Wideband Radar Target Recognition Based on Polarization Features and Double Layer K-LightGBM

Bowen Deng; Ping Lang; Xiongjun Fu; Jian Dong; Zhifeng Ma; Zongding Cui

doi:10.1109/Radar53847.2021.10027876

Wideband Radar Target Recognition Based on Polarization Features and Double Layer K-LightGBM

Bowen Deng, Ping Lang, Xiongjun Fu, Jian Dong, Zhifeng Ma^*, Zongding Cui

^*Corresponding author for this work

School of Integrated Circuits and Electronics

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

1 Citation (Scopus)

Abstract

To improve the shape-similar target recognition performance, a polarization features-based double layer K-LightGBM model is proposed in this paper. First, a target features dataset is built, which is based on polarization invariants and two kinds of polarization decompositions of targets. Then, a double layer K-LightGBM model is set up by improving StackNet with K LightGBM stacked. Finally, the dataset is fed into the proposed model for training and testing. The experimental results show that the proposed method in this paper has better performance in terms of generalization and denoising, compared to many existing state-of-the-art methods, including LightGBM.

Original language	English
Title of host publication	2021 CIE International Conference on Radar, Radar 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1293-1296
Number of pages	4
ISBN (Electronic)	9781665498142
DOIs	https://doi.org/10.1109/Radar53847.2021.10027876
Publication status	Published - 2021
Event	2021 CIE International Conference on Radar, Radar 2021 - Haikou, Hainan, China Duration: 15 Dec 2021 → 19 Dec 2021

Publication series

Name	Proceedings of the IEEE Radar Conference
Volume	2021-December
ISSN (Print)	1097-5764
ISSN (Electronic)	2375-5318

Conference

Conference	2021 CIE International Conference on Radar, Radar 2021
Country/Territory	China
City	Haikou, Hainan
Period	15/12/21 → 19/12/21

Keywords

LightGBM
StackNet
polarization decomposition
polarization invariants
radar target recognition

Access to Document

10.1109/Radar53847.2021.10027876

Cite this

Deng, B., Lang, P., Fu, X., Dong, J., Ma, Z., & Cui, Z. (2021). Wideband Radar Target Recognition Based on Polarization Features and Double Layer K-LightGBM. In 2021 CIE International Conference on Radar, Radar 2021 (pp. 1293-1296). (Proceedings of the IEEE Radar Conference; Vol. 2021-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/Radar53847.2021.10027876

@inproceedings{5e5f19e071054b5a9404530b0b7a8485,

title = "Wideband Radar Target Recognition Based on Polarization Features and Double Layer K-LightGBM",

abstract = "To improve the shape-similar target recognition performance, a polarization features-based double layer K-LightGBM model is proposed in this paper. First, a target features dataset is built, which is based on polarization invariants and two kinds of polarization decompositions of targets. Then, a double layer K-LightGBM model is set up by improving StackNet with K LightGBM stacked. Finally, the dataset is fed into the proposed model for training and testing. The experimental results show that the proposed method in this paper has better performance in terms of generalization and denoising, compared to many existing state-of-the-art methods, including LightGBM.",

keywords = "LightGBM, StackNet, polarization decomposition, polarization invariants, radar target recognition",

author = "Bowen Deng and Ping Lang and Xiongjun Fu and Jian Dong and Zhifeng Ma and Zongding Cui",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 CIE International Conference on Radar, Radar 2021 ; Conference date: 15-12-2021 Through 19-12-2021",

year = "2021",

doi = "10.1109/Radar53847.2021.10027876",

language = "English",

series = "Proceedings of the IEEE Radar Conference",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1293--1296",

booktitle = "2021 CIE International Conference on Radar, Radar 2021",

address = "United States",

}

Deng, B, Lang, P, Fu, X, Dong, J, Ma, Z & Cui, Z 2021, Wideband Radar Target Recognition Based on Polarization Features and Double Layer K-LightGBM. in 2021 CIE International Conference on Radar, Radar 2021. Proceedings of the IEEE Radar Conference, vol. 2021-December, Institute of Electrical and Electronics Engineers Inc., pp. 1293-1296, 2021 CIE International Conference on Radar, Radar 2021, Haikou, Hainan, China, 15/12/21. https://doi.org/10.1109/Radar53847.2021.10027876

Wideband Radar Target Recognition Based on Polarization Features and Double Layer K-LightGBM. / Deng, Bowen; Lang, Ping; Fu, Xiongjun et al.
2021 CIE International Conference on Radar, Radar 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 1293-1296 (Proceedings of the IEEE Radar Conference; Vol. 2021-December).

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

TY - GEN

T1 - Wideband Radar Target Recognition Based on Polarization Features and Double Layer K-LightGBM

AU - Deng, Bowen

AU - Lang, Ping

AU - Fu, Xiongjun

AU - Dong, Jian

AU - Ma, Zhifeng

AU - Cui, Zongding

PY - 2021

Y1 - 2021

N2 - To improve the shape-similar target recognition performance, a polarization features-based double layer K-LightGBM model is proposed in this paper. First, a target features dataset is built, which is based on polarization invariants and two kinds of polarization decompositions of targets. Then, a double layer K-LightGBM model is set up by improving StackNet with K LightGBM stacked. Finally, the dataset is fed into the proposed model for training and testing. The experimental results show that the proposed method in this paper has better performance in terms of generalization and denoising, compared to many existing state-of-the-art methods, including LightGBM.

AB - To improve the shape-similar target recognition performance, a polarization features-based double layer K-LightGBM model is proposed in this paper. First, a target features dataset is built, which is based on polarization invariants and two kinds of polarization decompositions of targets. Then, a double layer K-LightGBM model is set up by improving StackNet with K LightGBM stacked. Finally, the dataset is fed into the proposed model for training and testing. The experimental results show that the proposed method in this paper has better performance in terms of generalization and denoising, compared to many existing state-of-the-art methods, including LightGBM.

KW - LightGBM

KW - StackNet

KW - polarization decomposition

KW - polarization invariants

KW - radar target recognition

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DO - 10.1109/Radar53847.2021.10027876

M3 - Conference contribution

AN - SCOPUS:85181049718

T3 - Proceedings of the IEEE Radar Conference

SP - 1293

EP - 1296

BT - 2021 CIE International Conference on Radar, Radar 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 CIE International Conference on Radar, Radar 2021

Y2 - 15 December 2021 through 19 December 2021

ER -

Wideband Radar Target Recognition Based on Polarization Features and Double Layer K-LightGBM

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