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Wireless Networks
Published in: Wireless Networks 1/2023

01-09-2022 | Original Paper

The recognition of multi-components signals based on semantic segmentation

Authors: Changbo Hou, Dingyi Fu, Lijie Hua, Yun Lin, Guowei Liu, Zhichao Zhou

Published in: Wireless Networks | Issue 1/2023

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Abstract

The separation and recognition of radar signals are crucial in a complex electromagnetic environment, especially multi-component radar signals. However, most existing algorithms can only recognize dual-component signals. An algorithm based on semantic segmentation is proposed to separate the signal in the time-frequency domain and classify multi-component radar signals. An improved Cohen class time-frequency distribution (CTFD) is used to represent the one-dimensional signals as time-frequency images (TFIs). A convolutional denoising autoencoder (CDAE) is established to filter the TFIs. Three semantic segmentation networks are used, a fully convolutional neural network (FCN-8s), U-Net, and DeepLab V3+. The method can separate and recognize signals simultaneously and is applied to aliased signals composed of 1-4 components. The simulation results show that the proposed method provides excellent performance for separating and recognizing multi-component signals. At a signal-to-noise ratio (SNR) of 0 dB, the accuracies of the aliased radar signals with 1-4 components are 100%, 100%, 96.67%, and 93.75%, respectively. The separation and recognition algorithm can be adapted to other signal modulation types.
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Literature
1.
Yang, C., Feng, L., Zhang, H., He, S., & Shi, Z. (2018). A novel data fusion algorithm to combat false data injection attacks in networked radar systems. IEEE Transactions on Signal and Information Processing over Networks, 4(1), 125–136.MathSciNetCrossRef
2.
Ding, G., Wu, Q., Zhang, L., Lin, Y., Tsiftsis, T. A., & Yao, Y.-D. (2018). An amateur drone surveillance system based on the cognitive internet of things. IEEE Communications Magazine, 56(1), 29–35.CrossRef
3.
Dong, G., & Kuang, G. (2015). Classification on the monogenic scale space: application to target recognition in SAR image. IEEE Transactions on Image Processing, 24(8), 2527–2539.MathSciNetCrossRefMATH
4.
Zheng, J., & Lv, Y. (2018). Likelihood-based automatic modulation classification in OFDM with index modulation. IEEE Transactions on Vehicular Technology, 67(9), 8192–8204.CrossRef
5.
Lin, Y., Tu, Y., & Dou, Z. (2020). An improved neural network pruning technology for automatic modulation classification in edge devices. IEEE Transactions on Vehicular Technology, 69(5), 5703–5706.CrossRef
6.
Wang, Y., Yang, J., Liu, M., & Gui, G. (2020). Lightamc: Lightweight automatic modulation classification via deep learning and compressive sensing. IEEE Transactions on Vehicular Technology, 69(3), 3491–3495.CrossRef
7.
Peng, S., Jiang, H., Wang, H., Alwageed, H., Zhou, Y., Sebdani, M. M., & Yao, Y.-D. (2018). Modulation classification based on signal constellation diagrams and deep learning. IEEE Transactions on Neural Networks and Learning Systems, 30(3), 718–727.CrossRef
8.
Lin, Y., Tu, Y., Dou, Z., Chen, L., & Mao, S. (2020). Contour stella image and deep learning for signal recognition in the physical layer. IEEE Transactions on Cognitive Communications and Networking, 7(1), 34–46.CrossRef
9.
Tu, Y., Lin, Y., Hou, C., & Mao, S. (2020). Complex-valued networks for automatic modulation classification. IEEE Transactions on Vehicular Technology, 69(9), 10085–10089.CrossRef
10.
Lin, Y., Zhu, X., Zheng, Z., Dou, Z., & Zhou, R. (2019). The individual identification method of wireless device based on dimensionality reduction and machine learning. The Journal of Supercomputing, 75(6), 3010–3027.CrossRef
11.
Tu, Y., Lin, Y., Wang, J., & Kim, J.-U. (2018). Semi-supervised learning with generative adversarial networks on digital signal modulation classification. Computers Materials Continua, 55(2), 243–254.
12.
Baltrušaitis, T., Ahuja, C., & Morency, L.-P. (2018). Multimodal machine learning: a survey and taxonomy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(2), 423–443.CrossRef
13.
Zhang, Z., Wang, C., Gan, C., Sun, S., & Wang, M. (2019). Automatic modulation classification using convolutional neural network with features fusion of SPWVD and BJD. IEEE Transactions on Signal and Information Processing over Networks, 5(3), 469–478.MathSciNetCrossRef
