基于注意力图剪枝的辐射源信号识别方法

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中图分类号：TN957.51 文献标志码：A DOI：10.12305/j.issn.1001-506X.2025.04.05

Abstract： In order to solve the problems of redundant parameters and large amount of computation in the neural network used for radiation source signal recognition，a method of radiation source signal recognition based on attention map pruning is proposed.The proposed method uses the product of the first-order gradient and feature activation values to measurethe effectiveness values of the benchmark network convolution kernels，and removes the convolution kernels with low efectivenessvalues.In order to avoid the serious degradation of signal recognition performance of subnetworks after pruning， the proposed method introduces attention map knowledge loss in the fine-tuning training stage and constructs a joint lossfunction to transfer the feature extraction capability of the benchmark network to the subnetwork.The experimental results show that after pruning the benchmark network，the signal recognition accuracy of the subnetwork decreases by only 0.07% ， the number of network parameters decreases by 48.7% ，and the amount of computation decreases by 80.1% ： These outcomes validate that the approach presented in this paper effectively accomplishes network lightweighting while maintaining the accuracy of signal recognition.

Keywords：radiation source signal recognition；model pruning；attention map； network complexity

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大大增加。（剩余14666字）