Classification of Bird and Drone Targets Based on Motion Characteristics and Random Forest Model Using Surveillance Radar Data
Accurate detection and tracking of birds and drones are of great significance in various low altitude airspace surveillance scenarios. Radar is currently the most proper long range surveillance technology for this problem but also challenged by various difficulties on effective distinguishing betwee...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9626001/ |
| _version_ | 1818723656618999808 |
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| author | Jia Liu Qun Yu Xu Wei Shi Chen |
| author_facet | Jia Liu Qun Yu Xu Wei Shi Chen |
| author_sort | Jia Liu |
| collection | DOAJ |
| description | Accurate detection and tracking of birds and drones are of great significance in various low altitude airspace surveillance scenarios. Radar is currently the most proper long range surveillance technology for this problem but also challenged by various difficulties on effective distinguishing between birds and drones. This paper explores the inherent flight mechanic and behavior mode of birds and drones. A target classification method is proposed by extracting target motion characteristics from radar tracks. The random forest model is selected for target classification in the new feature space. The proposed method is verified by real bird surveillance radar systems deployed in airport region. Classification results on birds, quadcopter drones and dynamic precipitations indicate that the proposed method could provide good classification accuracy. The Gini importance descriptors in random forest model provide extra reference on motion characteristic evaluation and mining. High sample flexibility and efficiency make the classification system capable of handling complicated low altitude target surveillance and classification problems. Limitations of the existing method and potential optimization strategy are also discussed as future works. |
| first_indexed | 2024-12-17T21:13:59Z |
| format | Article |
| id | doaj.art-67df5b3e6a1c45abb11fa43f3401f289 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T21:13:59Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-67df5b3e6a1c45abb11fa43f3401f2892022-12-21T21:32:23ZengIEEEIEEE Access2169-35362021-01-01916013516014410.1109/ACCESS.2021.31302319626001Classification of Bird and Drone Targets Based on Motion Characteristics and Random Forest Model Using Surveillance Radar DataJia Liu0https://orcid.org/0000-0001-8841-1784Qun Yu Xu1Wei Shi Chen2Research Institute for Frontier Science, Beihang University, Beijing, ChinaResearch Institute of Civil Aviation Law, Regulation and Standardization, China Academy of Civil Aviation Science and Technology, Beijing, ChinaAirport Research Institute, China Academy of Civil Aviation Science and Technology, Beijing, ChinaAccurate detection and tracking of birds and drones are of great significance in various low altitude airspace surveillance scenarios. Radar is currently the most proper long range surveillance technology for this problem but also challenged by various difficulties on effective distinguishing between birds and drones. This paper explores the inherent flight mechanic and behavior mode of birds and drones. A target classification method is proposed by extracting target motion characteristics from radar tracks. The random forest model is selected for target classification in the new feature space. The proposed method is verified by real bird surveillance radar systems deployed in airport region. Classification results on birds, quadcopter drones and dynamic precipitations indicate that the proposed method could provide good classification accuracy. The Gini importance descriptors in random forest model provide extra reference on motion characteristic evaluation and mining. High sample flexibility and efficiency make the classification system capable of handling complicated low altitude target surveillance and classification problems. Limitations of the existing method and potential optimization strategy are also discussed as future works.https://ieeexplore.ieee.org/document/9626001/Target detectionradar trackingtarget classificationfeature extractionmachine learning |
| spellingShingle | Jia Liu Qun Yu Xu Wei Shi Chen Classification of Bird and Drone Targets Based on Motion Characteristics and Random Forest Model Using Surveillance Radar Data IEEE Access Target detection radar tracking target classification feature extraction machine learning |
| title | Classification of Bird and Drone Targets Based on Motion Characteristics and Random Forest Model Using Surveillance Radar Data |
| title_full | Classification of Bird and Drone Targets Based on Motion Characteristics and Random Forest Model Using Surveillance Radar Data |
| title_fullStr | Classification of Bird and Drone Targets Based on Motion Characteristics and Random Forest Model Using Surveillance Radar Data |
| title_full_unstemmed | Classification of Bird and Drone Targets Based on Motion Characteristics and Random Forest Model Using Surveillance Radar Data |
| title_short | Classification of Bird and Drone Targets Based on Motion Characteristics and Random Forest Model Using Surveillance Radar Data |
| title_sort | classification of bird and drone targets based on motion characteristics and random forest model using surveillance radar data |
| topic | Target detection radar tracking target classification feature extraction machine learning |
| url | https://ieeexplore.ieee.org/document/9626001/ |
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