Unknown Signal Incremental Recognition Based on Multi-Flow ConvNeXt Network and Mahalanobis Distance Metric
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％.
万方数据
维普
