SPARSE TIME-FREQUENCY REPRESENTATION BASED FEATURE EXTRACTION METHOD FOR LANDMINE DISCRIMINATION

被引：7
|
作者：
Wang, Y. [1 ]
Song, Q. [1 ]
Jin, T. [1 ]
Shi, Y. [1 ]
Huang, X. [1 ]
机构：
[1] Natl Univ Def Technol, Sch Elect Sci & Engn, Changsha 410073, Hunan, Peoples R China
来源：
PROGRESS IN ELECTROMAGNETICS RESEARCH-PIER | 2012年 / 133卷
基金：
中国国家自然科学基金;
关键词：
GROUND-PENETRATING RADAR; SYNTHETIC-APERTURE RADAR; MINE DETECTION; TARGET RECOGNITION; SAR; SYSTEM; PERFORMANCE; SIMULATION; CLUTTER; MODEL;
D O I：
10.2528/PIER12082104
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Low-frequency ultra-wideband synthetic aperture radar is a promising technology for landmine detection. According to the scattering characteristics of body-of-revolution (BOR) along with azimuth angles, a discriminator based on Bayesian decision rule is proposed, which uses sequential features, i.e., double-hump distance. First, the algorithm estimates the target scatterings in all azimuth angles based on regions of interest. Second, sequential aspect features are extracted by sparse time-frequency representation. Third, the distributions of features are obtained by training samples, and then the posterior probability of landmine class is computed as an input to the classifier adopting Mahalanobis distance. The experimental results indicate that the proposed algorithm is effective in BOR target discrimination.
引用
收藏
页码：459 / 475
页数：17
相关论文
共 50 条
  • [21] Research on Extraction Method of Time-Frequency Feature of Human Motion
    Wang Tianrun
    Su Zhong
    Li Chao
    Liu Ning
    PROCEEDINGS OF THE 2019 31ST CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2019), 2019, : 704 - 708
  • [22] Sparse Reconstruction Based Time-frequency Representation for Time-of-flight Extraction of Undersampled Lamb Wave Signal
    Wang, Zhe
    Huang, Songling
    Wang, Shen
    Wang, Qing
    Zhao, Wei
    2020 CONFERENCE ON PRECISION ELECTROMAGNETIC MEASUREMENTS (CPEM), 2020,
  • [23] Rolling Bearing Fault Monitoring for Sparse Time-Frequency Representation and Feature Detection Strategy
    Tang, Jiahui
    Wu, Jimei
    Qing, Jiajuan
    Kang, Tuo
    ENTROPY, 2022, 24 (12)
  • [24] Fault Severity Monitoring of Rolling Bearings Based on Texture Feature Extraction of Sparse Time-Frequency Images
    Du, Yan
    Chen, Yingpin
    Meng, Guoying
    Ding, Jun
    Xiao, Yajing
    APPLIED SCIENCES-BASEL, 2018, 8 (09):
  • [25] Diagnosis Method of Combing Feature Extraction Based on Time-Frequency Analysis and Intelligent Classifier
    Gao, Baolu
    Chen, Junjie
    Xiong, Xiaoyan
    Xiong, Shibo
    WEB INFORMATION SYSTEMS AND MINING, PT I, 2011, 6987 : 419 - +
  • [26] Feature extraction method of bearing performance degradation based on time-frequency image fusion
    Zhang, Lijun
    Liu, Bo
    Zhang, Bin
    He, Fei
    Jixie Gongcheng Xuebao/Journal of Mechanical Engineering, 2013, 49 (22): : 53 - 58
  • [27] A new gear fault feature extraction method based on hybrid time-frequency analysis
    Liu, Wenyi
    Han, Jiguang
    Lu, Xiangning
    NEURAL COMPUTING & APPLICATIONS, 2014, 25 (02): : 387 - 392
  • [28] Time-Frequency Fault Feature Extraction for Rolling Bearing Based on the Tensor Manifold Method
    Wang, Fengtao
    Chen, Shouhai
    Sun, Jian
    Yan, Dawen
    Wang, Lei
    Zhang, Lihua
    MATHEMATICAL PROBLEMS IN ENGINEERING, 2014, 2014
  • [29] Sparse time-frequency representation based on multiwindow discrete Gabor transform
    Li, Rui
    Zhou, Jian
    INTERNATIONAL JOURNAL OF WAVELETS MULTIRESOLUTION AND INFORMATION PROCESSING, 2018, 16 (05)
  • [30] PM Prediction Based on Time-Frequency Separation Feature Extraction
    Zhang, Huanming
    Lin, Bo
    Gao, Feifei
    IEEE WIRELESS COMMUNICATIONS LETTERS, 2025, 14 (01) : 183 - 187
12345