Factorized discriminative conditional variational auto-encoder for radar HRRP target recognition.

Highlights • A method for radar HRRP target recognition is proposed based on Conditional VAE. • The model is regularized through the average HRRP for robust representations. • The label information is introduced by a three-way weight conditional model. • Weight factorization is utilized to effectively reduce model parameters. • Our method performs well under the low SNR and small amount of training data. Abstract In this paper, we propose a conditional generative model for radar high resolution range profile (HRRP) target recognition to learn the discriminative representations and sufficiently encode the observed feature variability by taking the multi-layer perception (MLP) as the sufficient statistics of posterior approximation distribution, thus offering the potential to improve the overall recognition performance. Considering the target-aspect sensitivity of HRRP, the model is regularized through reconstructing the average profiles. Then we introduce three-way weight tensors for MLPs to capture the multiplicative interactions between label information and HRRP samples, which are then further factorized to effectively reduce model parameters. The extensive experimental results on the measured HRRP data demonstrate that the proposed algorithm achieves the promising target recognition and reconstruction performance. [ABSTRACT FROM AUTHOR]