Memory-Based Neural Network for Radar HRRP Noncooperative Target Recognition

In this paper, we propose a Memory-Based Neural Network(MBNN) for Radar Automatic Target Recognition
(RATR) based on High Resolution Range Profile (HRRP) in
imbalanced case to learn how to find out the discriminative
representations and generalize the ability to barely appeared
target samples of some categories. Specifically, we utilize a
Convolutional Neural Network (CNN) to explore discriminative
features among HRRP samples and employ a memory module
to record misclassified samples or samples that are correctly
classified with low confidence into a external storage, we called
it buffer. Then we leverage a Long Short Term Memory (LSTM)
to merge the classified samples with some of the most similar
ones in the buffer to make the final decision. It is worth noting
that MBNN can be inserted as a plug-and-play module into any
discriminative methods. Effectiveness and efficiency are evaluated
on the measured data.