Exploring Radar Micro-Doppler Signatures for Recognition of Drone Types
In this study, we examine the use of micro-Doppler signals produced by different blades (i.e., puller and lifting blades) to aid in radar-based target recognition of small drones. We categorize small drones into three types based on their blade types: fixed-wing drones with only puller blades, multi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Drones |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-446X/7/4/280 |
| _version_ | 1797605722931855360 |
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| author | Jun Yan Huiping Hu Jiangkun Gong Deyong Kong Deren Li |
| author_facet | Jun Yan Huiping Hu Jiangkun Gong Deyong Kong Deren Li |
| author_sort | Jun Yan |
| collection | DOAJ |
| description | In this study, we examine the use of micro-Doppler signals produced by different blades (i.e., puller and lifting blades) to aid in radar-based target recognition of small drones. We categorize small drones into three types based on their blade types: fixed-wing drones with only puller blades, multi-rotor drones with only lifting blades, and hybrid vertical take-off and landing (VTOL) fixed-wing drones with both lifting and puller blades. We quantify the radar signatures of the three drones using statistical measures, such as signal-to-noise ratio (SNR), signal-to-clutter ratio (SCR), Doppler speed, Doppler frequency difference (DFD), and Doppler magnitude ratio (DMR). Our findings show that the micro-Doppler signals of lifting blades in all three drone types were stronger than those of puller blades. Specifically, the DFD and DMR values of pusher blades were below 100 Hz and 0.3, respectively, which were much smaller than the 200 Hz and 0.8 values for lifting blades. The micro-Doppler signals of the puller blades were weaker and more stable than those of the lifting blades. Our study demonstrates the potential of using micro-Doppler signatures modulated by different blades for improving drone detection and the identification of drone types by drone detection radar. |
| first_indexed | 2024-03-11T05:05:06Z |
| format | Article |
| id | doaj.art-7b102629cc24448eb772020c9dde6366 |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-03-11T05:05:06Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-7b102629cc24448eb772020c9dde63662023-11-17T18:58:30ZengMDPI AGDrones2504-446X2023-04-017428010.3390/drones7040280Exploring Radar Micro-Doppler Signatures for Recognition of Drone TypesJun Yan0Huiping Hu1Jiangkun Gong2Deyong Kong3Deren Li4State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sense, Wuhan University, Wuhan 430072, ChinaWuhan Geomatics Institute, Wuhan 430022, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sense, Wuhan University, Wuhan 430072, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sense, Wuhan University, Wuhan 430072, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sense, Wuhan University, Wuhan 430072, ChinaIn this study, we examine the use of micro-Doppler signals produced by different blades (i.e., puller and lifting blades) to aid in radar-based target recognition of small drones. We categorize small drones into three types based on their blade types: fixed-wing drones with only puller blades, multi-rotor drones with only lifting blades, and hybrid vertical take-off and landing (VTOL) fixed-wing drones with both lifting and puller blades. We quantify the radar signatures of the three drones using statistical measures, such as signal-to-noise ratio (SNR), signal-to-clutter ratio (SCR), Doppler speed, Doppler frequency difference (DFD), and Doppler magnitude ratio (DMR). Our findings show that the micro-Doppler signals of lifting blades in all three drone types were stronger than those of puller blades. Specifically, the DFD and DMR values of pusher blades were below 100 Hz and 0.3, respectively, which were much smaller than the 200 Hz and 0.8 values for lifting blades. The micro-Doppler signals of the puller blades were weaker and more stable than those of the lifting blades. Our study demonstrates the potential of using micro-Doppler signatures modulated by different blades for improving drone detection and the identification of drone types by drone detection radar.https://www.mdpi.com/2504-446X/7/4/280automatic target recognition (ATR)drone bladesdrone type classificationmicro-Doppler |
| spellingShingle | Jun Yan Huiping Hu Jiangkun Gong Deyong Kong Deren Li Exploring Radar Micro-Doppler Signatures for Recognition of Drone Types Drones automatic target recognition (ATR) drone blades drone type classification micro-Doppler |
| title | Exploring Radar Micro-Doppler Signatures for Recognition of Drone Types |
| title_full | Exploring Radar Micro-Doppler Signatures for Recognition of Drone Types |
| title_fullStr | Exploring Radar Micro-Doppler Signatures for Recognition of Drone Types |
| title_full_unstemmed | Exploring Radar Micro-Doppler Signatures for Recognition of Drone Types |
| title_short | Exploring Radar Micro-Doppler Signatures for Recognition of Drone Types |
| title_sort | exploring radar micro doppler signatures for recognition of drone types |
| topic | automatic target recognition (ATR) drone blades drone type classification micro-Doppler |
| url | https://www.mdpi.com/2504-446X/7/4/280 |
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