{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T15:35:48Z","timestamp":1785512148929,"version":"3.56.0"},"reference-count":74,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,5,1]],"date-time":"2022-05-01T00:00:00Z","timestamp":1651363200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Development of doctoral studies","award":["09.3.3-ESFA-V-711-01-0001"],"award-info":[{"award-number":["09.3.3-ESFA-V-711-01-0001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Pedestrian occurrences in images and videos must be accurately recognized in a number of applications that may improve the quality of human life. Radar can be used to identify pedestrians. When distinct portions of an object move in front of a radar, micro-Doppler signals are produced that may be utilized to identify the object. Using a deep-learning network and time\u2013frequency analysis, we offer a method for classifying pedestrians and animals based on their micro-Doppler radar signature features. Based on these signatures, we employed a convolutional neural network (CNN) to recognize pedestrians and animals. The proposed approach was evaluated on the MAFAT Radar Challenge dataset. Encouraging results were obtained, with an AUC (Area Under Curve) value of 0.95 on the public test set and over 0.85 on the final (private) test set. The proposed DNN architecture, in contrast to more common shallow CNN architectures, is one of the first attempts to use such an approach in the domain of radar data. The use of the synthetic radar data, which greatly improved the final result, is the other novel aspect of our work.<\/jats:p>","DOI":"10.3390\/s22093456","type":"journal-article","created":{"date-parts":[[2022,5,3]],"date-time":"2022-05-03T08:26:35Z","timestamp":1651566395000},"page":"3456","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Pedestrian and Animal Recognition Using Doppler Radar Signature and Deep Learning"],"prefix":"10.3390","volume":"22","author":[{"given":"Danny","family":"Buchman","sequence":"first","affiliation":[{"name":"Department of Applied Informatics, Vytautas Magnus University, 44404 Kaunas, Lithuania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michail","family":"Drozdov","sequence":"additional","affiliation":[{"name":"JVC Sonderus, 05200 Vilnius, Lithuania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8509-420X","authenticated-orcid":false,"given":"Tomas","family":"Krilavi\u010dius","sequence":"additional","affiliation":[{"name":"Department of Applied Informatics, Vytautas Magnus University, 44404 Kaunas, Lithuania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2809-2213","authenticated-orcid":false,"given":"Rytis","family":"Maskeli\u016bnas","sequence":"additional","affiliation":[{"name":"Department of Applied Informatics, Vytautas Magnus University, 44404 Kaunas, Lithuania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9990-1084","authenticated-orcid":false,"given":"Robertas","family":"Dama\u0161evi\u010dius","sequence":"additional","affiliation":[{"name":"Department of Applied Informatics, Vytautas Magnus University, 44404 Kaunas, Lithuania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Guo, Z., Huang, Y., Hu, X., Wei, H., and Zhao, B. (2021). A survey on deep learningbased approaches for scene understanding in autonomous driving. Electronics, 10.","DOI":"10.3390\/electronics10040471"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.dcan.2017.10.002","article-title":"Machine learning for internet of things data analysis: A survey","volume":"4","author":"Mahdavinejad","year":"2018","journal-title":"Digit. Commun. Netw."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Hua, J., Zeng, L., Li, G., and Ju, Z. (2021). Learning for a robot: Deep reinforcement learning, imitation learning, transfer learning. Sensors, 21.","DOI":"10.3390\/s21041278"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Mauri, A., Khemmar, R., Decoux, B., Ragot, N., Rossi, R., Trabelsi, R., Boutteau, R., Ertaud, J., and Savatier, X. (2020). Deep learning for real-time 3D multi-object detection, localisation, and tracking: Application to smart mobility. Sensors, 20.","DOI":"10.3390\/s20020532"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1865","DOI":"10.1109\/JPROC.2017.2675998","article-title":"Remote Sensing Image Scene Classification: Benchmark and State of the Art","volume":"105","author":"Cheng","year":"2017","journal-title":"Proc. IEEE"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"103448","DOI":"10.1016\/j.artint.2020.103448","article-title":"Multiple object tracking: A literature review","volume":"293","author":"Luo","year":"2021","journal-title":"Artif. Intell."