Similarity preserving multi-task learning for radar target recognition

Abstract

In this study, a statistical recognition model based on similarity preserving multi-task learning (SP-MTL) is developed for radar target recognition of high-resolution range profile (HRRP) data. A similarity preserving constraint, which describes the similarity information of HRRP samples, is introduced into multi-task learning to enhance the discriminative capability of the statistical model with limited training data. In addition, the SP-MTL model can be applied to the model prediction of new data based on transfer learning theory. Experiments on measured data show that the proposed model can achieve better recognition performance than traditional methods when training data is small. The application of the SP-MTL model to model prediction based on transfer learning theory can improve the learning precision of the new statistical model compared with the single-task learning of new data.