Type-Aspect Disentanglement Network for HRRP Target Recognition With Missing Aspects

High-resolution range profile (HRRP) is essential for high-throughput radar automatic target recognition. Due to the aspect sensitivity of HRRP, the recognition performance degrades when the training dataset misses some aspects. Inspired by disentangled representation learning (DRL), this letter proposes a type-aspect disentanglement network (TADN) to alleviate the above aspects missing problem. The proposed method disentangles HRRP into uncorrelated type and aspect representations, so that the type representation can be used to accurately recognize target without being affected by target aspects. Specifically, the proposed network acquires initial type and aspect representations by residual factorization. On this basis, mutual information (MI) minimization is used to constrain the independence of two representations from each other, and a reconstruction loss is employed to avoid information reduction during the decorrelation process. Additionally, target type and aspect labels are exploited to constrain their semantic meanings. Experimental results demonstrate that the proposed method achieves improved robustness in varying degrees of aspects missing cases.