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

Abstract

Learning from a few high-resolution range profiles (HRRPs) with multi-modality property caused by sensitivity 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 discriminative 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.