Open set HRRP recognition with few samples based on multi-modality prototypical networks

Learning from a few high-resolution range profiles (HRRPs) with multi-modality property caused by sen-sitivity of the target aspect remains a challenge in radar HRRP-based target recognition. Despite recent advances in HRRP recognition based on deep neural networks (DNNs), thanks to their powerful expressive ability, the standard supervised deep learning paradigm does not offer a satisfactory solution for learning new classes rapidly from a few HRRPs. In this work, we employ ideas from metric learning based on dis-criminative neural features and from recent advances that augment neural networks with FiLM layers to adapt to multi-modality input data. We first define open set learning problem on HRRP recognition task. Then, we propose a multi-modality prototypical network (MMPN) to attack the problem. Our framework learns a modality-aware network that maps a few labelled HRRPs and unlabelled HRRPs to their labels via a well defined metric space with episodic-based meta-learning strategy, obviating the need for fine-tuning to adapt to new classes. Finally, a synthetic HRRP data, called RareHRRP, is developed to evaluate that the proposed model generalize well and is efficient in computation. (c) 2021 Published by Elsevier B.V.