Continuous IFF Response Signal Recognition Technology Based on Capsule Network

Yifan Jiang; Zhutian Yang; Chao Bo; Dongjia Zhang

doi:10.1007/978-3-030-90196-7_39

Continuous IFF Response Signal Recognition Technology Based on Capsule Network

Yifan Jiang
, Zhutian Yang^*
, Chao Bo
, Dongjia Zhang

^*Corresponding author for this work

School of Electronics and Information Engineering, Harbin Institute of Technology
Nanjing Institute of Electronic Equipment
China Aerospace Science and Industry Corporation

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

6 Scopus citations

Abstract

Identification of friend or foe (IFF) system has become an indispensable part in modern war. In order to meet the needs of air target situation control in rapid response operations, it is urgent to find an intelligent IFF signal recognition method. Aiming at the problems of low recognition accuracy and high false alarm rate of continuous IFF signal of single channel multiple air maneuvering targets in low SNR environment, a signal pattern recognition method of continuous IFF signal based on capsule network and attention mechanism in complex environment is proposed by improving signal data set and capsule network model structure. Using the good generalization ability and strong feature interpretation ability of attention mechanism provided by capsule network, the improved method has a certain degree of improvement in the pattern recognition ability of simulated complex signals compared with traditional frame detection method and multilayer convolutional neural network. At the same time, the false alarm rate and the missed alarm rate are significantly improved, which can meet the actual detection requirements.

Original language	English
Title of host publication	Artificial Intelligence for Communications and Networks - 3rd EAI International Conference, AICON 2021, Proceedings
Editors	Xianbin Wang, Kai-Kit Wong, Shanji Chen, Mingqian Liu
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	455-468
Number of pages	14
ISBN (Print)	9783030901950
DOIs	https://doi.org/10.1007/978-3-030-90196-7_39
State	Published - 2021
Externally published	Yes
Event	3rd EAI International Conference on Artificial Intelligence for Communications and Networks, AICON 2021 - Virtual, Online Duration: 23 Oct 2021 → 24 Oct 2021

Publication series

Name	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume	396 LNICST
ISSN (Print)	1867-8211
ISSN (Electronic)	1867-822X

Conference

Conference	3rd EAI International Conference on Artificial Intelligence for Communications and Networks, AICON 2021
City	Virtual, Online
Period	23/10/21 → 24/10/21

Keywords

Attention mechanism
Co-frequency interference
DM-CapsNet
Identification of friend or foe (IFF)

Access to Document

10.1007/978-3-030-90196-7_39

Cite this

Jiang, Y., Yang, Z., Bo, C., & Zhang, D. (2021). Continuous IFF Response Signal Recognition Technology Based on Capsule Network. In X. Wang, K.-K. Wong, S. Chen, & M. Liu (Eds.), Artificial Intelligence for Communications and Networks - 3rd EAI International Conference, AICON 2021, Proceedings (pp. 455-468). (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 396 LNICST). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-90196-7_39

Jiang, Yifan ; Yang, Zhutian ; Bo, Chao et al. / Continuous IFF Response Signal Recognition Technology Based on Capsule Network. Artificial Intelligence for Communications and Networks - 3rd EAI International Conference, AICON 2021, Proceedings. editor / Xianbin Wang ; Kai-Kit Wong ; Shanji Chen ; Mingqian Liu. Springer Science and Business Media Deutschland GmbH, 2021. pp. 455-468 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST).

@inproceedings{1c36f8bb1ccd4d6b923664e9ab8239a2,

title = "Continuous IFF Response Signal Recognition Technology Based on Capsule Network",

abstract = "Identification of friend or foe (IFF) system has become an indispensable part in modern war. In order to meet the needs of air target situation control in rapid response operations, it is urgent to find an intelligent IFF signal recognition method. Aiming at the problems of low recognition accuracy and high false alarm rate of continuous IFF signal of single channel multiple air maneuvering targets in low SNR environment, a signal pattern recognition method of continuous IFF signal based on capsule network and attention mechanism in complex environment is proposed by improving signal data set and capsule network model structure. Using the good generalization ability and strong feature interpretation ability of attention mechanism provided by capsule network, the improved method has a certain degree of improvement in the pattern recognition ability of simulated complex signals compared with traditional frame detection method and multilayer convolutional neural network. At the same time, the false alarm rate and the missed alarm rate are significantly improved, which can meet the actual detection requirements.",

keywords = "Attention mechanism, Co-frequency interference, DM-CapsNet, Identification of friend or foe (IFF)",

author = "Yifan Jiang and Zhutian Yang and Chao Bo and Dongjia Zhang",

note = "Publisher Copyright: {\textcopyright} 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.; 3rd EAI International Conference on Artificial Intelligence for Communications and Networks, AICON 2021 ; Conference date: 23-10-2021 Through 24-10-2021",

