Improving Ship Detection Based on Decision Tree Classification for High Frequency Surface Wave Radar
The traditional constant false alarm rate (CFAR) method, with fixed parameter settings and single noise background calculation, is unable to intelligently catch the current detection background. To improve the performance of the CFAR method, this paper proposes a target detection method based on dec...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/11/3/493 |
| _version_ | 1797610899713818624 |
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| author | Zhiqing Yang Yeping Lai Hao Zhou Yingwei Tian Yao Qin Zongwang Lv |
| author_facet | Zhiqing Yang Yeping Lai Hao Zhou Yingwei Tian Yao Qin Zongwang Lv |
| author_sort | Zhiqing Yang |
| collection | DOAJ |
| description | The traditional constant false alarm rate (CFAR) method, with fixed parameter settings and single noise background calculation, is unable to intelligently catch the current detection background. To improve the performance of the CFAR method, this paper proposes a target detection method based on decision tree classification (DTC) for high-frequency surface wave radar (HFSWR). Firstly, the training sample set and labels are obtained by means of a ship automatic identification system (AIS). Then, feature vector of range dimension, Doppler dimension and range-Doppler (RD) dimension is extracted by way of cell averaging, ordered statistics, censored mean and trimmed mean. Finally, DTC is used to recognize “true” and “false” targets in feature space. Experimental results show that, under the same number of detection targets, the DTC method is superior to traditional CFAR methods, and the accuracy of target detection can be increased by more than 5%. |
| first_indexed | 2024-03-11T06:21:24Z |
| format | Article |
| id | doaj.art-a88bf4df41e44b19866352b8e736bb50 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-11T06:21:24Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-a88bf4df41e44b19866352b8e736bb502023-11-17T11:56:25ZengMDPI AGJournal of Marine Science and Engineering2077-13122023-02-0111349310.3390/jmse11030493Improving Ship Detection Based on Decision Tree Classification for High Frequency Surface Wave RadarZhiqing Yang0Yeping Lai1Hao Zhou2Yingwei Tian3Yao Qin4Zongwang Lv5College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, ChinaPeng Cheng Laboratory, Shenzhen 518055, ChinaSchool of Electronic Information, Wuhan University, Wuhan 430072, ChinaSchool of Electronic Information, Wuhan University, Wuhan 430072, ChinaCollege of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, ChinaCollege of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, ChinaThe traditional constant false alarm rate (CFAR) method, with fixed parameter settings and single noise background calculation, is unable to intelligently catch the current detection background. To improve the performance of the CFAR method, this paper proposes a target detection method based on decision tree classification (DTC) for high-frequency surface wave radar (HFSWR). Firstly, the training sample set and labels are obtained by means of a ship automatic identification system (AIS). Then, feature vector of range dimension, Doppler dimension and range-Doppler (RD) dimension is extracted by way of cell averaging, ordered statistics, censored mean and trimmed mean. Finally, DTC is used to recognize “true” and “false” targets in feature space. Experimental results show that, under the same number of detection targets, the DTC method is superior to traditional CFAR methods, and the accuracy of target detection can be increased by more than 5%.https://www.mdpi.com/2077-1312/11/3/493HFSWRdecision treetarget classification and detection |
| spellingShingle | Zhiqing Yang Yeping Lai Hao Zhou Yingwei Tian Yao Qin Zongwang Lv Improving Ship Detection Based on Decision Tree Classification for High Frequency Surface Wave Radar Journal of Marine Science and Engineering HFSWR decision tree target classification and detection |
| title | Improving Ship Detection Based on Decision Tree Classification for High Frequency Surface Wave Radar |
| title_full | Improving Ship Detection Based on Decision Tree Classification for High Frequency Surface Wave Radar |
| title_fullStr | Improving Ship Detection Based on Decision Tree Classification for High Frequency Surface Wave Radar |
| title_full_unstemmed | Improving Ship Detection Based on Decision Tree Classification for High Frequency Surface Wave Radar |
| title_short | Improving Ship Detection Based on Decision Tree Classification for High Frequency Surface Wave Radar |
| title_sort | improving ship detection based on decision tree classification for high frequency surface wave radar |
| topic | HFSWR decision tree target classification and detection |
| url | https://www.mdpi.com/2077-1312/11/3/493 |
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