Drone Target Detection Method Using U-Net for DTV-Based Passive Radar 


Vol. 47,  No. 10, pp. 1620-1628, Oct.  2022
10.7840/kics.2022.47.10.1620


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  Abstract

A passive bistatic radar estimates the position and the speed of a target using commercial broadcasting or communication signals. Since DTV (digital television) signal has a stronger signal power and broader bandwidth than other signals, it can be useful for a passive radar when detecting small objects with low-speed such as drones. A radar system generally exploits a CFAR (constant false alarm rate) detector to find a target in the range-Doppler map. However, because of various false detections caused by noise and clutter signals, the false alarm probability of the CFAR detector increases. This paper proposes a target detector based on U-Net, a neural network model used in semantic segmentation, to solve the problem. The proposed target detector can achieve a lower false alarm probability and have a higher target detection probability than the CFAR detector. We present the outstanding results of the artificial-neural-network-based target detector by comparing the CFAR detector’s performances in terms of detection probability, false alarm probability, and F1 score through simulation and real drone data.

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[IEEE Style]

J. Park, D. Park, J. Bang, H. Kim, "Drone Target Detection Method Using U-Net for DTV-Based Passive Radar," The Journal of Korean Institute of Communications and Information Sciences, vol. 47, no. 10, pp. 1620-1628, 2022. DOI: 10.7840/kics.2022.47.10.1620.

[ACM Style]

Ji-Hun Park, Do-Hyun Park, Jong-Hyeon Bang, and Hyoung-Nam Kim. 2022. Drone Target Detection Method Using U-Net for DTV-Based Passive Radar. The Journal of Korean Institute of Communications and Information Sciences, 47, 10, (2022), 1620-1628. DOI: 10.7840/kics.2022.47.10.1620.

[KICS Style]

Ji-Hun Park, Do-Hyun Park, Jong-Hyeon Bang, Hyoung-Nam Kim, "Drone Target Detection Method Using U-Net for DTV-Based Passive Radar," The Journal of Korean Institute of Communications and Information Sciences, vol. 47, no. 10, pp. 1620-1628, 10. 2022. (https://doi.org/10.7840/kics.2022.47.10.1620)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880