year = "2021",

doi = "10.1007/978-3-030-90196-7\_39",

language = "英语",

isbn = "9783030901950",

series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "455--468",

editor = "Xianbin Wang and Kai-Kit Wong and Shanji Chen and Mingqian Liu",

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Jiang, Y, Yang, Z, Bo, C & Zhang, D 2021, Continuous IFF Response Signal Recognition Technology Based on Capsule Network. in X Wang, K-K Wong, S Chen & M Liu (eds), Artificial Intelligence for Communications and Networks - 3rd EAI International Conference, AICON 2021, Proceedings. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 396 LNICST, Springer Science and Business Media Deutschland GmbH, pp. 455-468, 3rd EAI International Conference on Artificial Intelligence for Communications and Networks, AICON 2021, Virtual, Online, 23/10/21. https://doi.org/10.1007/978-3-030-90196-7_39

Continuous IFF Response Signal Recognition Technology Based on Capsule Network. / Jiang, Yifan; Yang, Zhutian; Bo, Chao et al.
Artificial Intelligence for Communications and Networks - 3rd EAI International Conference, AICON 2021, Proceedings. ed. / Xianbin Wang; Kai-Kit Wong; Shanji Chen; Mingqian Liu. Springer Science and Business Media Deutschland GmbH, 2021. p. 455-468 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 396 LNICST).

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

TY - GEN

T1 - Continuous IFF Response Signal Recognition Technology Based on Capsule Network

AU - Jiang, Yifan

AU - Yang, Zhutian

AU - Bo, Chao

AU - Zhang, Dongjia

PY - 2021

Y1 - 2021

N2 - Identification of friend or foe (IFF) system has become an indispensable part in modern war. In order to meet the needs of air target situation control in rapid response operations, it is urgent to find an intelligent IFF signal recognition method. Aiming at the problems of low recognition accuracy and high false alarm rate of continuous IFF signal of single channel multiple air maneuvering targets in low SNR environment, a signal pattern recognition method of continuous IFF signal based on capsule network and attention mechanism in complex environment is proposed by improving signal data set and capsule network model structure. Using the good generalization ability and strong feature interpretation ability of attention mechanism provided by capsule network, the improved method has a certain degree of improvement in the pattern recognition ability of simulated complex signals compared with traditional frame detection method and multilayer convolutional neural network. At the same time, the false alarm rate and the missed alarm rate are significantly improved, which can meet the actual detection requirements.

AB - Identification of friend or foe (IFF) system has become an indispensable part in modern war. In order to meet the needs of air target situation control in rapid response operations, it is urgent to find an intelligent IFF signal recognition method. Aiming at the problems of low recognition accuracy and high false alarm rate of continuous IFF signal of single channel multiple air maneuvering targets in low SNR environment, a signal pattern recognition method of continuous IFF signal based on capsule network and attention mechanism in complex environment is proposed by improving signal data set and capsule network model structure. Using the good generalization ability and strong feature interpretation ability of attention mechanism provided by capsule network, the improved method has a certain degree of improvement in the pattern recognition ability of simulated complex signals compared with traditional frame detection method and multilayer convolutional neural network. At the same time, the false alarm rate and the missed alarm rate are significantly improved, which can meet the actual detection requirements.

KW - Attention mechanism

KW - Co-frequency interference

KW - DM-CapsNet

KW - Identification of friend or foe (IFF)

UR - https://www.scopus.com/pages/publications/85119894125

U2 - 10.1007/978-3-030-90196-7_39

DO - 10.1007/978-3-030-90196-7_39

M3 - 会议稿件

AN - SCOPUS:85119894125

SN - 9783030901950

T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST

SP - 455

EP - 468

BT - Artificial Intelligence for Communications and Networks - 3rd EAI International Conference, AICON 2021, Proceedings

A2 - Wang, Xianbin

A2 - Wong, Kai-Kit

A2 - Chen, Shanji

A2 - Liu, Mingqian

PB - Springer Science and Business Media Deutschland GmbH

T2 - 3rd EAI International Conference on Artificial Intelligence for Communications and Networks, AICON 2021

Y2 - 23 October 2021 through 24 October 2021

ER -

Jiang Y, Yang Z, Bo C, Zhang D. Continuous IFF Response Signal Recognition Technology Based on Capsule Network. In Wang X, Wong KK, Chen S, Liu M, editors, Artificial Intelligence for Communications and Networks - 3rd EAI International Conference, AICON 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 455-468. (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST). doi: 10.1007/978-3-030-90196-7_39