Meta Network for Radar HRRP Noncooperative Target Recognition with Missing Aspects

Abstract

We propose a meta network (MNet) for the problem of target-aspect missing in radar high-resolution range profile (HRRP)-based noncooperative target recognition, where a classifier must be generalized to new aspects not seen in the training set, given only a small number of HRRP data of each new aspect. The MNet is a time domain convolutional neural network (TCNN) that is built based upon recent progress in meta-learning. In effect, it learns a model that is easy and fast to fine-tune, allowing the adaptation to happen in the right space for fast learning. Besides, we construct a new controllable HRRP dataset suitable for the scenario of noncooperative target-aspect missing using electromagnetic simulation. Compared with the traditional methods, the MNet is more efficient and could achieve better performance. Extensive experiments on the simulated HRRP dataset are conducted to illustrate the effectiveness of the proposed method.