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Masood, H., Zafar, A., Ali, M.U., Hussain, T., Khan, M.A., Tariq, U., and Dama\u0161evi\u010dius, R. (2022). Tracking of a Fixed-Shape Moving Object Based on the Gradient Descent Method. Sensors, 22.","DOI":"10.3390\/s22031098"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ge, H., Zhu, Z., Lou, K., Wei, W., Liu, R., Dama\u0161evi\u010dius, R., and Wo\u017aniak, M. (2020). Classification of infrared objects in manifold space using kullback-leibler divergence of gaussian distributions of image points. Symmetry, 12.","DOI":"10.3390\/sym12030434"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhou, B., Duan, X., Ye, D., Wei, W., Wo\u017aniak, M., Po\u0142ap, D., and Dama\u0161evi\u010dius, R. (2019). Multi-level features extraction for discontinuous target tracking in remote sensing image monitoring. Sensors, 19.","DOI":"10.3390\/s19224855"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"32650","DOI":"10.1109\/ACCESS.2021.3060821","article-title":"Analysis Based on Recent Deep Learning Approaches Applied in Real-Time Multi-Object Tracking: A Review","volume":"9","author":"Kalake","year":"2021","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Mujahid, A., Awan, M.J., Yasin, A., Mohammed, M.A., Dama\u0161evi\u010dius, R., Maskeli\u016bnas, R., and Abdulkareem, K.H. (2021). Real-time hand gesture recognition based on deep learning YOLOv3 model. Appl. Sci., 11.","DOI":"10.3390\/app11094164"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ali, S.F., Aslam, A.S., Awan, M.J., Yasin, A., and Dama\u0161evi\u010dius, R. (2021). Pose estimation of driver\u2019s head panning based on interpolation and motion vectors under a boosting framework. Appl. Sci., 11.","DOI":"10.3390\/app112411600"},{"key":"ref_13","first-page":"4061","article-title":"Multi-Layered Deep Learning Features Fusion for Human Action Recognition","volume":"69","author":"Kiran","year":"2021","journal-title":"Comput. Mater. Contin."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"979","DOI":"10.1007\/s12652-019-01209-1","article-title":"Recognition of basketball referee signals from real-time videos","volume":"11","author":"Raudonis","year":"2020","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Patalas-maliszewska, J., Halikowski, D., and Dama\u0161evi\u010dius, R. (2021). An automated recognition of work activity in industrial manufacturing using convolutional neural networks. Electronics, 10.","DOI":"10.3390\/electronics10232946"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Huang, Q., Pan, C., and Liu, H. (2021, January 8\u201311). A Multi-sensor Fusion Algorithm for Monitoring the Health Condition of Conveyor Belt in Process Industry. Proceedings of the 2021 3rd International Conference on Industrial Artificial Intelligence (IAI), Shenyang, China.","DOI":"10.1109\/IAI53119.2021.9619194"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Bai, Z., Li, Y., Chen, X., Yi, T., Wei, W., Wozniak, M., and Damasevicius, R. (2020). Real-time video stitching for mine surveillance using a hybrid image registration method. Electronics, 9.","DOI":"10.3390\/electronics9091336"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4733","DOI":"10.1186\/s13673-020-00256-4","article-title":"Multiple Kinect based system to monitor and analyze key performance indicators of physical training","volume":"10","author":"Ryselis","year":"2020","journal-title":"Hum.-Centric Comput. Inf. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5259","DOI":"10.1007\/s10489-020-02047-x","article-title":"Occluded object tracking using object-background prototypes and particle filter","volume":"51","author":"Mondal","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1016\/j.imavis.2003.09.010","article-title":"A real-time system for monitoring of cyclists and pedestrians","volume":"22","year":"2004","journal-title":"Image Vis. Comput."