Research and Experiment of Radar Signal Support Vector Clustering Sorting Based on Feature Extraction and Feature Selection
The result of radar signal sorting directly affects the performance of electronic reconnaissance equipment. Sorting method based on intra-pulse features has become a research focus in recent years. However, as the number of extracted features increases, the dimension of the feature vector becomes hi...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9090144/ |
| _version_ | 1818664056063524864 |
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| author | Shiqiang Wang Caiyun Gao Qin Zhang Veerendra Dakulagi Huiyong Zeng Guimei Zheng Juan Bai Yuwei Song Jiliang Cai Binfeng Zong |
| author_facet | Shiqiang Wang Caiyun Gao Qin Zhang Veerendra Dakulagi Huiyong Zeng Guimei Zheng Juan Bai Yuwei Song Jiliang Cai Binfeng Zong |
| author_sort | Shiqiang Wang |
| collection | DOAJ |
| description | The result of radar signal sorting directly affects the performance of electronic reconnaissance equipment. Sorting method based on intra-pulse features has become a research focus in recent years. However, as the number of extracted features increases, the dimension of the feature vector becomes higher and higher. And too many dimensional feature vectors would make the complexity of the sorting algorithm increase geometrically. In this way, feature selection becomes more and more necessary. Combining the latest research on fuzzy rough sets, this paper proposes two feature selection methods, namely two-steps attribute reduction based on fuzzy dependency (TARFD) algorithm and fuzzy rough artificial bee colony (FRABC) algorithm. The TARFD method uses the candidate attribute set as starting point, according to the definition of the redundant attribute set. Then the less important attributes are successively eliminated. The FRABC method starts from the dependence degree of fuzzy rough set, and constructs a fitness function that reflects the importance of the attribute subset and the reduction rate. Based on this function, the artificial bee colony algorithm is used to reduce the attributes of the dataset. Using the TARFD and FRABC algorithms, the extracted feature sets, including entropy feature set, Zernike moment feature set, pseudo Zernike feature set, gray level co-occurrence matrix (GLCM) feature set, and Hu-invariant moment feature set are processed, then an optimal feature subset was obtained and a sorting test was performed. The results show the effectiveness of the extracted intra-pulse features and the efficiency of the feature selection algorithm. |
| first_indexed | 2024-12-17T05:26:40Z |
| format | Article |
| id | doaj.art-c9e0fe52a5fc4aca92aa788636ed527b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T05:26:40Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c9e0fe52a5fc4aca92aa788636ed527b2022-12-21T22:01:51ZengIEEEIEEE Access2169-35362020-01-018933229333410.1109/ACCESS.2020.29932709090144Research and Experiment of Radar Signal Support Vector Clustering Sorting Based on Feature Extraction and Feature SelectionShiqiang Wang0https://orcid.org/0000-0002-7791-7197Caiyun Gao1https://orcid.org/0000-0002-4847-5748Qin Zhang2https://orcid.org/0000-0002-8580-5099Veerendra Dakulagi3Huiyong Zeng4https://orcid.org/0000-0003-0450-8103Guimei Zheng5https://orcid.org/0000-0002-0461-6107Juan Bai6https://orcid.org/0000-0001-7737-7576Yuwei Song7https://orcid.org/0000-0003-2608-7784Jiliang Cai8https://orcid.org/0000-0002-6592-623XBinfeng Zong9https://orcid.org/0000-0002-4041-5388Air and Missile-Defence College, Air Force Engineering University, Xi’an, ChinaDepartment of Basic Sciences, Air Force Engineering University, Xi’an, ChinaAir and Missile-Defence College, Air Force Engineering University, Xi’an, ChinaDepartment of Electronics and Communication Engineering, Lincoln University College, Petaling Jaya, MalaysiaAir and Missile-Defence College, Air Force Engineering University, Xi’an, ChinaAir and Missile-Defence College, Air Force Engineering University, Xi’an, ChinaAir and Missile-Defence College, Air Force Engineering University, Xi’an, ChinaAir and Missile-Defence College, Air Force Engineering University, Xi’an, ChinaAir and Missile-Defence College, Air Force Engineering University, Xi’an, ChinaAir and Missile-Defence College, Air Force Engineering University, Xi’an, ChinaThe result of radar signal sorting directly affects the performance of electronic reconnaissance equipment. Sorting method based on intra-pulse features has become a research focus in recent years. However, as the number of extracted features increases, the dimension of the feature vector becomes higher and higher. And too many dimensional feature vectors would make the complexity of the sorting algorithm increase geometrically. In this way, feature selection becomes more and more necessary. Combining the latest research on fuzzy rough sets, this paper proposes two feature selection methods, namely two-steps attribute reduction based on fuzzy dependency (TARFD) algorithm and fuzzy rough artificial bee colony (FRABC) algorithm. The TARFD method uses the candidate attribute set as starting point, according to the definition of the redundant attribute set. Then the less important attributes are successively eliminated. The FRABC method starts from the dependence degree of fuzzy rough set, and constructs a fitness function that reflects the importance of the attribute subset and the reduction rate. Based on this function, the artificial bee colony algorithm is used to reduce the attributes of the dataset. Using the TARFD and FRABC algorithms, the extracted feature sets, including entropy feature set, Zernike moment feature set, pseudo Zernike feature set, gray level co-occurrence matrix (GLCM) feature set, and Hu-invariant moment feature set are processed, then an optimal feature subset was obtained and a sorting test was performed. The results show the effectiveness of the extracted intra-pulse features and the efficiency of the feature selection algorithm.https://ieeexplore.ieee.org/document/9090144/Feature extractionfeature selectionfeature setsupport vector machinesupport vector clustering |
| spellingShingle | Shiqiang Wang Caiyun Gao Qin Zhang Veerendra Dakulagi Huiyong Zeng Guimei Zheng Juan Bai Yuwei Song Jiliang Cai Binfeng Zong Research and Experiment of Radar Signal Support Vector Clustering Sorting Based on Feature Extraction and Feature Selection IEEE Access Feature extraction feature selection feature set support vector machine support vector clustering |
| title | Research and Experiment of Radar Signal Support Vector Clustering Sorting Based on Feature Extraction and Feature Selection |
| title_full | Research and Experiment of Radar Signal Support Vector Clustering Sorting Based on Feature Extraction and Feature Selection |
| title_fullStr | Research and Experiment of Radar Signal Support Vector Clustering Sorting Based on Feature Extraction and Feature Selection |
| title_full_unstemmed | Research and Experiment of Radar Signal Support Vector Clustering Sorting Based on Feature Extraction and Feature Selection |
| title_short | Research and Experiment of Radar Signal Support Vector Clustering Sorting Based on Feature Extraction and Feature Selection |
| title_sort | research and experiment of radar signal support vector clustering sorting based on feature extraction and feature selection |
| topic | Feature extraction feature selection feature set support vector machine support vector clustering |
| url | https://ieeexplore.ieee.org/document/9090144/ |
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