基于互易点学习的LPI信号开集识别

doi:10.12305/j.issn.1001-506X.2022.09.07

Abstract

Abstract:

The existing method of low probability of intercept (LPI) radar signal recognition using time-frequency image combined with deep learning model will fail in the open-set scenario. A method of radar signal open-set recognition based on reciprocal point learning (RPL) and threshold judgment is proposed to solve this problem. The embedding space is optimized by RPL, so the signals of known classes and unknown classes are distributed differently in it. Finally, an appropriate threshold is determined for open-set recognition. According to the characteristics of time-frequency image, the attention mechanism module is added in feature extraction network to make it pay more attention to the effective part of energy-intensive. Experimental results show that the proposed method has good adaptability in open electromagnetic environment.

Key words: low probability of intercept (LPI) signal, open-set recognition, deep learning, reciprocal point learning (RPL), attention mechanism

CLC Number:

TN971.1

Xiao HAN, Shiwen CHEN, Meng CHEN, Jincheng YANG. Open-set recognition of LPI radar signal based on reciprocal point learning[J]. Systems Engineering and Electronics, 2022, 44(9): 2752-2759.

Figures/Tables 12

Fig.1

Fig.2

Fig.3

Fig.4

Table 1

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Table 2

Fig.10

References 31

1	LUNDEN J , KOIVUNEN V . Automatic radar waveform recognition[J]. IEEE Journal of Selected Topics in Signal Processing, 2007, 1 (1): 124- 136. doi: 10.1109/JSTSP.2007.897055
2	RIGLING B D, ROUSH C. ACF-based classification of phase modulated waveforms[C]//Proc. of the IEEE Radar Conference, 2010: 287-291.
3	WANG C, WANG J, ZHANG X D. Automatic radar waveform recognition based on time-frequency analysis and convolutional neural network[C]//Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing, 2017: 2437-2441.
4	ZHANG M , DIAO M , GUO L M . Convolutional neural networks for automatic cognitive radio waveform recognition[J]. IEEE Access, 2017, 5, 11074- 11082. doi: 10.1109/ACCESS.2017.2716191
5	GUO Q , YU X , RUAN G Q . LPI radar waveform recognition based on deep convolutional neural network transfer learning[J]. Symmetry, 2019, 11 (4): 540. doi: 10.3390/sym11040540
6	NI X , WANG H L , ZHU Y , et al. Multi-resolution fusion convolutional neural networks for intrapulse modulation LPI radar waveforms recognition[J]. IEICE Trans.on Communications, 2020, E103-B (12): 1470- 1476. doi: 10.1587/transcom.2019EBP3262
7	秦鑫, 黄洁, 查雄, 等. 基于扩张残差网络的雷达辐射源信号识别[J]. 电子学报, 2020, 48 (3): 456- 462. doi: 10.3969/j.issn.0372-2112.2020.03.006
	QIN X , HUANG J , ZHA X , et al. Radar emitter signal recognition based on dilated residual network[J]. Acta Electronica Sinica, 2020, 48 (3): 456- 462. doi: 10.3969/j.issn.0372-2112.2020.03.006
8	肖易寒, 王亮, 郭玉霞. 基于去噪卷积神经网络的雷达信号调制类型识别[J]. 电子与信息学报, 2021, 43 (8): 2300- 2307.
	XIAO Y H , WANG L , GUO Y X , et al. Radar signal modulation type recognition based on denoising convolutional neural network[J]. Journal of Electronics & Information Technology, 2021, 43 (8): 2300- 2307.
9	SCHEIRER W J , JAIN L P , BOULT T E . Probability models for open set recognition[J]. IEEE Trans.on Pattern Analysis and Machine Intelligence, 2014, 36 (11): 2317- 2324. doi: 10.1109/TPAMI.2014.2321392
10	JAIN L P, SCHEIRER W J, BOULT T E. Multi-class open set recognition using probability of inclusion[C]//Proc. of the 13th European Conference on Computer Vision, 2014: 393-409.
11	RUDD E M , JAIN L P , SCHEIRER W J , et al. The extreme value machine[J]. IEEE Trans.on Pattern Analysis and Machine Intelligence, 2018, 40 (3): 762- 768. doi: 10.1109/TPAMI.2017.2707495
