SCNet: Scattering center neural network for radar target recognition with incomplete target-aspects

Abstract

Most of the previous radar automatic target recognition (RATR) methods based on high resolution range profile (HRRP) are designed under the assumption of complete target-aspects, which assumes the HRRP samples with different target-aspects are complete in training dataset or template library. Few works were concentrated on HRRP RATR with incomplete target-aspects. However, it is extremely difficult and sometimes even impossible to obtain HRRPs with complete target-aspects in real world applications. Therefore, it is required to recognize targets of unseen target-aspects with an incomplete target-aspect template library. Aiming at this problem, a scattering center neural network (SCNet) is proposed for radar HRRP target recognition with incomplete target-aspects. Based on the assumption that an HRRP sample can be represented by a linear combination of a few atoms from a scattering center dictionary, we proposed a scattering center layer (SC-layer), which encapsulates the dictionary coding into an implicit layer and treats the scattering center dictionary as its parameter that is optimized in an end-to-end manner. We further proposed a discriminative target-aspect frame dictionary by dividing HRRPs of each class into multiple target-aspect frames and associating frame label information with each atom of it to enforce discriminability in features. A multiple target-aspect prototype classifier is proposed, which is more robust to intra-class variations and thus more suitable to handle the incomplete target-aspects problem compared with softmax classifier. The classification is simply implemented by matching HRRP features with each prototype and each HRRP is assigned to the class with the nearest prototype. In order to integrate both intra-class compactness and inter-class separation, we proposed a novel discriminative prototype loss. Taking the advantage of the discriminative prototype loss, HRRPs belonging to the same class and the same target-aspect frame are pulled closer to the corresponding prototype in the feature space, while simultaneously pushing apart from other prototypes of different classes. Experiments on the aircraft electromagnetic simulation dataset and the measured dataset demonstrated the superior performance of the proposed method compared with other HRRP-based RTAR methods under the condition of incomplete target-aspects.