Proposed deep neural network ARTRNet for automatic target recognition for FMCW radar
138 viewsDOI:
https://doi.org/10.54939/1859-1043.j.mst.84.2022.24-31Keywords:
FMCW; Radar; Range; Azimuth; Doppler; Object detection; Deep learning.Abstract
In this paper, we propose a deep learning neural network (named ARTRNet) that automatically recognizes radar targets based on the characteristic signature of radar cross section and Doppler frequency of target in the reflected signal. The raw data input to ARTRNet is 3D formatted with distance - azimuth - frequency information. The author proposes an improvement of the loss function in the neural network training process to improve the target recognition performance of the model.
References
[1]. Jedrzej Drozdowicz, Maciej Wielgo, Piotr Samczynski, Krzysztof Kulpa, Jaroslaw Krzonkalla, Maj Mordzonek, Marcin Bryl, Zbigniew Jakielaszek “35 GHz FMCW Drone Detection System”.
[2]. Jinwei Wan, Bo Chen1, Bin Xu, Hongwei Liu and Lin Jin. “Convolutional neural networks for radar HRRP target recognition and rejection”. EURASIP Journal on Advances in Signal, 2019:5, (2019). DOI: https://doi.org/10.1186/s13634-019-0603-y
[3]. Nguyễn Văn Trà, Đoàn Văn Sáng, Vũ Chí Thanh, Trần Công Tráng, "Đánh giá hiệu năng tự động phân loại mục tiêu radar của một số mạng nơ-ron hiện đại". Tạp chí Nghiên cứu KH&CN quân sự, số 74. (2021).
[4]. Woosuk Kim 1, Hyunwoong Cho 1, Jongseok Kim 1, Byungkwan Kim 2 and Seongwook Lee “YOLO-Based Simultaneous Target Detection and Classification in Automotive FMCW Radar Systems”. MDPI, 20 May, (2020). DOI: https://doi.org/10.3390/s20102897
[5]. Long, X., Deng, K., Wang, G., Zhang, Y., Dang, Q., Gao, Y.,... & Wen, S., “PP-YOLO: An Effective and Efficient Implementation of Object Detector”. arXiv preprint arXiv:2007.12099, (2020).
[6]. Liu, Y., Wang, Y., Wang, S., Liang, T., Zhao, Q., Tang, Z., & Ling, H., CBNet: “A Novel Composite Backbone Network Architecture for Object Detection”. In AAAI, pp. 11653- 1660, (2020). DOI: https://doi.org/10.1609/aaai.v34i07.6834
[7]. A. Bochkovskiy, C. Y. Wang, and H. M. Liao, “Yolov4: Optimal speed and accuracy of object detection,” arXiv preprint arXiv:2004.10934, (2020).
[8]. Yu, J., Jiang, Y., Wang, Z., Cao, Z., & Huang, T., “Unitbox: An advanced object detection network”. In Proceedings of the 24th 15 ACM international conference on Multimedia, pp. 516-520, (2016). DOI: https://doi.org/10.1145/2964284.2967274
[9]. Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., & Ren, D. “Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression”. In AAAI, pp. 12993-13000, (2020). DOI: https://doi.org/10.1609/aaai.v34i07.6999
[10]. Tomasz Jasinski, Irina Antipov, Sildomar T. Monteiro, Graham Brooker “W-Band Maritime Target Classification using HighResolution Range Profiles”. The University of Sydney NSW, Australia, (2006).
[11]. Villeval, S.; Bilik, I.; Gurbuz, S.Z. “Application of a 24 GHz FMCW automotive radar for urban target classification”. In Proceedings of the IEEE Radar Conference, Cincinnati, OH, USA, 19–23, (2014). DOI: https://doi.org/10.1109/RADAR.2014.6875787
[12]. Rytel-Andrianik, R.; Samczynski, P.; Gromek, D.; Weilgo, J.; Drozdowicz, J.; Malanowski, M. “Micro-range, micro-Doppler joint analysis of pedestrian radar echo”. In Proceedings of the IEEE Signal Processing Symposium (SPSympo), Debe, Poland, 10–12 June, (2015). DOI: https://doi.org/10.1109/SPS.2015.7168298
[13]. Lim, S.; Lee, S.; Yoon, J.; Kim, S.-C. “Phase-based target classification using neural network in automotive radar systems”. In Proceedings of the IEEE Radar Conference (RadarConf), Boston, MA, USA, 22–26 April, (2019). DOI: https://doi.org/10.1109/RADAR.2019.8835725
[14]. Shang Jiang, Haoran Qin, Bingli Zhang, Jieyu Zheng. “Optimized Loss Functions for Object detection: A Case Study on Nighttime Vehicle Detection”, Computer Vision and Pattern Recognition, (2020).
[15]. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in IEEE conference on computer vision and pattern recognition, pp. 770–778, (2016).
[16]. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in IEEE conference on Computer Vision and Pattern Recognition, pp. 779–788, (2016). DOI: https://doi.org/10.1109/CVPR.2016.91
