Target Recognition for Anti-Interference Radar Based on Adaptive Threshold Convolution Network
This paper proposes an adaptive threshold convolutional network(ATCN)for anti-interference radar target recognition based on HRRP data.The core module in ATCN is the adaptive threshold convolutional unit(ATCU),which enables the accurate and efficient feature extraction from HRRP data.In ATCU,an adaptive threshold function is employed as the activation function to automatically adjust the threshold for different signal-to-interference ratios.Multi-ple convolutional kernels of different scales are used to capture regional difference features in HRRP data.The channel attention mechanism and residual connection are introduced to optimize the network structure.Extensive experiments on anti-interference target recognition are conducted in this study.The experimental results demonstrate that compared with the three selected comparison networks,the proposed ATCN provides better average recognition rate and better in-dex stability under different interference types and signal-to-interference ratios.Furthermore,the ATCN network has fewer model parameters and floating-point operations,demonstrating its lightweight and efficient characteristics.
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