Isometric Cost-Sensitive Laplacian Eigenmaps for Imbalance Radar Target Recognition

Abstract

Traditional radar target recognition algorithms utilize balance data set to train the classifier and achieve a satisfactory result on a balance test data set. However, in the case of non-cooperative target recognition, we only obtain a small amount of non-cooperative target samples, while we can obtain a larger number of cooperative target samples easily, which leads to an imbalance training data set. In this paper, we consider the imbalance data classification problem in radar target recognition. We utilize the cost-sensitive approach and assume that different kinds of mistakes lead to different losses. Based on this assumption, a novel radar target recognition algorithm, called isometric cost-sensitive Laplacian eigenmaps (ICSLE), is presented. The basic idea of ICSLE is that the larger the misclassification cost is, the further the distance between two classes is, and vice versa. Moreover, in order to effectively utilize the cost information and local property of observation samples, we use the geodesic distance as the edge weight, instead of the local Euclidean distance. Experiments on millimeter wave radar high-resolution range profile (HRRP) demonstrate the effectiveness of our method.