{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:32:07Z","timestamp":1760146327299,"version":"build-2065373602"},"reference-count":71,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T00:00:00Z","timestamp":1730246400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002347","name":"Federal Ministry of Education and Research, Germany [Bundesministerium f\u00fcr Bildung und Forschung (BMBF)]","doi-asserted-by":"publisher","award":["16KISK084"],"award-info":[{"award-number":["16KISK084"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>FMCW radar systems are increasingly used in diverse applications, and emerging technologies like JCAS offer new opportunities. However, machine learning for radar faces challenges due to limited application-specific datasets, often requiring advanced simulations to supplement real-world data. This paper presents a setup for generating synthetic radar data for indoor environments, evaluated using CNNs. The setup involves comprehensive modeling, including far-field antenna simulations, variations in human radar cross-section, and detailed representations of indoor environments with their corresponding propagation channel properties. These synthetic data are used to train CNNs, and their performance is assessed on real measurement data. The results demonstrate that CNNs trained on synthetic data can perform well when tested on real measurement data. Specifically, the models trained with synthetic data showed performance comparable to models trained with real measurement data, which required a minimum of 300 samples to reach similar levels of accuracy. This result demonstrates that synthetic data can effectively train neural networks, providing an alternative to real measurement data, particularly when collecting sufficient real-world samples is difficult or costly. This approach significantly reduces the time required for generating datasets, and the ability to quickly label data in simulations simplifies and accelerates post-processing. Additionally, the generated datasets can be made more heterogeneous by introducing varying signal conditions, enhancing the diversity and robustness of the training data.<\/jats:p>","DOI":"10.3390\/rs16214028","type":"journal-article","created":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T12:25:39Z","timestamp":1730291139000},"page":"4028","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Accelerating Deep Learning in Radar Systems: A Simulation Framework for 60 GHz Indoor Radar"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-4547-6050","authenticated-orcid":false,"given":"Philipp","family":"Reitz","sequence":"first","affiliation":[{"name":"Institute for Smart Electronics and Systems, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, 91058 Erlangen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2586-4732","authenticated-orcid":false,"given":"Timo","family":"Maiwald","sequence":"additional","affiliation":[{"name":"Institute for Smart Electronics and Systems, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, 91058 Erlangen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0130-1998","authenticated-orcid":false,"given":"Jonas","family":"B\u00f6nsch","sequence":"additional","affiliation":[{"name":"Institute for Smart Electronics and Systems, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, 91058 Erlangen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2777-4722","authenticated-orcid":false,"given":"Norman","family":"Franchi","sequence":"additional","affiliation":[{"name":"Institute for Smart Electronics and Systems, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, 91058 Erlangen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6527-8925","authenticated-orcid":false,"given":"Maximilian","family":"L\u00fcbke","sequence":"additional","affiliation":[{"name":"Institute for Smart Electronics and Systems, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, 91058 Erlangen, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"012032","DOI":"10.1088\/1742-6596\/2093\/1\/012032","article-title":"Research on Comparison of LiDAR and Camera in Autonomous Driving","volume":"2093","author":"Wang","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Mielle, M., Magnusson, M., and Lilienthal, A.J. (2019, January 4\u20136). A comparative analysis of radar and lidar sensing for localization and mapping. Proceedings of the 2019 European Conference on Mobile Robots (ECMR), Prague, Czech Republic.","DOI":"10.1109\/ECMR.2019.8870345"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1109\/MITS.2022.3162886","article-title":"Comparative Analysis of Radar and Lidar Technologies for Automotive Applications","volume":"15","author":"Bilik","year":"2023","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"102643","DOI":"10.1109\/ACCESS.2023.3313505","article-title":"A Secure