Abstract
This paper proposes a multi-view polarization high-resolution range profile (HRRP) target recognition method based on convolutional neural network (CNN-based MVPHRRP), which combines high-resolution technology with polarization technology to extract radar signal features. Using the feature layer fusion method, the intensity of scattering centers, the ratios of odd and even scattering extracted by Pauli decomposition constitute a three-dimensional feature tensor. On the basis of retaining the time-domain distribution characteristics, the multi-polarization characteristics and the target’s structural composition are fitted. Then we build a CNN for radar target recognition. The simulation results show that the use of CNN-based MVPHRRP for radar target recognition has a good effect, and the classification accuracy is less affected by the signal-to-noise ratio (SNR). Increasing the number of multi-views plays a positive role in improving the recognition performance, and the introduction of multi-view multi-polarization information can effectively increase the average recognition probability of target recognition.
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Acknowledgments
This work was supported by the National Key R&D Program of China under Grant No. 2016YFB1200100, and the National Nature Science Foundation of China under Grant No. 91638301.
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Ma, M., Liu, K., Luo, X., Zhang, T. (2021). Multi-view Polarization HRRP Target Recognition Based on Convolutional Neural Network. In: Li, B., Li, C., Yang, M., Yan, Z., Zheng, J. (eds) IoT as a Service. IoTaaS 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 346. Springer, Cham. https://doi.org/10.1007/978-3-030-67514-1_56
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DOI: https://doi.org/10.1007/978-3-030-67514-1_56
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