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Peng, X., and Shan, J. (2021). Detection and tracking of pedestrians using doppler lidar. Remote Sens., 13.","DOI":"10.3390\/rs13152952"},{"key":"ref_22","unstructured":"Held, P., Steinhauser, D., Koch, A., Brandmeier, T., and Schwarz, U.T. (2021). A Novel Approach for Model-Based Pedestrian Tracking Using Automotive Radar. IEEE Trans. Intell. Transp. Syst., 1\u201314."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.eswa.2019.06.048","article-title":"Pedestrian recognition using micro Doppler effects of radar signals based on machine learning and multi-objective optimization","volume":"136","author":"Severino","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3726","DOI":"10.1109\/JSEN.2022.3141202","article-title":"Radar-Based Robust People Tracking and Consumer Applications","volume":"22","author":"Ninos","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5119","DOI":"10.1109\/JSEN.2020.3036047","article-title":"RAMP-CNN: A Novel Neural Network for Enhanced Automotive Radar Object Recognition","volume":"21","author":"Gao","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, Z., Miao, X., Huang, Z., and Luo, H. (2021). Research of target detection and classification techniques using millimeter-wave radar and vision sensors. Remote Sens., 13.","DOI":"10.3390\/rs13061064"},{"key":"ref_27","first-page":"113","article-title":"A method of feature selection in the aspect of specific identification of radar signals","volume":"65","author":"Dudczyk","year":"2017","journal-title":"Bull. Pol. Acad. Sci. Tech. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1109\/MAES.2016.140167","article-title":"A survey of radar systems for medical applications","volume":"31","author":"Pisa","year":"2016","journal-title":"IEEE Aerosp. Electron. Syst. Mag."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Cardillo, E., and Caddemi, A. (2019, January 4\u20136). Feasibility Study to Preserve the Health of an Industry 4.0 Worker: A Radar System for Monitoring the Sitting-Time. In Proceedings of the 2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0&IoT), Naples, Italy.","DOI":"10.1109\/METROI4.2019.8792905"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1993","DOI":"10.1109\/TITS.2016.2634580","article-title":"Video processing from electro-optical sensors for object detection and tracking in a maritime environment: A survey","volume":"18","author":"Prasad","year":"2017","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Mishra, A., and Li, C. (2021). A review: Recent progress in the design and development of nonlinear radars. Remote Sens., 13.","DOI":"10.3390\/rs13244982"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1140","DOI":"10.1049\/iet-rsn.2015.0118","article-title":"Review of micro-Doppler signatures","volume":"9","author":"Tahmoush","year":"2015","journal-title":"IET Radar Sonar Navig."},{"key":"ref_33","unstructured":"Anderson, S. (2004, January 11\u201313). Target Classification, Recognition and Identification with HF Radar. Proceedings of the NATO Research and Technology Agency, Sensors and Electronics Technology Panel Symposium SET\u2013080\/RSY17\/RFT: Target Identification and Recognition Using RF Systems, Oslo, Norway."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1109\/MAES.2006.275302","article-title":"Review of airport surface movement radar technology","volume":"21","author":"Perl","year":"2006","journal-title":"IEEE Aerosp. Electron. Syst. Mag."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1109\/JOE.2017.2758858","article-title":"Theoretical Performance of Space-Time Adaptive Processing for Ship Detection by High-Frequency Surface Wave Radars","volume":"43","author":"Gorski","year":"2018","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Coluccia, A., Parisi, G., and Fascista, A. (2020). Detection and classification of multirotor drones in radar sensor networks: A review. Sensors, 20.","DOI":"10.3390\/s20154172"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1276","DOI":"10.1049\/iet-rsn.2015.0125","article-title":"Micro-Doppler signatures of helicopters in multistatic passive radars","volume":"9","author":"Baczyk","year":"2015","journal-title":"IET Radar Sonar Navig."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhou, T., Yang, M., Jiang, K., Wong, H., and Yang, D. (2020). Mmw radar-based technologies in autonomous driving: A review. Sensors, 20.","DOI":"10.3390\/s20247283"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"102702","DOI":"10.1016\/j.dsp.2020.102702","article-title":"Micro-Doppler based target classification in ground surveillance radar systems","volume":"101","author":"Amiri","year":"2020","journal-title":"Digit. Signal Process. Rev. J."