Using Coupled Multilinear Rank-(L, L, 1) Block Term Decomposition in Multi-Static-Multi-Pulse MIMO Radar to Localize Targets

Jia Xing Yang; Xiao Feng Gong; Hui Li; You Gen Xu; Zhi Wen Liu

doi:10.1007/978-3-030-22808-8_56

Using Coupled Multilinear Rank-(L, L, 1) Block Term Decomposition in Multi-Static-Multi-Pulse MIMO Radar to Localize Targets

Jia Xing Yang^*, Xiao Feng Gong, Hui Li, You Gen Xu, Zhi Wen Liu

^*Corresponding author for this work

School of Integrated Circuits and Electronics

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

6 Citations (Scopus)

Abstract

We propose a tensorial method for target localization based on multi-static-multi-pulse MIMO radar, which consists of multiple widely separated transmitting and receiving arrays. We show how a set of tensors, which admits a coupled multilinear (ML) rank-(L, L, 1) block term decomposition (BTD), can be constructed from the output signals of different receiving arrays. As such, we compute the coupled ML rank-(L, L, 1) BTD of these tensors to obtain factor matrices. The target locations are then able to be determined from the latent DOA parameters in these factor matrices. The proposed method neither requires prior knowledge, nor assumes orthogonality between probing signals. In addition, by exploiting the coupling, different receiving array outputs are jointly processed, yielding improved performance than uncoupled BTD based methods. Simulation results are provided to illustrate the performance of the proposed method.

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	565-574
Number of pages	10
ISBN (Print)	9783030228071
DOIs	https://doi.org/10.1007/978-3-030-22808-8_56
Publication status	Published - 2019
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

Block Term Decomposition
Direction of arrival of arrival
MIMO radar
Multilinear rank
Tensor

Access to Document

10.1007/978-3-030-22808-8_56

Cite this

Yang, J. X., Gong, X. F., Li, H., Xu, Y. G., & Liu, Z. W. (2019). Using Coupled Multilinear Rank-(L, L, 1) Block Term Decomposition in Multi-Static-Multi-Pulse MIMO Radar to Localize Targets. In H. Lu, H. Tang, & Z. Wang (Eds.), Advances in Neural Networks – ISNN 2019 - 16th International Symposium on Neural Networks, ISNN 2019, Proceedings (pp. 565-574). (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_56

Yang, Jia Xing ; Gong, Xiao Feng ; Li, Hui et al. / Using Coupled Multilinear Rank-(L, L, 1) Block Term Decomposition in Multi-Static-Multi-Pulse MIMO Radar to Localize Targets. 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. 565-574 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{e92d9197d2a740878fbc642c9377b602,

title = "Using Coupled Multilinear Rank-(L, L, 1) Block Term Decomposition in Multi-Static-Multi-Pulse MIMO Radar to Localize Targets",

abstract = "We propose a tensorial method for target localization based on multi-static-multi-pulse MIMO radar, which consists of multiple widely separated transmitting and receiving arrays. We show how a set of tensors, which admits a coupled multilinear (ML) rank-(L, L, 1) block term decomposition (BTD), can be constructed from the output signals of different receiving arrays. As such, we compute the coupled ML rank-(L, L, 1) BTD of these tensors to obtain factor matrices. The target locations are then able to be determined from the latent DOA parameters in these factor matrices. The proposed method neither requires prior knowledge, nor assumes orthogonality between probing signals. In addition, by exploiting the coupling, different receiving array outputs are jointly processed, yielding improved performance than uncoupled BTD based methods. Simulation results are provided to illustrate the performance of the proposed method.",

keywords = "Block Term Decomposition, Direction of arrival of arrival, MIMO radar, Multilinear rank, Tensor",

author = "Yang, \{Jia Xing\} and Gong, \{Xiao Feng\} and Hui Li and Xu, \{You Gen\} and Liu, \{Zhi Wen\}",

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\_56",

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 = "565--574",

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",

}

Yang, JX, Gong, XF, Li, H, Xu, YG & Liu, ZW 2019, Using Coupled Multilinear Rank-(L, L, 1) Block Term Decomposition in Multi-Static-Multi-Pulse MIMO Radar to Localize Targets. 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. 565-574, 16th International Symposium on Neural Networks, ISNN 2019, Moscow, Russian Federation, 10/07/19. https://doi.org/10.1007/978-3-030-22808-8_56

Using Coupled Multilinear Rank-(L, L, 1) Block Term Decomposition in Multi-Static-Multi-Pulse MIMO Radar to Localize Targets. / Yang, Jia Xing; Gong, Xiao Feng; Li, Hui 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. 565-574 (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 - Using Coupled Multilinear Rank-(L, L, 1) Block Term Decomposition in Multi-Static-Multi-Pulse MIMO Radar to Localize Targets

AU - Yang, Jia Xing

AU - Gong, Xiao Feng

AU - Li, Hui

AU - Xu, You Gen

AU - Liu, Zhi Wen

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

PY - 2019

Y1 - 2019

N2 - We propose a tensorial method for target localization based on multi-static-multi-pulse MIMO radar, which consists of multiple widely separated transmitting and receiving arrays. We show how a set of tensors, which admits a coupled multilinear (ML) rank-(L, L, 1) block term decomposition (BTD), can be constructed from the output signals of different receiving arrays. As such, we compute the coupled ML rank-(L, L, 1) BTD of these tensors to obtain factor matrices. The target locations are then able to be determined from the latent DOA parameters in these factor matrices. The proposed method neither requires prior knowledge, nor assumes orthogonality between probing signals. In addition, by exploiting the coupling, different receiving array outputs are jointly processed, yielding improved performance than uncoupled BTD based methods. Simulation results are provided to illustrate the performance of the proposed method.

AB - We propose a tensorial method for target localization based on multi-static-multi-pulse MIMO radar, which consists of multiple widely separated transmitting and receiving arrays. We show how a set of tensors, which admits a coupled multilinear (ML) rank-(L, L, 1) block term decomposition (BTD), can be constructed from the output signals of different receiving arrays. As such, we compute the coupled ML rank-(L, L, 1) BTD of these tensors to obtain factor matrices. The target locations are then able to be determined from the latent DOA parameters in these factor matrices. The proposed method neither requires prior knowledge, nor assumes orthogonality between probing signals. In addition, by exploiting the coupling, different receiving array outputs are jointly processed, yielding improved performance than uncoupled BTD based methods. Simulation results are provided to illustrate the performance of the proposed method.

KW - Block Term Decomposition

KW - Direction of arrival of arrival

KW - MIMO radar

KW - Multilinear rank

KW - Tensor

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U2 - 10.1007/978-3-030-22808-8_56

DO - 10.1007/978-3-030-22808-8_56

M3 - Conference contribution

AN - SCOPUS:85068607804

SN - 9783030228071

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

SP - 565

EP - 574

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 -

Yang JX, Gong XF, Li H, Xu YG, Liu ZW. Using Coupled Multilinear Rank-(L, L, 1) Block Term Decomposition in Multi-Static-Multi-Pulse MIMO Radar to Localize Targets. 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. 565-574. (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_56

Using Coupled Multilinear Rank-(L, L, 1) Block Term Decomposition in Multi-Static-Multi-Pulse MIMO Radar to Localize Targets

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