14.
Ni, X., Wang, H., Meng, F., Hu, J., & Tong, C. (2021). LPI radar waveform recognition based on multi-resolution deep feature fusion. IEEE Access, 9, 26138–26146.CrossRef
15.
Gao, J., Shen, L., & Gao, L. (2019). Modulation recognition for radar emitter signals based on convolutional neural network and fusion features. Transactions on Emerging Telecommunications Technologies, 30(12), e3612.CrossRef
16.
17.
Liu, Z., Li, L., Xu, H., & Li, H.: A method for recognition and classification for hybrid signals based on deep convolutional neural network. In: 2018 international conference on electronics technology (ICET), pp. 325–330 (2018). IEEE
18.
Qu, Z., Hou, C., Hou, C., & Wang, W. (2020). Radar signal intra-pulse modulation recognition based on convolutional neural network and deep q-learning network. IEEE Access, 8, 49125–49136.CrossRef
19.
Cheng, Y., Shao, J., Zhao, Y., Liu, S., & Malekian, R. (2019). An improved separation method of multi-components signal for sensing based on time-frequency representation. Review of Scientific Instruments, 90(6), 064901.CrossRef
20.
Chen, S., Dong, X., Xing, G., Peng, Z., Zhang, W., & Meng, G. (2017). Separation of overlapped non-stationary signals by ridge path regrouping and intrinsic chirp component decomposition. IEEE Sensors Journal, 17(18), 5994–6005.CrossRef
21.
Feng, Z., Liang, M., & Chu, F. (2013). Recent advances in time-frequency analysis methods for machinery fault diagnosis: a review with application examples. Mechanical Systems and Signal Processing, 38(1), 165–205.CrossRef
22.
Yu, J., & Zhou, X. (2020). One-dimensional residual convolutional autoencoder based feature learning for gearbox fault diagnosis. IEEE Transactions on Industrial Informatics, 16(10), 6347–6358.CrossRef
23.
Lundén, J., & Koivunen, V. (2007). Automatic radar waveform recognition. IEEE Journal of Selected Topics in Signal Processing, 1(1), 124–136.CrossRef
24.
Qu, Z., Mao, X., & Deng, Z. (2018). Radar signal intra-pulse modulation recognition based on convolutional neural network. IEEE Access, 6, 43874–43884.CrossRef
25.
Zhang, Y., Xiao, J., Peng, J., Ding, Y., Liu, J., Guo, Z., & Zong, X. (2018). Kernel wiener filtering model with low-rank approximation for image denoising. Information Sciences, 462, 402–416.MathSciNetCrossRefMATH
26.
Wu, Q., Li, Y., & Lin, Y. (2017). The application of nonlocal total variation in image denoising for mobile transmission. Multimedia Tools and Applications, 76(16), 17179–17191.CrossRef
27.
28.
Long, J., Shelhamer, E., & Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3431–3440 (2015)
29.
Ronneberger, O., Fischer, & P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention, pp. 234–241 (2015). Springer
30.
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European conference on computer vision (ECCV), pp. 801–818 (2018)
31.
Simonyan, K., & Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:​1409.​1556 (2014)
32.
Chen, L.-C., Papandreou, G., Schroff, F., & Adam, H.: Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:​1706.​05587 (2017)
33.
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp. 2980–2988 (2017)
34.
Pan, Z., Wang, S., Zhu, M., & Li, Y. (2020). Automatic waveform recognition of overlapping LPI radar signals based on multi-instance multi-label learning. IEEE Signal Processing Letters, 27, 1275–1279.CrossRef
35.
Si, W., Wan, C., & Zhang, C. (2021). Towards an accurate radar waveform recognition algorithm based on dense CNN. Multimedia Tools and Applications, 80(2), 1779–1792.CrossRef
36.
Lin, Y., Zhao, H., Ma, X., Tu, Y., & Wang, M. (2020). Adversarial attacks in modulation recognition with convolutional neural networks. IEEE Transactions on Reliability, 70(1), 389–401.CrossRef
37.
Sun, J., Wang, W., Kou, L., Lin, Y., Zhang, L., Da, Q., & Chen, L. (2020). A data authentication scheme for UAV ad hoc network communication. The Journal of Supercomputing, 76(6), 4041–4056.CrossRef
38.
Metadata
Title
The recognition of multi-components signals based on semantic segmentation
Authors
Changbo Hou
Dingyi Fu
Lijie Hua
Yun Lin
Guowei Liu
Zhichao Zhou
Publication date
01-09-2022
Publisher
Springer US
Published in
Wireless Networks / Issue 1/2023
Print ISSN: 1022-0038
Electronic ISSN: 1572-8196
DOI
https://doi.org/10.1007/s11276-022-03086-7

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