and Resilient 6G Architecture Vision of the German Flagship Project 6G-ANNA","volume":"11","author":"Hoffmann","year":"2023","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Albuquerque, D., Cruz, B., Gouveia, C., Coelho, V., Pinho, P., Matos, J., Oliveira, A., and Carvalho, N. (2022, January 20\u201322). Indoor Near-Field Impact in the RADAR Signals for 6G Mobile Networks Integration. Proceedings of the 2022 13th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP), Porto, Portugal.","DOI":"10.1109\/CSNDSP54353.2022.9907954"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yang, Y., Wang, Y., Zheng, B., Yi, X., Che, W., and Xue, Q. (2021, January 28\u201330). Challenges of Joint Radar-Communication Front-End for 6G Applications. Proceedings of the 2021 IEEE International Workshop on Electromagnetics: Applications and Student Innovation Competition (iWEM), Guangzhou, China.","DOI":"10.1109\/iWEM53379.2021.9790573"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhang, A., Nowruzi, F.E., and Laganiere, R. (2021, January 26\u201328). RADDet: Range-Azimuth-Doppler based Radar Object Detection for Dynamic Road Users. Proceedings of the 2021 18th Conference on Robots and Vision (CRV), Burnaby, BC, Canada.","DOI":"10.1109\/CRV52889.2021.00021"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"97147","DOI":"10.1109\/ACCESS.2023.3312382","article-title":"Radars for Autonomous Driving: A Review of Deep Learning Methods and Challenges","volume":"11","author":"Srivastav","year":"2023","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Neekzad, B., Sayrafian-Pour, K., Perez, J., and Baras, J.S. (2007, January 3\u20137). Comparison of Ray Tracing Simulations and Millimeter Wave Channel Sounding Measurements. Proceedings of the 2007 IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications, Athens, Greece.","DOI":"10.1109\/PIMRC.2007.4394537"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Placidi, S., Vetere, A., Pino, E., and Meta, A. (2021, January 1\u20133). Advanced SAR simulator for ATR and AI database generation. Proceedings of the 2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR), Bali, Indonesia.","DOI":"10.1109\/APSAR52370.2021.9688497"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Nguyen Ngoc, T.M., Linh, M., Dinh Uyen, N., and Van Su, T. (2015, January 14\u201316). A 3D model to characterize EM far-field scattering and its applications in SAR data synthesis. Proceedings of the 2015 International Conference on Advanced Technologies for Communications (ATC), Ho Chi Minh City, Vietnam.","DOI":"10.1109\/ATC.2015.7388408"},{"key":"ref_12","unstructured":"Reitz, P., Maiwald, T., Franchi, N., Weigel, R., and L\u00fcbke, M. (2024, January 2\u20134). Evaluating Synthetic Data Potential for 60 GHz FMCW Radar Simulations with Measurements. Proceedings of the 2024 International Radar Symposium (IRS), Wroclaw, Poland."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4704","DOI":"10.1109\/LRA.2021.3068916","article-title":"Virtual Radar: Real-Time Millimeter-Wave Radar Sensor Simulation for Perception-Driven Robotics","volume":"6","author":"Ubezio","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Stetco, C., Ubezio, B., M\u00fchlbacher-Karrer, S., and Zangl, H. (August, January 31). Radar Sensors in Collaborative Robotics: Fast Simulation and Experimental Validation. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France.","DOI":"10.1109\/ICRA40945.2020.9197180"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Schouten, G., Jansen, W., and Steckel, J. (2021). Simulation of Pulse-Echo Radar for Vehicle Control and SLAM. Sensors, 21.","DOI":"10.3390\/s21020523"},{"key":"ref_16","unstructured":"Abadpour, S. (2023). Modeling Backscattering Behavior of Vulnerable Road Users Based on High-Resolution Radar Measurements. [Ph.D. Thesis, KIT Scientific Publishing]."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Belgiovane, D., Chen, C.C., Chen, M., Chien, S.Y.P., and Sherony, R. (2014, January 19\u201323). 77 GHz radar scattering properties of pedestrians. Proceedings of the 2014 IEEE Radar Conference, Cincinnati, OH, USA.","DOI":"10.1109\/RADAR.2014.6875687"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"H\u00fcgler, P., Geiger, M., and Waldschmidt, C. (2016, January 14\u201316). RCS measurements of a human hand for radar-based gesture recognition at E-band. Proceedings of the 2016 German Microwave Conference (GeMiC), Bochum, Germany.","DOI":"10.1109\/GEMIC.2016.7461605"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1582","DOI":"10.1109\/TMTT.2021.3131156","article-title":"Backscattering Behavior of Vulnerable Road Users Based on High-Resolution RCS Measurements","volume":"70","author":"Abadpour","year":"2022","journal-title":"IEEE Trans. Microw. Theory Tech."