上海交通大学学报（英文版） ›› 2017, Vol. 22 ›› Issue (5): 615-623.doi: 10.1007/s12204-017-1879-4

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Range Profile Target Recognition Using Sparse Representation Based on Feature Space

LÜ Wentao1* (吕文涛), WANG Junfeng2 (王军锋), YU Wenxian2 (郁文贤), BAO Xiaomin1 (包晓敏)   

  1. (1. School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China; 2. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)
  • 出版日期:2017-09-30 发布日期:2017-09-30
  • 通讯作者: Lü Wentao (吕文涛) E-mail:alvinlwt@zstu.edu.cn

Range Profile Target Recognition Using Sparse Representation Based on Feature Space

LÜ Wentao1* (吕文涛), WANG Junfeng2 (王军锋), YU Wenxian2 (郁文贤), BAO Xiaomin1 (包晓敏)   

  1. (1. School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China; 2. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)
  • Online:2017-09-30 Published:2017-09-30
  • Contact: Lü Wentao (吕文涛) E-mail:alvinlwt@zstu.edu.cn

摘要： A novel method is presented to improve the recognition rate of warhead in this paper. Firstly, a tool for electromagnetic calculation, like CST Microwave Studio, is used to simulate the frequency response of the electromagnetic scattering. Secondly, the echo and further the range profile are acquired from the frequency response by further processing. Thirdly, a set of discriminative features is extracted from the range profiles of the target. Fourthly, these features are used to construct a dictionary for the sparse representation classifier. Finally, the sample of the target can be classified by solving the sparsest coefficients. Since the reconstruction result is determined by a linear combination of the training samples, this method has a good robustness for the variable features. By formulating the problem within a feature-based sparse representation framework, the presented method combines the discriminative features of each sample during the sparse recovery process rather than in a postprocessing manner. Moreover, based on the feature representation space rather than a single feature or image pixel, the constructed dictionary exhibits both strong expressive and discriminative powers that can enhance the classification performance of the test sample. A series of test results based on the simulated data demonstrates the effectiveness of our method.

关键词: warhead recognition, range profile, sparse representation, feature dictionary, echo simulation

Abstract: A novel method is presented to improve the recognition rate of warhead in this paper. Firstly, a tool for electromagnetic calculation, like CST Microwave Studio, is used to simulate the frequency response of the electromagnetic scattering. Secondly, the echo and further the range profile are acquired from the frequency response by further processing. Thirdly, a set of discriminative features is extracted from the range profiles of the target. Fourthly, these features are used to construct a dictionary for the sparse representation classifier. Finally, the sample of the target can be classified by solving the sparsest coefficients. Since the reconstruction result is determined by a linear combination of the training samples, this method has a good robustness for the variable features. By formulating the problem within a feature-based sparse representation framework, the presented method combines the discriminative features of each sample during the sparse recovery process rather than in a postprocessing manner. Moreover, based on the feature representation space rather than a single feature or image pixel, the constructed dictionary exhibits both strong expressive and discriminative powers that can enhance the classification performance of the test sample. A series of test results based on the simulated data demonstrates the effectiveness of our method.

Key words: warhead recognition, range profile, sparse representation, feature dictionary, echo simulation

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