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However, when the range precision surpasses the range resolution, it leads to a rapid increase in the number of labels, resulting in elevated learning costs. The removal of background noise in indoor environments is also crucial. In response, this study proposes a methodology aiming to increase range precision while mitigating the issue of a growing number of labels in supervised learning. Neural networks learned for a specific section are reused to minimize learning costs and maximize computational efficiency. Formulas and experiments confirmed that identical fractional multiple patterns in the frequency domain can be applied to analyze patterns in other FFT bin positions (representing different target positions). In conclusion, the results suggest that neural networks trained with the same data can be repurposed, enabling efficient hardware implementation.<\/jats:p>","DOI":"10.3390\/s24010136","type":"journal-article","created":{"date-parts":[[2023,12,27]],"date-time":"2023-12-27T02:58:12Z","timestamp":1703645892000},"page":"136","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["FMCW Radar Sensors with Improved Range Precision by Reusing the Neural Network"],"prefix":"10.3390","volume":"24","author":[{"given":"Homin","family":"Cho","sequence":"first","affiliation":[{"name":"Department of Semiconductor Systems Engineering, Sejong University, Gunja-dong, Gwangjin-gu, Seoul 05006, Republic of Korea"},{"name":"Department of Convergence Engineering of Intelligent Drone, Sejong University, Gunja-dong, Gwangjin-gu, Seoul 05006, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2299-9911","authenticated-orcid":false,"given":"Yunho","family":"Jung","sequence":"additional","affiliation":[{"name":"Department of Smart Drone Convergence, Korea Aerospace University, Goyang 10540, Gyeonggi-do, Republic of Korea"},{"name":"School of Electronics and Information Engineering, Korea Aerospace University, Goyang 10540, Gyeonggi-do, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9344-7052","authenticated-orcid":false,"given":"Seongjoo","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Semiconductor Systems Engineering, Sejong University, Gunja-dong, Gwangjin-gu, Seoul 05006, Republic of Korea"},{"name":"Department of Convergence Engineering of Intelligent Drone, Sejong University, Gunja-dong, Gwangjin-gu, Seoul 05006, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,26]]},"reference":[{"key":"ref_1","unstructured":"Qi, G. (September, January 31). High accuracy range estimation of FMCW level radar based on the phase of the zero-padded FFT. Proceedings of the 7th International Conference on Signal Processing, 2004. Proceedings. ICSP '04. 2004, Beijing, China."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Baek, S., Jung, Y., and Lee, S. (2021). Signal Expansion Method in Indoor FMCW Radar Systems for Improving Range Resolution. Sensors, 21.","DOI":"10.3390\/s21124226"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3059","DOI":"10.1109\/JSEN.2022.3227025","article-title":"Single 24-GHz FMCW Radar-Based Indoor Device-Free Human Localization and Posture Sensing with CNN","volume":"23","author":"Yang","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Li, W., Li, Y., Zhang, J., Lu, J., Dong, S., Gu, C., and Mao, J. (2023, January 11\u201316). A Feature-based Filtering Algorithm with 60GHz MIMO FMCW Radar for Indoor Detection and Trajectory Tracking. Proceedings of the 2023 IEEE\/MTT-S International Microwave Symposium\u2014IMS 2023, San Diego, CA, USA.","DOI":"10.1109\/IMS37964.2023.10188205"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Sharma, P., Gaba, S.P., and Singh, D. (2015, January 13\u201315). Study of background subtraction for ground penetrating radar. Proceedings of the 2015 National Conference on Recent Advances in Electronics & Computer Engineering (RAECE), Roorkee, India.","DOI":"10.1109\/RAECE.2015.7510234"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2609873","DOI":"10.1155\/2016\/2609873","article-title":"A General Range-Velocity Processing Scheme for Discontinuous Spectrum FMCW Signal in HFSWR Applications","volume":"2016","author":"Pan","year":"2016","journal-title":"Int. J. Antennas Propag."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Hartmann, S., and Kern-Isberner, G. (2008). FoIKS 2008: Foundations of Information and Knowledge Systems, Springer. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-540-77684-0"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"83329","DOI":"10.1109\/ACCESS.2021.3087207","article-title":"Automatic Label Creation Framework for FMCW Radar Images Using Camera Data","volume":"9","author":"Mendez","year":"2021","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kim, J., Ju, J., Feldt, R., and Yoo, S. (2020, January 8\u201313). Reducing DNN labelling cost using surprise adequacy: An industrial case study for autonomous driving. Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC\/FSE 2020), Online.","DOI":"10.1145\/3368089.3417065"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Liu, J., Gu, C., Zhang, Y., and Mao, J.-F. (2020, January 8\u201311). Suppressing Coupling and Stationary Clutters in FMCW Radars with Temporal Filtering. Proceedings of the 2020 IEEE Asia-Pacific Microwave Conference (APMC), Hong Kong, China.","DOI":"10.1109\/APMC47863.2020.9331488"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Park, K.-E., Lee, J.-P., and Kim, Y. (2021). Deep Learning-Based Indoor Distance Estimation Scheme Using FMCW Radar. Information, 12.","DOI":"10.3390\/info12020080"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Knott, E.F., Schaeffer, J.F., and Tulley, M.T. (2004). Radar Cross Section, SciTech Publishing.","DOI":"10.1049\/SBRA026E"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Park, H., Kim, M., Jung, Y., and Lee, S. (2022). Method for Improving Range Resolution of Indoor FMCW Radar Systems Using DNN. Sensors, 22.","DOI":"10.3390\/s22218461"},{"key":"ref_14","unstructured":"Touretzky, D. (1990). Advances in Neural Information Processing Systems 2, Morgan Kaufmann."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-Based Learning Applied to Document Recognition","volume":"86","author":"Bottou","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_16","unstructured":"Murphy, K.P. (2012). Machine Learning: A Probabilistic Perspective, MIT Press."},{"key":"ref_17","unstructured":"Glorot, X., and Bengio, Y. (2010, January 13\u201315). Understanding the Difficulty of Training Deep Feedforward Neural Networks. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, AISTATS, Sardinia, Italy."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 7\u201313). Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. Proceedings of the 2015 IEEE International Conference on Computer Vision, Cambridge, MA, USA.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_19","unstructured":"Ioffe, S., and Szegedy, C. (2015). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv."},{"key":"ref_20","unstructured":"Nair, V., and Hinton, G.E. (2010, January 21\u201324). Rectified linear units improve restricted boltzmann machines. Proceedings of the 27th International Conference on Machine Learning (ICML-10), Haifa, Israel."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Nagi, J., Ducatelle, F., Di Caro, G.A., Ciresan, D., Meier, U., Giusti, A., Nagi, F., Schmidhuber, J., and Gambardella, L.M. (2011, January 16\u201318). Max-Pooling Convolutional Neural Networks for Vision-based Hand Gesture Recognition. Proceedings of the IEEE International Conference on Signal and Image Processing Applications (ICSIPA2011), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICSIPA.2011.6144164"},{"key":"ref_22","unstructured":"Saxe, A.M., McClelland, J.L., and Ganguli, S. (2013). Exact solutions to the nonlinear dynamics of learning in deep linear neural networks. arXiv."},{"key":"ref_23","unstructured":"Bishop, C.M. (2006). Pattern Recognition and Machine Learning, Springer."},{"key":"ref_24","unstructured":"RFbeam (2023, November 10). K-MD2 Radar Transceiver. Available online: https:\/\/rfbeam.ch\/download\/k-md2-datasheet\/?tmstv=1696875210."},{"key":"ref_25","unstructured":"Krishnapura, N., Pavan, S., Mathiazhagan, C., and Ramamurthi, B. (June, January 31). A baseband pulse shaping filter for Gaussian minimum shift keying. Proceedings of the 1998 IEEE International Symposium on Circuits and Systems (ISCAS), Monterey, CA, USA."},{"key":"ref_26","unstructured":"Rappaport, T.S. (2002). Wireless Communications: Principles and Practice, Prentice Hall. [2nd ed.]."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"9294","DOI":"10.1109\/JSEN.2019.2923053","article-title":"Reconfigurable Range-Doppler Processing and Range Resolution Improvement for FMCW Radar","volume":"19","author":"Neemat","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Kim, B.-S., Lee, J., and Kim, S. (2023). SNR and Resolution Improvement Algorithm with the Concatenation of Multiple Chirps for FMCW Radar. IEEE Antennas Wirel. Propag. Lett. (Early Access Article), 1\u20135.","DOI":"10.1109\/LAWP.2023.3317872"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Park, K., Lee, J., and Kim, Y. (2021). Deep Learning-Based Indoor Two-Dimensional Localization Scheme Using a Frequency-Modulated Continuous Wave Radar. 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