A Small Sample Air Combat Target Intent Recognition Method Based on Meta-Metric Learning
In order to achieve rapid and accurate recognition of combat intent in complex battlefield envi-ronments with fewer air combat data samples,a combat intent recognition model based on meta-metric learning framework is proposed.Regarding received small sample data during air combat intent recognition,a two-way gated recurrent unit network based on the air combat temporal data are developed to realize effec-tive feature extraction,and the attention mechanism is introduced for promoting the network to fully extract the temporal core features of the air combat data when facing the small sample data are merely available for obtaining the inter-class differences which finally achieve a fairly higher recognition accuracy and recogni-tion speed.Simulation results show that the model proposed in this paper has better accuracy and real-time performance for air combat target intent recognition,especially in the case of small sample data.
Air combat targetsIntent recognitionAttentional mechanismBi-directional gated recurrent cell networksMeta-metric learning
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