Kim, Byung-Kwan
(Microwave and Antenna Laboratory, School of Electrical Engineering, Korea Advanced Institute of Science and Technology)
,
Park, Junhyeong
(Microwave and Antenna Laboratory, School of Electrical Engineering, Korea Advanced Institute of Science and Technology)
,
Park, Seong-Jin
(Microwave and Antenna Laboratory, School of Electrical Engineering, Korea Advanced Institute of Science and Technology)
,
Kim, Tae-Wan
(Microwave and Antenna Laboratory, School of Electrical Engineering, Korea Advanced Institute of Science and Technology)
,
Jung, Dae-Hwan
(Microwave and Antenna Laboratory, School of Electrical Engineering, Korea Advanced Institute of Science and Technology)
,
Kim, Do-Hoon
(Microwave and Antenna Laboratory, School of Electrical Engineering, Korea Advanced Institute of Science and Technology)
,
Kim, Taihyung
(System Technology & Control)
,
Park, Seong-Ook
(Microwave and Antenna Laboratory, School of Electrical Engineering, Korea Advanced Institute of Science and Technology)
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...
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.
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.
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가설 설정
The number of detections of the horizontally fixed drone is the largest at the maximum detection range; however, the detection is not available for further distances. The reasons for the shorter detection range and sudden undetectability of the horizontally rotating blades are the following: 1) The RCS value of the blades is small because the surface area of the blades is tiny when they are horizontally rotating. Therefore, the required SNR is higher than that in the vertical case.
제안 방법
In this study, the Swerling target model of a drone was estimated using the results from the presented prototype of a chirp-pulse radar. Based on the radar equation, the pulse timing of the prototype radar is designed with three different pulse modes.
In this study, we thus measured the returned signals from the drone’s vertically and horizontally rotating blades using commercial chirp-pulse radar.
대상 데이터
All six motors (XM5010TE-9MR; 360 rpm/V; DualSky Corporation) rotated both clockwise and counterclockwise and were controlled by the Naza-M multi-rotor controller [19]. The drone had six attached carbon-fiber propellers called MRP-15 and made by DualSky Corporation [20] (Fig. 4). Two types of blades were employed for each rotation direction and they were symmetric to each other.
1; the specifications are listed in Table 1. The prototype is a chirp-pulse Doppler monostatic radar, which uses a 34.5 GHz carrier frequency. We introduce three distinct pulse modes to overcome an ambiguity problem and the large minimum range of pulse compression radar.
The unmanned drone used in this experiment was a Bumblebee F820 hexacopter from Hobbylord Corporation. The drone height was 440 mm and the diameter was 820 mm.
이론/모형
The target fluctuation model was first presented by Swerling [16], who showed that the radar targets can be classified by the RCS fluctuation due to their movement or rotation. In addition, detection probability of the targets was derived with the chi-square model. If we apply a proper integration method according to the Swerling model of the drone, the detectability can be significantly increased.
성능/효과
Using a given probability of detection and the number of false alarms, the required SNR can be calculated and plotted in Fig. 9. The difference of the required SNR between each model is 4.1 dB, and that between integration methods is 4.8 dB for 64 pulse integration, respectively.
From the measurement results and analysis on the integration gain with target models, it can be concluded that the proposed system detected the drone’s frame by the gain from coherent pulse integration.
The results additionally showed that the detectability of the vertically fixed drone was better than that of the horizontally fixed drone on account of the rapid fluctuation of the blade’s RCS.
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