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Bertram, T. (2021). Physics-Based, Real-Time MIMO Radar Simulation for Autonomous Driving. Proceedings of the Automatisiertes Fahren 2021, Springer Nature.","DOI":"10.1007\/978-3-658-34754-3"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"930","DOI":"10.1109\/JMW.2021.3103647","article-title":"Flexible Direction-of-Arrival Simulation for Automotive Radar Target Simulators","volume":"1","author":"Schoeder","year":"2021","journal-title":"IEEE J. Microwaves"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"7611","DOI":"10.1109\/TAES.2023.3291335","article-title":"Vehicular-Motion-Based DOA Estimation With a Limited Amount of Snapshots for Automotive MIMO Radar","volume":"59","author":"Yuan","year":"2023","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_23","first-page":"4239725","article-title":"From Antenna Design to High Fidelity, Full Physics Automotive Radar Sensor Corner Case Simulation","volume":"2018","author":"Chipengo","year":"2018","journal-title":"Model. Simul. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Diewald, A. (2023). High-Precision Automotive Radar Target Simulation. [Ph.D. Thesis, Institut f\u00fcr Hochfrequenztechnik und Elektronik (IHE)].","DOI":"10.1109\/RadarConf2351548.2023.10149775"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3500504","DOI":"10.1109\/LSENS.2024.3359693","article-title":"Virtually Augmented Radar Measurements with Hardware Radar Target Simulators for Machine Learning Applications","volume":"8","author":"Kern","year":"2024","journal-title":"IEEE Sens. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Moilanen, I., Lintonen, T., Kiviranta, M., Sangi, P., Pyhtil\u00e4, J., Pirinen, P., and Juntti, M. (2023, January 10\u201313). Ray Tracing Assisted Radar Detection in 6G. Proceedings of the 2023 IEEE 98th Vehicular Technology Conference (VTC2023-Fall), Hong Kong, China.","DOI":"10.1109\/VTC2023-Fall60731.2023.10333844"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Schuessler, C., Zhang, W., Br\u00e4unig, J., Hoffmann, M., Stelzig, M., and Vossiek, M. (2024, January 6\u201310). Radar-Based Recognition of Static Hand Gestures in American Sign Language. Proceedings of the 2024 IEEE Radar Conference (RadarConf24), Denver, CO, USA.","DOI":"10.1109\/RadarConf2458775.2024.10548292"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"10032","DOI":"10.1109\/JSEN.2020.2991741","article-title":"mm-Pose: Real-Time Human Skeletal Posture Estimation Using mmWave Radars and CNNs","volume":"20","author":"Sengupta","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"678","DOI":"10.1109\/TIV.2020.3048944","article-title":"Machine Learning-Based Target Classification for MMW Radar in Autonomous Driving","volume":"6","author":"Cai","year":"2021","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1653","DOI":"10.1007\/s11760-021-01901-w","article-title":"Machine learning-based radar waveform classification for cognitive EW","volume":"15","author":"Orduyilmaz","year":"2021","journal-title":"Signal Image Video Process."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"51470","DOI":"10.1109\/ACCESS.2020.2977922","article-title":"Machine Learning-Based Radar Perception for Autonomous Vehicles Using Full Physics Simulation","volume":"8","author":"Sligar","year":"2020","journal-title":"IEEE Access"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"16614","DOI":"10.1109\/JSEN.2024.3386221","article-title":"A Paradigm Shift From an Experimental-Based to a Simulation-Based Framework Using Motion-Capture Driven MIMO Radar Data Synthesis","volume":"24","author":"Waqar","year":"2024","journal-title":"IEEE Sens. J."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2512610","DOI":"10.1109\/TIM.2023.3272383","article-title":"A New Image Simulation Technique for Deep-Learning-Based Radar Target Recognition","volume":"72","author":"Dong","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Schnattinger, G., Baur, C., and Huber, B. (2023, January 20\u201322). Generating and Using Synthetic Data for Machine Learning in Personnel Security Screening Scenarios. Proceedings of the 2023 20th European Radar Conference (EuRAD), Berlin, Germany.","DOI":"10.23919\/EuRAD58043.2023.10289568"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"14276","DOI":"10.1109\/JSEN.2023.3276798","article-title":"A Simulation Method for Millimeter-Wave Radar Sensing in Traffic Intersection Based on Bidirectional Analytical Ray-Tracing Algorithm","volume":"23","author":"Zong","year":"2023","journal-title":"IEEE Sensors J."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Liu, L., Zhao, H., L\u00f3pez-Ben\u00edtez, M., Yu, L., and Yue, Y. (2022). Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges. Sensors, 22.","DOI":"10.3390\/s22114208"},{"key":"ref_37","unstructured":"Zhou, Y. (2024, October 23). GitHub\u2014ZHOUYI1023\/Awesome-Radar-Perception: A Curated List of Radar Datasets, Detection, Tracking and Fusion. Available online: https:\/\/github.com\/ZHOUYI1023\/awesome-radar-perception."