanshan

PB - Springer Verlag

T2 - 16th International Symposium on Neural Networks, ISNN 2019

Y2 - 10 July 2019 through 12 July 2019

ER -

Yu GC, Gong XF, Jiang JC, Liu ZW, Xu YG. Wideband Direction Finding via Spatial Decimation and Coupled Canonical Polyadic Decomposition. In Lu H, Tang H, Wang Z, editors, Advances in Neural Networks – ISNN 2019 - 16th International Symposium on Neural Networks, ISNN 2019, Proceedings. Springer Verlag. 2019. p. 585-594. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-22808-8_58

Wideband Direction Finding via Spatial Decimation and Coupled Canonical Polyadic Decomposition

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this