Railway Track Recognition Based on Radar Cross-Section Statistical Characterization Using mmWave Radar
The track settlement has a great influence on the safe operation of high-speed trains. The existing track settlement measurement approach requires sophisticated or expensive equipments, and the real-time performance is limited. To address the issue, an ultra-high resolution track settlement detectio...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/2/294 |
| _version_ | 1797490748224962560 |
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| author | Shuo Li Jieqiong Ding Weirong Liu Heng Li Feng Zhou Zhengfa Zhu |
| author_facet | Shuo Li Jieqiong Ding Weirong Liu Heng Li Feng Zhou Zhengfa Zhu |
| author_sort | Shuo Li |
| collection | DOAJ |
| description | The track settlement has a great influence on the safe operation of high-speed trains. The existing track settlement measurement approach requires sophisticated or expensive equipments, and the real-time performance is limited. To address the issue, an ultra-high resolution track settlement detection method is proposed by using millimeter wave radar based on frequency modulated continuous wave (FMCW). Firstly, by constructing the RCS statistical feature data set of multiple objects in the track settlement measurement environment, a directed acyclic graph-support vector machine (DAG-SVM) based method is designed to solve the problem of track recognition in multi-object scenes. Then, the adaptive chirp-z-transform (ACZT) algorithm is used to estimate the distance between the radar and the track surface, which realizes automatic real-time track settlement detection. An experimental platform has been constructed to verify the effectiveness of the proposed method. The experimental results show that the accuracy of track classification and identification is at least 95%, and the accuracy of track settlement measurement exceeds 0.5 mm, which completely meets the accuracy requirements of the railway system. |
| first_indexed | 2024-03-10T00:36:31Z |
| format | Article |
| id | doaj.art-8b128e75f9574d23b92c5bdf81b7d011 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T00:36:31Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-8b128e75f9574d23b92c5bdf81b7d0112023-11-23T15:15:15ZengMDPI AGRemote Sensing2072-42922022-01-0114229410.3390/rs14020294Railway Track Recognition Based on Radar Cross-Section Statistical Characterization Using mmWave RadarShuo Li0Jieqiong Ding1Weirong Liu2Heng Li3Feng Zhou4Zhengfa Zhu5School of Electrical and Information Engineering, Changsha University of Science & Technology, Changsha 410114, ChinaSchool of Electrical and Information Engineering, Changsha University of Science & Technology, Changsha 410114, ChinaSchool of Computer Science and Engineering, Central South University, Changsha 410083, ChinaSchool of Computer Science and Engineering, Central South University, Changsha 410083, ChinaSchool of Electrical and Information Engineering, Changsha University of Science & Technology, Changsha 410114, ChinaSchool of Electrical and Information Engineering, Changsha University of Science & Technology, Changsha 410114, ChinaThe track settlement has a great influence on the safe operation of high-speed trains. The existing track settlement measurement approach requires sophisticated or expensive equipments, and the real-time performance is limited. To address the issue, an ultra-high resolution track settlement detection method is proposed by using millimeter wave radar based on frequency modulated continuous wave (FMCW). Firstly, by constructing the RCS statistical feature data set of multiple objects in the track settlement measurement environment, a directed acyclic graph-support vector machine (DAG-SVM) based method is designed to solve the problem of track recognition in multi-object scenes. Then, the adaptive chirp-z-transform (ACZT) algorithm is used to estimate the distance between the radar and the track surface, which realizes automatic real-time track settlement detection. An experimental platform has been constructed to verify the effectiveness of the proposed method. The experimental results show that the accuracy of track classification and identification is at least 95%, and the accuracy of track settlement measurement exceeds 0.5 mm, which completely meets the accuracy requirements of the railway system.https://www.mdpi.com/2072-4292/14/2/294millimeter wave radarradar cross section (RCS)target recognitionfrequency-modulated continuous wave (FMCW)statistical feature extractionsupport vector machine (SVM) |
| spellingShingle | Shuo Li Jieqiong Ding Weirong Liu Heng Li Feng Zhou Zhengfa Zhu Railway Track Recognition Based on Radar Cross-Section Statistical Characterization Using mmWave Radar Remote Sensing millimeter wave radar radar cross section (RCS) target recognition frequency-modulated continuous wave (FMCW) statistical feature extraction support vector machine (SVM) |
| title | Railway Track Recognition Based on Radar Cross-Section Statistical Characterization Using mmWave Radar |
| title_full | Railway Track Recognition Based on Radar Cross-Section Statistical Characterization Using mmWave Radar |
| title_fullStr | Railway Track Recognition Based on Radar Cross-Section Statistical Characterization Using mmWave Radar |
| title_full_unstemmed | Railway Track Recognition Based on Radar Cross-Section Statistical Characterization Using mmWave Radar |
| title_short | Railway Track Recognition Based on Radar Cross-Section Statistical Characterization Using mmWave Radar |
| title_sort | railway track recognition based on radar cross section statistical characterization using mmwave radar |
| topic | millimeter wave radar radar cross section (RCS) target recognition frequency-modulated continuous wave (FMCW) statistical feature extraction support vector machine (SVM) |
| url | https://www.mdpi.com/2072-4292/14/2/294 |
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