结合特征分布优化的辐射源类增量识别方法OA

The existing models for class incremental recognition of radiation source are affected by catastrophic forgetting,resulting in low recognition rates and imbalance between stability and plasticity.A radiation source class incremental recognition method based on classifier incremental learning and feature distribution optimization is proposed.The new class features are transformed by Gaussian distribution to improve the distribution and increase the recognition rate of the new classes.For the increasing of the recognition rate of the old classes,feature distribution similar to the old classes is generated by utilizing a small amount of inter-and intra-cluster distribution information.The stability and plasticity are eventually balanced through Gaussian distribution and the inter-and intra-cluster distribution.The average accuracy of the proposed method on a mobile phone radiation source dataset and a radio radiation source dataset reaches 96.38%and 94.02%,respectively,which indicates that this method can effectively improve the recognition rate of radiation source while balancing its stability and plasticity.