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1109\/LRA.2020.2967272","article-title":"CNN Based Road User Detection Using the 3D Radar Cube","volume":"5","author":"Palffy","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1933","DOI":"10.1109\/LGRS.2015.2439393","article-title":"Classification of Unarmed\/Armed Personnel Using the NetRAD Multistatic Radar for Micro-Doppler and Singular Value Decomposition Features","volume":"12","author":"Fioranelli","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2011RS004662","article-title":"Radar target classification method with high accuracy and decision speed performance using MUSIC spectrum vectors and PCA projection","volume":"46","author":"Secmen","year":"2011","journal-title":"Radio Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2304","DOI":"10.1109\/TAES.2014.130082","article-title":"Robust PCA micro-doppler classification using SVM on embedded systems","volume":"50","author":"Zabalza","year":"2014","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Jiang, W., Ren, Y., Liu, Y., and Leng, J. (2022). Artificial Neural Networks and Deep Learning Techniques Applied to Radar Target Detection: A Review. Electronics, 11.","DOI":"10.3390\/electronics11010156"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.eswa.2018.02.019","article-title":"Micro-Doppler radar classification of humans and animals in an operational environment","volume":"102","author":"Berndt","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"8161","DOI":"10.1109\/JSEN.2021.3050262","article-title":"Deep Learning-Based Subsurface Target Detection from GPR Scans","volume":"21","author":"Hou","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Abdu, F.J., Zhang, Y., Fu, M., Li, Y., and Deng, Z. (2021). Application of deep learning on millimeter-wave radar signals: A review. Sensors, 21.","DOI":"10.3390\/s21061951"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1152","DOI":"10.1109\/JSEN.2020.3020626","article-title":"Deep 3D Object Detection Networks Using LiDAR Data: A Review","volume":"21","author":"Wu","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_49","unstructured":"(2022, January 16). MAFAT Radar Challenge Homepage, Available online: https:\/\/competitions.codalab.org\/competitions\/25389#learn_the_details-overview."},{"key":"ref_50","unstructured":"Jianjun, H., Jingxiong, H., and Xie, W. (1996, January 8\u201310). Target Classification by Conventional Radar. Proceedings of the International Radar Conference, Beijing, China."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"23","DOI":"10.3844\/jcssp.2009.23.32","article-title":"Artificial Neural Network Approach in Radar Target Classification","volume":"5","author":"Ibrahim","year":"2009","journal-title":"J. Comput. Sci."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Ardon, G., Simko, O., and Novoselsky, A. (2020, January 22\u201324). Aerial Radar Target Classification using Artificial Neural Networks. Proceedings of the ICPRAM, Valletta, Malta.","DOI":"10.5220\/0008911701360141"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Gadde, A., Amin, M.G., Zhang, Y.D., and Ahmad, F. (2014, January 29). Fall detection and classifications based on time-scale radar signal characteristics. Proceedings of the SPIE\u2014The International Society for Optical Engineering, Baltimore, MD, USA.","DOI":"10.1117\/12.2050998"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Ma, Y., Anderson, J., Crouch, S., and Shan, J. (2019). Moving object detection and tracking with doppler LiDAR. Remote Sens., 11.","DOI":"10.3390\/rs11101154"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Han, H., Kim, J., Park, J., Lee, Y., Jo, H., Park, Y., Matson, E., and Park, S. (2019, January 11\u201313). Object classification on raw radar data using convolutional neural networks. Proceedings of the 2019 IEEE Sensors Applications Symposium (SAS), Sophia Antipolis, Valbonne, France.","DOI":"10.1109\/SAS.2019.8706004"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"19529","DOI":"10.1109\/JSEN.2021.3092002","article-title":"Data-Driven Radar Processing Using a Parametric Convolutional Neural Network for Human Activity