Abstract
Conventional radar emitter identification methods are faced with intensive noise and complex electromagnetic environment, which degrades the performance. Aiming at the feature extraction and identification at low signal-to-noise ratio (SNR), an improved algorithm is developed for the analysis and identification. First, the approach forms the time-frequency distribution and preprocesses the 2-dimensional image with whitening and normalization operations. Then, convolutional neural network (CNN) is introduced to generate high-level and abstract representations. Next, random vector functional link (RVFL) is trained to promote the fast feature learning. Finally, identification is implemented by picking out the maximum of RVFL output. 6 types of simulated emitters are used to validate the proposed algorithm. Experimental results show that the selected types of radar waveforms with additive Gaussian white noise can reach 85% accuracy when SNR up to −2 dB. Compared with the state-of-the-art techniques in feature extraction and identification, the proposed algorithm outperforms in the robustness and comprehensive performance.
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Zhou, Z., Zhang, J., Zhang, T. (2020). A Novel Algorithm of Radar Emitter Identification Based Convolutional Neural Network and Random Vector Functional-Link. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1075. Springer, Cham. https://doi.org/10.1007/978-3-030-32591-6_90
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DOI: https://doi.org/10.1007/978-3-030-32591-6_90
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