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Herda, D.L., Suryana, J., and Izzuddin, A. (2020, January 3\u20134). Radar Cross Section of F35: Simulation and Measurement. Proceedings of the 2020 6th International Conference on Wireless and Telematics (ICWT), Yogyakarta, Indonesia.","DOI":"10.1109\/ICWT50448.2020.9243627"},{"key":"ref_39","unstructured":"PREDICS (2024, October 23). Radar Cross Section (RCS) Simulation Validations via Benchmark Objects. Available online: https:\/\/predicsrcs.com\/themes\/base\/assets\/image\/predics\/whitePapers\/PREDICS_WP1_RCS%20OF%20BENCHMARK%20OBJECTS.pdf."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"5155","DOI":"10.1109\/TAP.2013.2265253","article-title":"Rough Surface RCS Measurements and Simulations Using the Physical Optics Approximation","volume":"61","author":"Corbel","year":"2013","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2847","DOI":"10.1109\/TAP.2019.2894328","article-title":"Bistatic RCS Measurements of Large Targets in a Compact Range","volume":"67","author":"Potgieter","year":"2019","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Pienaar, M., Odendaal, J.W., Joubert, J., Pienaar, C., and Smit, J.C. (2017, January 11\u201315). Bistatic RCS measurements in a compact range. Proceedings of the 2017 International Conference on Electromagnetics in Advanced Applications (ICEAA), Verona, Italy.","DOI":"10.1109\/ICEAA.2017.8065484"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"L\u00fcbke, M., Fuchs, J., Dubey, A., Hamoud, H., Dressler, F., Weigel, R., and Lurz, F. (2021, January 27\u201330). Validation and Analysis of the Propagation Channel at 60 GHz for Vehicular Communication. Proceedings of the 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall), Norman, OK, USA.","DOI":"10.1109\/VTC2021-Fall52928.2021.9625066"},{"key":"ref_44","unstructured":"Altair (2024, October 23). User Guide Objects. Available online: https:\/\/help.altair.com\/feko\/pdf\/Altair_Feko_User_Guide.pdf."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1109\/MAP.2014.6931711","article-title":"RCS Patterns of Pedestrians at 76\u201377 GHz","volume":"56","author":"Chen","year":"2014","journal-title":"IEEE Antennas Propag. Mag."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Yamada, N., Tanaka, Y., and Nishikawa, K. (2005, January 4\u20136). Radar cross section for pedestrian in 76GHz band. Proceedings of the 2005 European Microwave Conference, Paris, France.","DOI":"10.1109\/EUMC.2005.1610101"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1104","DOI":"10.1049\/iet-rsn.2018.5016","article-title":"Radar cross-section of pedestrians in the low-THz band","volume":"12","author":"Marchetti","year":"2018","journal-title":"IET Radar Sonar Navig."},{"key":"ref_48","unstructured":"International Telecommunication Union (2021). Effects of Building Materials and Structures on Radiowave Propagation Above About 100 MHz, International Telecommunication Union. Technical Report P.2040-2, ITU-R."},{"key":"ref_49","unstructured":"(2024, October 23). Dielectric Properties of Body Tissues. Home Page. Available online: http:\/\/niremf.ifac.cnr.it\/tissprop\/#refs."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1002\/bem.20363","article-title":"Millimeter wave dosimetry of human skin","volume":"29","author":"Alekseev","year":"2008","journal-title":"Bioelectromagnetics"},{"key":"ref_51","unstructured":"Dham, V. (2020). Programming Chirp Parameters in TI Radar Devices, Texas Instruments. Application Report."},{"key":"ref_52","unstructured":"Carlson, A.B., and Crilly, P.B. (2010). Communication Systems, McGraw-Hill Higher Education."},{"key":"ref_53","unstructured":"Richards, M.A. (2022). Fundamentals of Radar Signal Processing, McGraw-Hill Education. [3rd ed.]."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Yang, Z.K., Shi, H., Zhao, S., and Huang, X.D. (2020). Vital Sign Detection During Large-Scale and Fast Body Movements Based on an Adaptive Noise Cancellation Algorithm Using a Single Doppler Radar Sensor. Sensors, 20.","DOI":"10.3390\/s20154183"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Lei, P., Yu, Q., and Wang, J. (2019, January 19\u201321). Polynomial Fitting Based Crosstalk Suppression in the Monostatic FMCW Radar. Proceedings of the 2019 IEEE 4th International Conference on Signal and Image Processing (ICSIP), Wuxi, China.","DOI":"10.1109\/SIPROCESS.2019.8868908"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Sacco, G., Piuzzi, E., Pittella, E., and Pisa, S. (2020). An FMCW Radar for Localization and Vital Signs Measurement for Different Chest Orientations. Sensors, 20.","DOI":"10.3390\/s20123489"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"3596","DOI":"10.1109\/TAP.2024.3365859","article-title":"RCS-Based 3-D Millimeter-Wave Channel Modeling Using Quasi-Deterministic Ray Tracing","volume":"72","author":"Ebrahimizadeh","year":"2024","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1049\/iet-rsn.2019.0471","article-title":"Radar Cross-Sections of Pedestrians at Automotive Radar Frequencies Using Ray Tracing and Point Scatterer Modelling","volume":"14","author":"Deep","year":"2020","journal-title":"IET Radar Sonar Navig."