Classification","volume":"21","author":"Stadelmayer","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"4962","DOI":"10.1186\/s13634-019-0603-y","article-title":"Convolutional neural networks for radar HRRP target recognition and rejection","volume":"2019","author":"Wan","year":"2019","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Dadon, Y.D., Yamin, S., Feintuch, S., Permuter, H.H., Bilik, I., and Taberkian, J. (2021, January 8\u201314). Moving Target Classification Based on micro-Doppler Signatures Via Deep Learning. Proceedings of the IEEE National Radar Conference\u2014Proceedings, Atlanta, GA, USA.","DOI":"10.1109\/RadarConf2147009.2021.9455270"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Tiwari, A., Goomer, R., Yenneti, S.S.S., Mehta, S., and Mishra, V. (2021, January 27\u201329). Classification of Humans and Animals from Radar Signals using Multi-Input Mixed Data Model. Proceedings of the 2021 International Conference on Computer Communication and Informatics, ICCCI 2021, Rhodes, Greece.","DOI":"10.1109\/ICCCI50826.2021.9402280"},{"key":"ref_60","unstructured":"Chen, V.C., and Ling, H. (2001). Time-Frequency Transforms for Radar Imaging and Signal Analysis, Artech House."},{"key":"ref_61","unstructured":"Chen, V.C. (2011). The Micro-Doppler Effect in Radar, Artech House."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1109\/TAES.2006.1603422","article-title":"Gmm-based target classification for ground surveillance doppler radar","volume":"42","author":"Bilik","year":"2006","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"Imagenet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_64","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press. Available online: http:\/\/www.deeplearningbook.org."},{"key":"ref_65","unstructured":"Lee, C.-Y., Gallagher, P.W., and Tu, Z. (2022, January 16). Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree. Available online: https:\/\/ieeexplore.ieee.org\/document\/7927440."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Dodge, S.F., and Karam, L.J. (August, January 31). A study and comparison of human and deep learning recognition performance under visual distortions. Proceedings of the 2017 26th International Conference on Computer Communication and Networks (ICCCN), Vancouver, BC, Canada.","DOI":"10.1109\/ICCCN.2017.8038465"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"He, K.E.A. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1145\/1007730.1007735","article-title":"A study of the behavior of several methods for balancing machine learning training data","volume":"6","author":"Batista","year":"2004","journal-title":"SIGKDD Explor. Newsl."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1016\/S0031-3203(96)00142-2","article-title":"The use of the area under the roc curve in the evaluation of machine learning algorithms","volume":"30","author":"Bradley","year":"1997","journal-title":"Pattern Recog."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015). Deep Residual Learning for Image Recognition. arXiv.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_71","unstructured":"Kingma, D.P., and Ba, J. (2022, February 16). Adam: A Method for Stochastic Optimization, Available online: http:\/\/xxx.lanl.gov\/abs\/1412.6980."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Smith, L. (2017, January 24\u201331). Cyclical Learning Rates for Training Neural Networks. Proceedings of the 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), Santa Rosa, CA, USA.","DOI":"10.1109\/WACV.2017.58"},{"key":"ref_73","unstructured":"Kultavewuti, P. (2022, January 16). One Cycle & Cyclic Learning Rate for Keras. Available online: https:\/\/github.com\/psklight\/keras_one_cycle_clr."},{"key":"ref_74","unstructured":"Axon Pulse (2022, January 16). MAFAT Radar Challenge: Solution by Axon Pulse. Available online: https:\/\/medium.com\/axon-pulse\/mafat-radar-challenge-solution-by-axon-pulse-a4f082e62b3e."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/9\/3456\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:05:28Z","timestamp":1760137528000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/9\/3456"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,1]]},"references-count":74,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["s22093456"],"URL":"https:\/\/doi.org\/10.3390\/s22093456","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,1]]}}}