Earticle

초록

영어

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.

목차

Abstract
 1. Introduction
 2. Similarity Measurement and Geodesic Distance
 3. Isometric Cost-Sensitive Laplacian Eigenmaps
 4. Experiment
  4.1. Experiment Settings
  4.2. Experimental Results and Discussion
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Xingjian Xu [ School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, China ]
• Yuehua Li [ School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, China ]
• Jianqiao Wang [ School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605