| Citation: | LIU Q，SHANG S，QIAO T Z，et al. Ionospheric clutter suppression method based on improved TCN-Elman neural network[J]. Journal of Beijing University of Aeronautics and Astronautics，2025，51（9）：3203-3211 （in Chinese） doi: 10.13700/j.bh.1001-5965.2023.0429
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High-frequency surface wave radar is used in the field of offshore engineering worldwide because of its excellent sea surface target detection capability. One of the key elements to improve its target detection capability lies in the suppression of ionospheric clutter in the echo signal. In response, this paper proposed an ionospheric clutter prediction and suppression model (Mixatt-ITCN-Elman) based on bottleneck expansion convolution module improved temporal convolution (ITCN)-Elman neural network combined with hybrid attention mechanism. First, the ionospheric clutter time series was reconstructed in phase space and subjected to disordered normalization. The spatial features within the high-dimensional phase space were extracted by using ITCN, and the key spatial features were highlighted by combining the self-attention mechanism. Then, the spatial features were combined with the original time series and input into the Elman neural network. The spatial-temporal features of the sequences were highlighted by combining the attention mechanism. Finally, the spatial-temporal features combined with the Elman neural network output sequence were output to obtain the final prediction result. In comparison with Elman, TCN, Att-CNN-Elman, and TCN-Elman models, the proposed model has better prediction performance and stability, having high application value for the suppression of ionospheric clutter.
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