Abstract
Maritime target detection, because of the difficulties in the extraction and recognition of target and clutter micro-motion characteristics, has always been one of the difficulties in radar target detection. In this paper, convolutional neural networks are used for the detection of maritime target micro-Doppler. Firstly, using the IPIX measured sea clutter and target signal data, the two-dimensional time-frequency signal dataset is built by time-frequency analysis. Two Deep CNN models, LeNet and GoogLeNet are trained and used for the detection of maritime targets, and their performances are compared. Then the method is tested under different sea states and polarization. The results show that the proposed method can achieve high detection probability under different circumstance, which provides a new approach for the detection of maritime targets.
This work was supported in part by the National Natural Science Foundation of China (61871391, U1633122, 61871392, 61531020), Project of Shandong Province Higher Educational Science and Technology Program (J17KB139), Young Elite Scientist Sponsorship Program of CAST.
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References
Luo, Y., Zhang, Q., Wang, G., Guan, H., Bai, Y.: Micro-motion signature extraction method for wideband radar based on complex image OMP decomposition. J. Radars 1(4), 361–369 (2012)
Darzikolaei, M.A., Ebrahimzade, A., Gholami, E.: Classification of radar clutters with artificial neural network. In: 2015 2nd International Conference on Knowledge-Based Engineering and Innovation, pp. 577–581 (2015)
Wagner, S.A.: SAR ATR by a combination of convolutional neural network and support vector machines. IEEE Trans. Aerosp. Electron. Syst. 52(6), 2861–2872 (2016)
Tian, Z., Zhan, R., Hu, J., Zhang, J.: SAR ATR based on convolutional neural network. J. Radars 5(3), 320–325 (2016)
Wang, J., Zheng, T., Lei, P., Wei, S.: Study on deep learning in radar. J. Radars 7(4), 395–411 (2018)
Wang, X., Zhou, Y., Zhou, D., Chen, Z., Tian, Y.: Research on low probability of intercept radar signal recognition using deep belief network and bispectra diagonal slice. J. Electron. Inf. Technol. 38(11), 2972–2976 (2016)
Chen, X., Guan, J., He, Y.: Applications and prospect of micro-motion theory in the detection of sea surface target. J. Radars 2(1), 123–134 (2013)
Chen, X., Guan, J., Bao, Z., He, Y.: Detection and extraction of target with micro-motion in spiky sea clutter via short-time fractional Fourier transform. IEEE Trans. Geosci. Remote Sens. 52(2), 1002–1018 (2014)
Chen, X., Guan, J., Li, X., He, Y.: Effective coherent integration method for marine target with micromotion via phase differentiation and radon-Lv’s distribution. IET Radar Sonar Navig. (Spec. Issue: Micro-Doppler) 9(9), 1284–1295 (2015)
Xu, B., Chen, B., Liu, H., Jin, L.: Attention-based recurrent neural network model for radar high-resolution range profile target recognition. J. Electron. Inf. Technol. 38(12), 2988–2995 (2016)
Xu, Z., Wang, R., Li, N., Zhang, H., Zhang, L.: A novel approach to change detection in SAR images with CNN classification. J. Radars 6(5), 483–491 (2017)
Xu, F., Wang, H., Jin, Y.: Deep learning as applied in SAR target recognition and terrain classification. J. Radars 6, 1–13 (2017)
Yan, L., Sun, P., Yi, L., Han, N., Tang, J.: Modeling of compound-gaussian sea clutter based on an inverse gaussian distribution. J. Radars 2(4), 461–465 (2013)
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Su, N., Chen, X., Guan, J., Li, Y. (2019). Deep CNN-Based Radar Detection for Real Maritime Target Under Different Sea States and Polarizations. In: Sun, F., Liu, H., Hu, D. (eds) Cognitive Systems and Signal Processing. ICCSIP 2018. Communications in Computer and Information Science, vol 1006. Springer, Singapore. https://doi.org/10.1007/978-981-13-7986-4_29
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DOI: https://doi.org/10.1007/978-981-13-7986-4_29
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