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http://dx.doi.org/10.6109/jkiice.2022.26.1.104

Deep learning-based target distance and velocity estimation technique for OFDM radars  

Choi, Jae-Woong (Department of Mobile Convergence and Engineering, Hanbat National University)
Jeong, Eui-Rim (Department of Information and Communication Engineering, Hanbat National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.26, no.1, 2022 , pp. 104-113 More about this Journal
Abstract
In this paper, we propose deep learning-based target distance and velocity estimation technique for OFDM radar systems. In the proposed technique, the 2D periodogram is obtained via 2D fast Fourier transform (FFT) from the reflected signal after removing the modulation effect. The periodogram is the input to the conventional and proposed estimators. The peak of the 2D periodogram represents the target, and the constant false alarm rate (CFAR) algorithm is the most popular conventional technique for the target's distance and speed estimation. In contrast, the proposed method is designed using the multiple output convolutional neural network (CNN). Unlike the conventional CFAR, the proposed estimator is easier to use because it does not require any additional information such as noise power. According to the simulation results, the proposed CNN improves the mean square error (MSE) by more than 5 times compared with the conventional CFAR, and the proposed estimator becomes more accurate as the number of transmitted OFDM symbols increases.
Keywords
OFDM radar; Multiple output CNN; Distance and velocity estimation; Deep learning;
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1 C. B. Barneto, T. Riihonen, M. Turunen, L. Anttila, M. Fleischer, K. Stadius, and M. Valkama, "Full-duplex OFDM radar with LTE and 5G NR waveforms: Challenges, solutions, and measurements," IEEE Transactions on Microwave Theory and Techniques, vol. 67, no. 10, pp. 4042-4054, Oct. 2019.   DOI
2 B. Major, D. Fontijne, A. Ansari, R. T. Sukhavasi, P. Gowaikar, M. Hamilton, S. Lee, S. Grechnik, and S. Subramanian, "Vehicle detection with automotive radar using deep learning on range-azimuth-doppler tensors," in Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, Seoul: KR, pp. 924-932, Oct. 2019.
3 B. Dekker, S. Jacobs, A. S. Kossen, M. C. Kruithof, A. G. Huizing, and M. Geurts, "Gesture recognition with a low power FMCW radar and a deep convolutional neural network," in 2017 European Radar Conference (EURAD), Nuremberg: DE, pp. 163-166, Oct. 2017.
4 L. Zhang, W. You, Q. Wu, S. Qi, and Y. Ji, "Deep learning-based automatic clutter/interference detection for HFSWR," Remote Sensing, vol. 10, no. 10, pp. 1517, Sep. 2018.   DOI
5 S. Mercier, S. Bidon, D. Roque, and C. Enderli, "Comparison of correlation-based OFDM radar receivers," IEEE Transactions on Aerospace and Electronic Systems, vol. 56, no. 6, pp. 4796-4813, Dec. 2020.   DOI
6 J. Joung, S. Jung, S. Chung, and E. R. Jeong, "CNN-based Tx-Rx distance estimation for UWB system localisation," Electronics Letters, vol. 55, no. 17, pp. 938-940, Aug. 2019.   DOI
7 Y. Kawamoto, H. Takagi, H. Nishiyama, and N. Kato, "Efficient resource allocation utilizing Q-Learning in multiple UA communications," in IEEE Transactions on Network Science and Engineering, vol. 6, no. 3, pp. 293-302, Mar. 2019.   DOI
8 G. M. Nam, T. Y. Jung, S. H. Jung, and E. R. Jeong, "Distance estimation using convolutional neural network in UWB systems," Institute of Information and Communication Engineering, vol. 23, no. 10, pp. 640-651, Oct. 2019.
9 K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas: LV, pp. 770-778, Jun. 2016.
10 W. Na, S. Jang, Y. Lee, L. Park, N. Dao, and S. Cho, "Frequency resource allocation and interference management in mobile edge computing for an Internet of Things system," IEEE Internet of Things Journal, vol. 6, no. 3, pp. 4910-4920, Jun. 2019.   DOI
11 K. Satyanarayana, M. El-Hajjar, P. H. Kuo, and L. Hanzo, "Hybrid beamforming design for full-duplex millimeter wave communication," IEEE Transactions on Vehicular Technology, vol. 68, no. 2, pp. 1394-1404, Jun. 2017.   DOI
12 M. Braun, M. Muller, M. Fuhr, and F. K. Jondral, "A USRP-based testbed for OFDM-based radar and communication systems," in Proceedings of 22nd Virginia Tech. Symposium on Wireless Communications, Blacksburg: VA, Jun. 2012.
13 A. Evers and J. A. Jackson, "Analysis of an LTE waveform for radar applications," in 2014 IEEE Radar Conference, Cincinnati: OH, pp. 0200-0205, May. 2014.
14 T. Wild, V. Braun, and H. Viswanathan, "Joint design of communication and sensing for beyond 5G and 6G systems," IEEE Access, vol. 9, pp. 30845-30857, Feb. 2021.   DOI