그림 1. FMCW 레이다 비트 주파수 Fig. 1 FMCW radar beat frequency
그림 2. RNN 학습 및 추론 과정 Fig. 2 RNN learning and inferencing process
그림 3. 시스템 하드웨어 구성 Fig. 3 System hardware
그림 4. 비트 주파수 탐지 과정 Fig. 4 Beat frequency detection process
그림 5. 비트 주파수 흐름 Fig. 5 Beat frequency stream
그림 6. CFAR 기법과 RNN의 오탐지 발생률 (좌) 및 탐지율 (우) Fig. 6 False detection rate (Left) and detection accuracy (Right) of CFAR algorithm and RNN
표 1. FMCW 레이다 제원 Table 1. FMCW radar parameters
표 2. 드론 사양 Table 2. Drone specifications
표 3. 드론 비행 시나리오 Table 3. Drone flight scenario
References
- H. Rohling, "Radar CFAR Thresholding in Clutter and Multiple Target Situations," Proceedings of IEEE Transactions on Aerospace and Electronic Systems, Vol. AES-19, No. 4, pp. 608-621, 1983. https://doi.org/10.1109/TAES.1983.309350
- C. Jeong, Y. Jung, S. Lee, "Neural Network-based Radar Signal Classification System Using Probability Moment and ApEn," Journal of Soft Computing, Vol. 22, No. 13, pp. 4205-4219, 2018. https://doi.org/10.1007/s00500-017-2711-7
- M. Jan, "Radar Signal Identification Using a Neural Network and Pattern Recognition Methods," Proceedings of IEEE Telecommunications and Computer Engineering, pp. 79-83, 2018.
- J.M. García, D. Zoeke, M. Vossiek, "MIMO-FMCW Radar-Based Parking Monitoring Application With a Modified Convolutional Neural Network With Spatial Priors," Proceedings of IEEE Access, Vol. 6, pp.41391-41398, 2018. https://doi.org/10.1109/ACCESS.2018.2857007
- R. Perez, F. Schubert, R. Rasshofer, E. Biebl, "Single-Frame Vulnerable Road Users Classification with a 77 GHz FMCW Radar Sensor and a Convolutional Neural Network," Proceedings of IEEE International Radar Symposium, pp. 1-10, 2018.
- B. Vandersmissen, N. Knudde, A. Jalavand, I. Couckuyt, A. Bourdoux, "Indoor Person Identification Using a Low-Power FMCW Radar," Proceedings of IEEE Transactions on Geoscience and Remote Sensing, Vol. 99, pp. 1-12, 2018.
- A. Krizhevsky, I. Sutskever, G.E. Hinton, "Imagenet Classification with Deep Convolutional Neural Networks," Proceedings of Advances in neural information processing systems, pp. 1097-1105, 2012.
- B. Kim, "(A) Study on Radar for Micro-drone Detection and Target Classification Based on Deep Learning," Ph.D. Dissertation, 2017.
- C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, A. Rabinovich, "Going Deeper with Convolutions," Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1-9, 2015.
- E.D. Solovyeva, "Types of Recurrent Neural Networks for Nonlinear Dynamic System Modeling," SCM, 2017 XX IEEE International Conference, pp. 252-255, 2017.
- M. Kang, M. Lee, S. Kim, "Leakage and Clutter Suppression on FMCW Radar System for Small Unmanned Aerial Vehicle Detection," Proceedings of 2017 Defense SW/ICT Convergence Conference, SIGDS, 2017. (in Korean)
- C. Grimm, T. Breddermann, R. Farhoud, T. Fei, E. Warsitz, R. Haeb-Umbach, "Discrimination of Stationary from Moving Targets with Recurrent Neural Networks in Automotive Radar," Proceedings of IEEE MTT-S International Conference, pp. 1-4, 2018.
- D.A. Brooks, O. Schwander, F. Barbaresco, J.Y. Schneider, M. Cord, "Temporal Deep Learning for Drone Micro-Doppler Classification," Proceedings of IEEE International Radar Symposium, pp. 1-10 2018.
- M. Xu, C. Fan, J.D. Paden, G.C. Fox, D.J. Crandall, "Multi-Task Spatiotemporal Neural Networks for Structured Surface Reconstruction," arXiv, 1801.03986, 2018.
- M. Lee, S. Kim, "Performance Controllable Shared Cache Architecture for Multi-Core Soft Real-Time Systems," Proceedings of IEEE Computer Design, pp. 519-522, 2013.
- T. Park, S. Kim, "Dynamic Scheduling Algorithm and Its Schedulability Analysis for Certifiable Dual-criticality Systems," Proceedings of Embedded Sofrware, pp. 253-262, 2011.