DDIRNet: robust radar emitter recognition via single domain generalization

doi:10.23919/JSEE.2025.000053

Journal of Systems Engineering and Electronics ›› 2025, Vol. 36 ›› Issue (2): 397-404.doi: 10.23919/JSEE.2025.000053

• DEFENCE ELECTRONICS TECHNOLOGY • Previous Articles

DDIRNet: robust radar emitter recognition via single domain generalization

Honglin WU(), Xueqiong LI(), Junjie HUANG(), Ruochun JIN(), Yuhua TANG()

Institute for Quantum Information & State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha 410073, China

Received:2023-08-07 Accepted:2023-08-07 Online:2025-04-18 Published:2025-05-20
Contact: Xueqiong LI E-mail:honglinwu@nudt.edu.cn;lixueqiong13@nudt.edu.cn;jjhuang@nudt.edu.cn;jinrc@nudt.edu.cn;yhtang62@163.com
About author:
WU Honglin was born in 1999. He received his M.S. degree from the College of Computer Science and Technology, National University of Defense Technology, Changsha, China, in 2021. He is working toward his Ph.D. degree in computer science and technology from National University of Defense Technology. His research interests include signal processing and deep learning. E-mail: honglinwu@nudt.edu.cn

LI Xueqiong was born in 1991. She received her Ph.D. degree from the College of Computer Science and Technology, National University of Defense Technology, Changsha, China, in 2020. She is currently an associate researcher with the College of Computer Science and Technology, National University of Defense Technology. Her current research interests include signal processing and machine learning. E-mail: lixueqiong13@nudt.edu.cn

HUANG Junjie was born in 1990. He received his Ph.D. degree from Imperial College London, London, U.K., in 2019. He is currently a lecturer with the College of Computer Science and Technology, National University of Defence Technology, Changsha, China. During 2019–2021, he was a Postdoc with Communications and Signal Processing Group, Electrical and Electronic Engineering Department, ICL, London. His current research interests include explainable deep learning and computer vision. E-mail: jjhuang@nudt.edu.cn

JIN Ruochun was born in 1993. He received his Ph.D. degree from the College of Computer Science and Technology, National University of Defense Technology, Changsha, China, in 2021. He is currently an assistant researcher with the College of Computer Science and Technology, National University of Defense Technology. His current research interests include databases, data quality, and data science. E-mail: jinrc@nudt.edu.cn

TANG Yuhua was born in 1962. She received her M.S degree from College of Computer Scienceand Technology, National University of Defense Technology, Changsha, China, in 1986. She is currently a professor with the Institute for Quantum Information & State Key Laboratory of High Performance Computing, College of Computer Science and Technology, National University of Defense Technology.Her research interests include supercomputer architecture and core router design E-mail: yhtang62@163.com
Supported by:
This work was supported by the National Natural Science Foundation of China (62101575), the Research Project of NUDT (ZK22-57), and the Self-directed Project of State Key Laboratory of High Performance Computing (202101-16).

Abstract

Abstract:

Automatically recognizing radar emitters from complex electromagnetic environments is important but non-trivial. Moreover, the changing electromagnetic environment results in inconsistent signal distribution in the real world, which makes the existing approaches perform poorly for recognition tasks in different scenes. In this paper, we propose a domain generalization framework is proposed to improve the adaptability of radar emitter signal recognition in changing environments. Specifically, we propose an end-to-end denoising based domain-invariant radar emitter recognition network (DDIRNet) consisting of a denoising model and a domain invariant representation learning model (IRLM), which mutually benefit from each other. For the signal denoising model, a loss function is proposed to match the feature of the radar signals and guarantee the effectiveness of the model. For the domain invariant representation learning model, contrastive learning is introduced to learn the cross-domain feature by aligning the source and unseen domain distribution. Moreover, we design a data augmentation method that improves the diversity of signal data for training. Extensive experiments on classification have shown that DDIRNet achieves up to 6.4% improvement compared with the state-of-the-art radar emitter recognition methods. The proposed method provides a promising direction to solve the radar emitter signal recognition problem.

Key words: radar emitter recognition, domain generalization, denoising, contrastive learning, data augmentation

Honglin WU, Xueqiong LI, Junjie HUANG, Ruochun JIN, Yuhua TANG. DDIRNet: robust radar emitter recognition via single domain generalization[J]. Journal of Systems Engineering and Electronics, 2025, 36(2): 397-404.

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References 30

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Dataset	$ \rho_r $	$ \rho_m $	$ \rho_n $
$ P_{{\mathrm{train}}} $	0.05	0.2	0.4
$ P_1 $	0.02	0.05	0.2
$ P_2 $	0.05	0.2	0.4
$ P_3 $	0.05	0.3	0.6
$ P_4 $	0.1	0.5	0.8

Method	$ P_1 $	$ P_2 $	$ P_3 $	$ P_4 $	Average
A-RNN [7]	86.2	93.7	75.1	50.5	76.4
ACSE [8]	86.6	93.1	74.9	49.0	75.9
TCN [9]	87.0	94.2	74.6	52.2	77.0
CombinedNet [10]	89.7	94.2	80.4	60.5	81.2
d-SNE [17]	88.9	93.1	78.6	59.7	80.1
MADA [18]	89.5	93.6	81.6	61.4	81.6
MetaCNN [19]	90.1	93.1	82.9	64.7	82.7
DDIRNet	92.9	93.9	89.1	74.5	87.6

Method	CST	JIT	SLD	WOB	D&S	STG
A-RNN [7]	40.4	40.6	59.3	57.7	53.0	50.8
ACSE [8]	40.0	38.6	53.4	58.6	54.5	49.0
TCN [9]	46.4	48.3	56.3	54.9	58.5	51.6
CombinedNet [10]	53.1	57.6	65.2	67.0	62.7	59.8
d-SNE [17]	58.5	56.1	64.6	62.0	59.2	59.2
MADA [18]	56.6	55.9	65.3	70.7	62.8	60.8
MetaCNN [19]	59.9	58.4	69.2	74.8	66.6	63.2
DDIRNet	71.0	61.7	80.5	84.4	83.1	72.9

Method	STG1	STG2	STG3	STG4	STG5
A-RNN [7]	52.8	51.1	45.1	49.1	56.1
ACSE [8]	50.4	53.2	49.5	47.9	45.1
TCN [9]	56.3	53.6	47.0	47.0	54.8
CombinedNet [10]	65.5	54.7	53.6	58.9	66.7
d-SNE [17]	63.5	61.7	53.2	56.2	62.8
MADA [18]	62.8	60.9	61.7	57.2	63.4
MetaCNN [19]	65.9	62.4	64.5	59.8	65.1
DDIRNet	78.3	75.1	66.8	69.9	74.4

$ D $	$ P $	$ P_1 $	$ P_2 $	$ P_3 $	$ P_4 $	Average
×	×	87.2	92.3	83.3	67.8	82.7
√	×	92.7	93.1	85.5	71.0	85.6
×	√	91.1	94.0	87.4	70.3	85.7
√	√	92.9	93.9	89.1	74.5	87.6

DDIRNet: robust radar emitter recognition via single domain generalization

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