基于多流ConvNeXt网络和马氏距离度量的未知信号增量识别

Unknown Signal Incremental Recognition Based on Multi-Flow ConvNeXt Network and Mahalanobis Distance Metric

为解决现阶段基于深度学习网络的信号识别技术无法实现未知信号增量识别的问题,提出了基于多流ConvNeXt网络和马氏距离度量(MDM)相结合的未知信号增量识别方法.首先,利用改进的多流ConvNeXt网络提取信号的属性特征;其次,使用马氏距离度量判决方法进行未知信号检测进而实现已知信号和未知信号的二分类;最后,该方法根据不断增加的未知信号对模型的参数进行自动更新,使模型具备了自我进化的能力,进而可以识别出不断增加的新的未知信号类别,实现对未知信号的增量识别.仿真实验结果表明,该方法对未知信号的平均识别率达到97%以上.

In order to solve the problem that the signal recognition technology based on deep learning network cannot currently realize the incremental recognition of unknown signals,a method for incremental recognition of such unknown signals,based on the combination of the multi-flow ConvNeXt network and Mahalanobis distance metric(MDM)is proposed.First,the improved multi-flow ConvNeXt network is used to extract the attribute features of signals.Then,the MDM judgement method is used to detect unknown signals,and apply the binary classification for known and unknown signals.Finally,the parameters of the model is automatically updated according to the increasing number of unknown signals.In such way,the model has the ability of self-evolution,and it has the ability to recognize incrementally more types of unknown signals.The simulation results show that the average recognition rate of unknown signals is more than 97%.