12	BENDALE A, BOULT T E. Towards open set deep networks[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 1563-1572.
13	OZA P, PATEL V M. C2AE: class conditioned auto-encoder for open-set recognition[C]//Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 2302-2311.
14	YANG H M , ZHANG X Y , YIN F , et al. Convolutional prototype network for open set recognition[J]. IEEE Trans.on Pattern Analysis and Machine Intelligence, 2020, 2358- 2370.
15	GE Z, DEMYANOV S, GARNAVI R. Generative OpenMax for multi-class open set classification[C]//Proc. of the British Machine Vision Conference, 2017.
16	NEAL L, OLSON M, FERN X, et al. Open set learning with counterfactual images[C]//Proc. of the 15th European Conference on Computer Vision, 2018: 620-635.
17	YUE Z Q, WANG T, SUN Q R, et al. Counterfactual zero-shot and open-set visual recognition[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2021.
18	PAVY A M , RIGLING B D . SV-means: a fast SVM-based level set estimator for phase-modulated radar waveform classification[J]. IEEE Journal of Selected Topics in Signal Processing, 2018, 12 (1): 191- 201. doi: 10.1109/JSTSP.2018.2797798
19	PAVY A M, RIGLING B D. Phase modulated radar waveform classification using quantile one-class SVMs[C]//Proc. of the IEEE Radar Conference, 2015: 745-750.
20	郝云飞, 刘章孟, 郭福成, 等. 基于生成对抗网络的信号调制方式的开集识别[J]. 系统工程与电子技术, 2019, 41 (11): 2619- 2624.
	HAO Y F , LIU Z M , GUO F C , et al. Open-set recognition of signal modulation based on generative adversarial networks[J]. Systems Engineering and Electronics, 2019, 41 (11): 2619- 2624.
21	丁嘉辉, 汤建龙, 于正洋. 轻量化的增量式集成学习算法设计[J]. 系统工程与电子技术, 2021, 43 (4): 861- 867.
	DING J H , TANG J L , YU Z Y . Design of lightweight incremental ensemble learning algorithm[J]. Systems Engineering and Electronics, 2021, 43 (4): 861- 867.
22	CHEN G Y, QIAO L M, SHI Y M, et al. Learning open set network with discriminative reciprocal points[C]//Proc. of the 16th European Conference on Computer Vision, 2020: 507-522.
23	CHEN G Y, PENG P X, WANG X Q, et al. Adversarial reciprocal points learning for open set recognition[EB/OL]. [2021-09-20]. https://arxiv.org/abs/2103.00953v1.
24	XU K, BA J L, KIROS R, et al. Show, attend and tell: neural image caption generation with visual attention[C]//Proc. of the 32nd International Conference on Machine Learning, 2015: 2048-2057.
25	WOO S, PARK J, LEE J Y. CBAM: convolutional block attention module[C]//Proc. of the 15th European Conference on Computer Vision, 2018: 3-19.
26	SNELL J. Prototypical networks for few-shot learning[C]//Proc. of the 31st International Conference on Neural Information Processing Systems, 2017: 4080-4090.
27	HEIN M, ANDRIUSHCHENKO M, BITTERWOLF J, et al. Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem[C]//Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 41-50.
28	PACE P E . Detecting and classifying low probability of intercept radar[J]. IEEE Aerospace and Electronic Systems Magaizne, 2004, 19 (5): 42- 44. doi: 10.1109/MAES.2004.1301226
29	KONG S H , KIM M , HOANG L M , et al. Automatic LPI radar waveform recognition using CNN[J]. IEEE Access, 2018, 6, 4207- 4219. doi: 10.1109/ACCESS.2017.2788942
30	SELVARAJU R R, COGSWELL M, DAS A, et al. Grad-CAM: visual explanations from deep networks via gradient-based localization[C]//Proc. of the IEEE International Conference on Computer Vision, 2017: 618-626.
31	SCHEIRER W J , DE REZENDE ROCHA A , SAPKOTA A , et al. Toward open set recognition[J]. IEEE Trans.on Pattern Analysis and Machine Intelligence, 2013, 25 (7): 1757- 1772.