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Dudek, M., Nasr, I., Kissinger, D., Weigel, R., and Fischer, G. (2011, January 24\u201327). The impact of phase noise parameters on target signal detection in FMCW-radar system simulations for automotive applications. Proceedings of the Proceedings of 2011 IEEE CIE International Conference on Radar, Chengdu, China.","DOI":"10.1109\/CIE-Radar.2011.6159587"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Lin, Y.S., Lee, C.Y., and Chen, C.C. (2015, January 25\u201328). A 9.99 mW low-noise amplifier for 60 GHz WPAN system and 77 GHz automobile radar system in 90 nm CMOS. Proceedings of the 2015 IEEE Radio and Wireless Symposium (RWS), San Diego, CA, USA.","DOI":"10.1109\/RWS.2015.7129714"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Baktir, C., Sobaci, E., and D\u00f6nmez, A. (2012, January 7\u201311). A guide to reduce the phase noise effect in FMCW Radars. Proceedings of the 2012 IEEE Radar Conference, Atlanta, GA, USA.","DOI":"10.1109\/RADAR.2012.6212143"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Rengifo, S.C., Chicco, F., Le Roux, E., and Enz, C. (2021, January 22\u201328). Modulation Scheme Impact on Phase Noise in FMCW Radar for Short-Range Applications. Proceedings of the 2021 IEEE International Symposium on Circuits and Systems (ISCAS), Daegu, Korea.","DOI":"10.1109\/ISCAS51556.2021.9401757"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1109\/JERM.2020.3042390","article-title":"Impact of Textile on Electromagnetic Power and Heating in Near-Surface Tissues at 26 GHz and 60 GHz","volume":"5","author":"Sacco","year":"2021","journal-title":"IEEE J. Electromagn. Microwaves Med. Biol."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1109\/JSEN.2022.3224961","article-title":"A Correction Method for the Nonlinearity of FMCW Radar Sensors Based on Multisynchrosqueezing Transform","volume":"23","author":"Zheng","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"648","DOI":"10.1109\/JMW.2022.3195574","article-title":"Detailed Analysis and Modeling of Phase Noise and Systematic Phase Distortions in FMCW Radar Systems","volume":"2","author":"Tschapek","year":"2022","journal-title":"IEEE J. Microwaves"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"46834","DOI":"10.1109\/ACCESS.2024.3382547","article-title":"Evaluation of the Interference Performance of FMCW Radar Sensors in Dense Indoor Environments","volume":"12","author":"Reitz","year":"2024","journal-title":"IEEE Access"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"2100223","DOI":"10.1002\/smtd.202100223","article-title":"Synthetic Image Rendering Solves Annotation Problem in Deep Learning Nanoparticle Segmentation","volume":"5","author":"Mill","year":"2021","journal-title":"Small Methods"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Proen\u00e7a, P.F., and Gao, Y. (August, January 31). Deep Learning for Spacecraft Pose Estimation from Photorealistic Rendering. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France.","DOI":"10.1109\/ICRA40945.2020.9197244"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Man, K., and Chahl, J. (2022). A Review of Synthetic Image Data and Its Use in Computer Vision. J. Imaging, 8.","DOI":"10.3390\/jimaging8110310"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"9221","DOI":"10.1007\/s10462-022-10358-3","article-title":"Review and analysis of synthetic dataset generation methods and techniques for application in computer vision","volume":"56","author":"Paulin","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1209","DOI":"10.1049\/rsn2.12105","article-title":"Transfer learning-based radar imaging with deep convolutional neural networks for distributed frequency modulated continuous waveform multiple-input multiple-output radars","volume":"15","author":"Seo","year":"2021","journal-title":"IET Radar Sonar Navig."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/21\/4028\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:23:46Z","timestamp":1760113426000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/21\/4028"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,30]]},"references-count":71,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2024,11]]}},"alternative-id":["rs16214028"],"URL":"https:\/\/doi.org\/10.3390\/rs16214028","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2024,10,30]]}}}