Research on ground-moving target type recognition based on local mean decomposition (LMD) and support vector machine (SVM)

被引：0

作者：

Liu, Weibo ^{[1
]}

Yu, Xiaojing ^{[2
]}

Wen, Jiangtao ^{[2
]}

Zhao, Jinge ^{[2
]}

Li, Kaiyan ^{[2
]}

Zheng, Longjiang ^{[2
]}

机构：

[1] Yanshan Univ, Phys Educ Coll, Qinhuangdao 066004, Peoples R China

[2] Yanshan Univ, Key Lab Measurement Technol & Instrumentat Hebei, Qinhuangdao 066004, Peoples R China

来源：

SIXTH INTERNATIONAL CONFERENCE ON ELECTRONICS AND INFORMATION ENGINEERING | 2015年 / 9794卷

关键词：

Local Mean Decomposition (LMD); Support Vector Machine (SVM); Ground-moving Target Type Recognition; Seismic Signal;

D O I：

10.1117/12.2203650

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：

This paper presents a target type recognition method based on local mean decomposition (LMD) and support vector machine (SVM) using the seismic signal caused by the ground-moving target. The wavelet packet filter is used for improving signal noise ratio (SNR). Then, the seismic signal is decomposed into several production function (PF) components. The feature vector is composed of the energy of each principal PF. SVM is used as classifier which discriminate the human, car and truck. The experiment result shows that, the average discrimination accuracy of proposed method is over 92.0%.

引用

页数：5

共 8 条

[1] A Target Recognition Algorithm Based on Support Vector Machine
Ding, Yan
Jin, Weiqi
Yu, Yuhong
Wang, Han
2008 INTERNATIONAL CONFERENCE ON OPTICAL INSTRUMENTS AND TECHNOLOGY: OPTICAL SYSTEMS AND OPTOELECTRONIC INSTRUMENTS, 2009, 7156
[2] Brahmi character recognition based on SVM (support vector machine) classifier using image gradient features
Kaur, Sandeep
Sagar, B. B.
JOURNAL OF DISCRETE MATHEMATICAL SCIENCES & CRYPTOGRAPHY, 2019, 22 (08) : 1365 - 1381
[3] Research on fault localization of distribution transformers based on frequency response analysis and support vector machine (SVM)
Liu, Xingting
Wang, Haiqi
Gao, Zhuo
Han, Xutao
Frontiers in Energy Research, 2024, 12
[4] Finger vein recognition using weighted local binary pattern code based on a support vector machine
Hyeon Chang Lee
Byung Jun Kang
Eui Chul Lee
Kang Ryoung Park
Journal of Zhejiang University SCIENCE C, 2010, 11 : 514 - 524
[5] Finger vein recognition using weighted local binary pattern code based on a support vector machine
Hyeon Chang LEE
Byung Jun KANG
Eui Chul LEE
Kang Ryoung PARK
Frontiers of Information Technology & Electronic Engineering, 2010, (07) : 514 - 524
[6] Finger vein recognition using weighted local binary pattern code based on a support vector machine
Lee, Hyeon Chang
Kang, Byung Jun
Lee, Eui Chul
Park, Kang Ryoung
JOURNAL OF ZHEJIANG UNIVERSITY-SCIENCE C-COMPUTERS & ELECTRONICS, 2010, 11 (07): : 514 - 524
[7] Enhanced polynomial kernel (EPK)-based support vector machine (SVM) (EPK-SVM) classification technique for speech recognition in hearing-impaired listeners
Pavithra, S.
Janakiraman, S.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2021, 33 (03)
[8] Web-Based Application for Accurately Classifying Cancer Type from Microarray Gene Expression Data Using a Support Vector Machine (SVM) Learning Algorithm
Pawar, Shrikant
BIOINFORMATICS AND BIOMEDICAL ENGINEERING (IWBBIO 2019), PT II, 2019, 11466 : 149 - 154

← 1 →