调制类型	主要参数	值
LFM	调制带宽	U(f_s/20, f_s/16)
BPSK	巴克码位数	{7, 11, 13}
Costas	频率序列	{[3, 2, 6, 4, 5, 1], [5, 4, 6, 2, 3, 1], [2, 4, 8, 5, 10, 9, 7, 3, 6, 1]}
Frank, P1	步进频率	{6, 7, 8}
P2	步进频率	{6, 8}
P3, P4	步进频率	{6, 7, 8}
T1, T2	序列段数	{4, 5, 6}
T3, T4	调制带宽	U(f_s/20, f_s/10)
T3, T4	序列段数	{4, 5, 6}

调制类型	主要参数	值
LFM/BPSK	带宽	U(f_s/20, f_s/10)
LFM/BPSK	巴克码位数	{7, 13}
LFM/FSK	带宽	U(f_s/40, f_s/30)
	跳频数	{3, 4}
	跳频频率	U(f_s/60, f_s/40)
BPSK/FSK	巴克码位数	{5, 7}
	跳频数	{3, 4, 5}
	跳频频率	U(f_s/6, f_s/48)
NLFM	带宽	U(0.14f_s, 0.3f_s)

[1]	Limin ZHANG, Kaiwen TAN, Wenjun YAN, Yuyuan ZHANG. Radar emitter recognition based on multi-level jumper residual network [J]. Systems Engineering and Electronics, 2022, 44(7): 2148-2156.
[2]	Guodong JIN, Yuanliang XUE, Lining TAN, Jiankun XU. Advances in object tracking algorithm based on siamese network [J]. Systems Engineering and Electronics, 2022, 44(6): 1805-1822.
[3]	Xiaofeng ZHAO, Yebin XU, Fei WU, Jiahui NIU, Wei CAI, Zhili ZHANG. Ground infrared target detection method based on global sensing mechanism [J]. Systems Engineering and Electronics, 2022, 44(5): 1461-1467.
[4]	Pingliang XU, Yaqi CUI, Wei XIONG, Zhenyu XIONG, Xiangqi GU. Generative track segment consecutive association method [J]. Systems Engineering and Electronics, 2022, 44(5): 1543-1552.
[5]	Hong ZOU, Chenyang BAI, Peng HE, Yaping CUI, Ruyan WANG, Dapeng WU. Edge service placement strategy based on distributed deep learning [J]. Systems Engineering and Electronics, 2022, 44(5): 1728-1737.
[6]	Dong CHEN, Yanwei JU. Ship object detection SAR images based on semantic segmentation [J]. Systems Engineering and Electronics, 2022, 44(4): 1195-1201.
[7]	Jingming SUN, Shengkang YU, Jun SUN. Pose sensitivity analysis of HRRP recognition based on deep learning [J]. Systems Engineering and Electronics, 2022, 44(3): 802-807.
[8]	Tao WU, Lunwen WANG, Jingcheng ZHU. Camouflage image segmentation based on transfer learning and attention mechanism [J]. Systems Engineering and Electronics, 2022, 44(2): 376-384.
[9]	Yunxiang YAO, Ying CHEN. Target tracking network based on dual-modal interactive fusion under attention mechanism [J]. Systems Engineering and Electronics, 2022, 44(2): 410-419.
[10]	Tao JIN, Xiaofeng WANG, Runlan TIAN, Xindong ZHANG. Rapid recognition method of radar emitter based on improved 1DCNN+TCN [J]. Systems Engineering and Electronics, 2022, 44(2): 463-469.
[11]	Yutang MA, Peng SUN, Jieyong ZHANG, Peng WANG, Yunfei YAN, Liang ZHAO. Air group intention recognition method under imbalance samples [J]. Systems Engineering and Electronics, 2022, 44(12): 3747-3755.
[12]	Yiqiang TANG, Xiaopeng YANG, Shengming ZHU. Low-orbit satellite channel prediction algorithm based on the hybrid CNN-BiLSTM using attention mechanism [J]. Systems Engineering and Electronics, 2022, 44(12): 3863-3870.
[13]	Yongxing GAO, Xudong WANG, Ling WANG, Daiyin ZHU, Jun GUO, Fanwang MENG. Weather signal detection for dual polarization weather radar based on RCNN [J]. Systems Engineering and Electronics, 2022, 44(11): 3380-3387.
[14]	Lingzhi QU, Junan YANG, Hui LIU, Keju HUANG. Method for individual identification of communication radiation source embedded in attention mechanism [J]. Systems Engineering and Electronics, 2022, 44(1): 20-27.
[15]	Yiheng ZHOU, Jun YANG, Saiqiang XIA, Mingjiu LYU. Estimation method of micro-motion parameters for rotor targets under flashing [J]. Systems Engineering and Electronics, 2022, 44(1): 54-63.

Open-set recognition of LPI radar signal based on reciprocal point learning

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 12

References 31

Related Articles 15

Recommended Articles

Metrics

Comments