Multi-station Fusion Recognition of Ballistic Targets Based on Two-stage Attention Transformer
Multi-station ballistic target fusion recognition aims to enhance the recognition performance of ballistic targets by levera-ging the complementarity of radar station information from multiple radar stations.Traditional methods for multi-station ballistic tar-get recognition do not directly consider the inherent data characteristics between stations,typically overlooking inter-station data correlations during the decision-making process.This paper addresses the problem of high-speed flying target recognition in a multi-station context based on dynamic radar cross-section(RCS).A two-stage attention-based ballistic target multi-station fusion recog-nition approach is proposed.Firstly,a dimension segmentation module is added to the existing Transformer model to embed multi-station radar data into 2-dimensional vectors,preserving both intra-station temporal and inter-station correlation information.Sec-ondly,two-stage attention layers are incorporated to effectively capture intra-station temporal information and inter-station dimen-sional dependencies.Finally,fusion recognition experiments are conducted using simulated dynamic RCS data to simulate multi-station scenarios,demonstrating that the proposed approach can significantly enhance the recognition performance of ballistic targets under multi-station condition.
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