基于元学习的压制式干扰识别方法研究

A Blanket Jamming Recognition Method Based onMeta-learning

针对复杂电磁环境下干扰信号样本量少而难以识别的问题,提出基于元学习的干扰识别方法。首先计算干扰信号频率响应的Holder系数;然后将干扰信号的时频图经残差网络输出的特征向量与上述Holder系数进行多模态融合组合成新的多维特征向量;最后利用元学习将输出的多维特征向量拆分为编码向量和干扰信号时频图相关的协方差矩阵,计算干扰信号的预测值,通过计算实际值与预测值之间的最短欧氏距离进行干扰信号的识别分类。仿真结果表明,该干扰识别方法能够有效提高在小样本数据集1-shot和5-shot上的识别率。

In complex electromagnetic environments,it is difficult to identify jamming signals due to small sample size.To solve the problem,a jamming signal recognition method based on meta-learning is proposed.Firstly,the Holder coefficient of the frequency response of the jamming signal is calculated.Then,the time-frequency diagram of the jamming signal is input into the residual network,and the output is the eigenvector.Multi-modal fusion of the eigenvector with the above Holder coefficient is conducted to form a new multi-dimensional eigenvector.Finally,through meta-learning,the outputted multi-dimensional eigenvector is split into a coding vector and a covariance matrix related to the time-frequency diagram of the jamming signal to calculate the predicted value of the jamming signal,and the shortest Euclidean distance between the actual value and the predicted value is calculated to identify and classify the jamming signal.The simulation results show that the jamming signal recognition method can effectively improve the recognition rate on the 1-shot and 5-shot data sets of small sample size.