Drone Detection with Chirp‐Pulse Radar Based on Target Fluctuation Models
This paper presents a pulse radar system to detect drones based on a target fluctuation model, specifically the Swerling target model. Because drones are small atypical objects and are mainly composed of non‐conducting materials, their radar cross‐section value is low and fluctuating. Therefore, det...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Electronics and Telecommunications Research Institute (ETRI)
2018-04-01
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| Series: | ETRI Journal |
| Subjects: | |
| Online Access: | https://doi.org/10.4218/etrij.2017-0090 |
| _version_ | 1819226582743515136 |
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| author | Byung‐Kwan Kim Junhyeong Park Seong‐Jin Park Tae‐Wan Kim Dae‐Hwan Jung Do‐Hoon Kim Taihyung Kim Seong‐Ook Park |
| author_facet | Byung‐Kwan Kim Junhyeong Park Seong‐Jin Park Tae‐Wan Kim Dae‐Hwan Jung Do‐Hoon Kim Taihyung Kim Seong‐Ook Park |
| author_sort | Byung‐Kwan Kim |
| collection | DOAJ |
| description | This paper presents a pulse radar system to detect drones based on a target fluctuation model, specifically the Swerling target model. Because drones are small atypical objects and are mainly composed of non‐conducting materials, their radar cross‐section value is low and fluctuating. Therefore, determining the target fluctuation model and applying a proper integration method are important. The proposed system is herein experimentally verified and the results are discussed. A prototype design of the pulse radar system is based on radar equations. It adopts three different pulse modes and a coherent pulse integration to ensure a high signal‐to‐noise ratio. Outdoor measurements are performed with a prototype radar system to detect Doppler frequencies from both the drone frame and blades. The results indicate that the drone frame and blades are detected within an instrumental maximum range. Additionally, the results show that the drone's frame and blades are close to the Swerling 3 and 4 target models, respectively. By the analysis of the Swerling target models, proper integration methods for detecting drones are verified and can thus contribute to increasing in detectability. |
| first_indexed | 2024-12-23T10:27:47Z |
| format | Article |
| id | doaj.art-088bda4268ee436f9f0eda47811bbdc8 |
| institution | Directory Open Access Journal |
| issn | 1225-6463 2233-7326 |
| language | English |
| last_indexed | 2024-12-23T10:27:47Z |
| publishDate | 2018-04-01 |
| publisher | Electronics and Telecommunications Research Institute (ETRI) |
| record_format | Article |
| series | ETRI Journal |
| spelling | doaj.art-088bda4268ee436f9f0eda47811bbdc82022-12-21T17:50:30ZengElectronics and Telecommunications Research Institute (ETRI)ETRI Journal1225-64632233-73262018-04-0140218819610.4218/etrij.2017-009010.4218/etrij.2017-0090Drone Detection with Chirp‐Pulse Radar Based on Target Fluctuation ModelsByung‐Kwan KimJunhyeong ParkSeong‐Jin ParkTae‐Wan KimDae‐Hwan JungDo‐Hoon KimTaihyung KimSeong‐Ook ParkThis paper presents a pulse radar system to detect drones based on a target fluctuation model, specifically the Swerling target model. Because drones are small atypical objects and are mainly composed of non‐conducting materials, their radar cross‐section value is low and fluctuating. Therefore, determining the target fluctuation model and applying a proper integration method are important. The proposed system is herein experimentally verified and the results are discussed. A prototype design of the pulse radar system is based on radar equations. It adopts three different pulse modes and a coherent pulse integration to ensure a high signal‐to‐noise ratio. Outdoor measurements are performed with a prototype radar system to detect Doppler frequencies from both the drone frame and blades. The results indicate that the drone frame and blades are detected within an instrumental maximum range. Additionally, the results show that the drone's frame and blades are close to the Swerling 3 and 4 target models, respectively. By the analysis of the Swerling target models, proper integration methods for detecting drones are verified and can thus contribute to increasing in detectability.https://doi.org/10.4218/etrij.2017-0090Doppler measurementsDoppler radarMillimeter wave radarRadar signal processing |
| spellingShingle | Byung‐Kwan Kim Junhyeong Park Seong‐Jin Park Tae‐Wan Kim Dae‐Hwan Jung Do‐Hoon Kim Taihyung Kim Seong‐Ook Park Drone Detection with Chirp‐Pulse Radar Based on Target Fluctuation Models ETRI Journal Doppler measurements Doppler radar Millimeter wave radar Radar signal processing |
| title | Drone Detection with Chirp‐Pulse Radar Based on Target Fluctuation Models |
| title_full | Drone Detection with Chirp‐Pulse Radar Based on Target Fluctuation Models |
| title_fullStr | Drone Detection with Chirp‐Pulse Radar Based on Target Fluctuation Models |
| title_full_unstemmed | Drone Detection with Chirp‐Pulse Radar Based on Target Fluctuation Models |
| title_short | Drone Detection with Chirp‐Pulse Radar Based on Target Fluctuation Models |
| title_sort | drone detection with chirp pulse radar based on target fluctuation models |
| topic | Doppler measurements Doppler radar Millimeter wave radar Radar signal processing |
| url | https://doi.org/10.4218/etrij.2017-0090 |
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