{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T18:30:26Z","timestamp":1773772226114,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,4,28]],"date-time":"2024-04-28T00:00:00Z","timestamp":1714262400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Beijing Nova Program","award":["20230484477"],"award-info":[{"award-number":["20230484477"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Automotive radar is one of the key sensors for intelligent driving. Radar image sequences contain abundant spatial and temporal information, enabling target classification. For existing radar spatiotemporal classifiers, multi-view radar images are usually employed to enhance the information of the target and 3D convolution is employed for spatiotemporal feature extraction. These models consume significant hardware resources and are not applicable to real-time applications. In this paper, RadarTCN, a novel lightweight network, is proposed that achieves high-accuracy online target classification using single-view radar image sequences only. In RadarTCN, 2D convolution and 3D-TCN are employed to extract spatiotemporal features sequentially. To reduce data dimensionality and computational complexity, a multi-layer max pooling down-sampling method is designed in a 2D convolution module. Meanwhile, the 3D-TCN module is improved through residual pruning and causal convolution is introduced for leveraging the performance of online target classification. The experimental results demonstrate that RadarTCN can achieve high-precision online target recognition for both range-angle and range-Doppler map sequences. Compared to the reference models on the CARRADA dataset, RadarTCN exhibits better classification performance, with fewer parameters and lower computational complexity.<\/jats:p>","DOI":"10.3390\/s24092813","type":"journal-article","created":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T08:49:24Z","timestamp":1714380564000},"page":"2813","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["RadarTCN: Lightweight Online Classification Network for Automotive Radar Targets Based on TCN"],"prefix":"10.3390","volume":"24","author":[{"given":"Yuan","family":"Li","sequence":"first","affiliation":[{"name":"School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengmeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information, North China University of Technology, Beijing 100104, China"},{"name":"College of Robotics, Beijing Union University, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5613-1216","authenticated-orcid":false,"given":"Hongyuan","family":"Jing","sequence":"additional","affiliation":[{"name":"College of Robotics, Beijing Union University, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhi","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information, North China University of Technology, Beijing 100104, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1109\/7.18645","article-title":"OS-CFAR theory for multiple targets and nonuniform clutter","volume":"24","author":"Blake","year":"1988","journal-title":"Aerosp. Electron. Syst. IEEE Trans."},{"key":"ref_2","unstructured":"Ester, M., Kriegel, H.P., Sander, J., and Xu, X. (1996, January 2\u20134). A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, KDD\u201996, Portland Oregon."},{"key":"ref_3","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_4","doi-asserted-by":"crossref","first-page":"55965","DOI":"10.1109\/ACCESS.2023.3282688","article-title":"Resource-Efficient Range-Doppler Map Generation Using Deep Learning Network for Automotive Radar Systems","volume":"11","author":"Taewon","year":"2023","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"17932","DOI":"10.1109\/JSEN.2022.3194527","article-title":"Comparative Analysis of Radar-Cross-Section-Based UAV Recognition Techniques","volume":"22","author":"Martins","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Liu, T., He, D., Guan, K., Ma, Y., Liu, R., Guo, L., and Zhong, Z. (2023, January 15\u201317). Channel Modelling on Micro-Doppler Feature of a Pedestrian at 77 GHz. Proceedings of the IEEE Conference on Antenna Measurements and Applications (CAMA), Genoa, Italy.","DOI":"10.1109\/CAMA57522.2023.10352790"},{"key":"ref_7","unstructured":"Zhu, Y., Fan, H., and Lu, Z. (2011, January 24). Relationship between radar target signatures and motion modes. Proceedings of the CIE International Conference on Radar, Chengdu, China."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Chen, X., Su, N., Guan, J., Mou, X., and Xue, Y. (2019, January 9\u201315). Integrated Processing of Radar Detection and Classification for Moving Target via Time-frequency Graph and CNN Learning. Proceedings of the URSI Asia-Pacific Radio Science Conference (AP-RASC), New Delhi, India.","DOI":"10.23919\/URSIAP-RASC.2019.8738388"},{"key":"ref_9","unstructured":"Svante, S., and Niclas, W. (2019, January 23\u201327). Target Detection and Classification of Small Drones by Deep Learning on Radar Micro-Doppler. Proceedings of the International Radar Conference (RADAR), Toulon, France."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1082","DOI":"10.1049\/iet-rsn.2018.0103","article-title":"Practical classification of different moving targets using automotive radar and deep neural networks","volume":"12","author":"Angelov","year":"2018","journal-title":"IET Radar Sonar Navig."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ouaknine, A., Newson, A., Rebut, J., Tupin, F., and P\u00e9rez, P. (2021, January 10). CARRADA Dataset: Camera and automotive radar with range-angle-Doppler annotations. Proceedings of the International Conference on Pattern Recognition (ICPR), Milan, Italy.","DOI":"10.1109\/ICPR48806.2021.9413181"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wang, G., Hsu, H.-M., Liu, H., and Hwang, J.-N. (2021, January 19). Rethinking of Radar\u2019s Role: A Camera-Radar Dataset and Systematic Annotator via Coordinate Alignment. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Nashville, TN, USA.","DOI":"10.1109\/CVPRW53098.2021.00316"},{"key":"ref_13","unstructured":"Ao, Z., Farzan, E.N., and Robert, L. (2021, January 26\u201328). RADDet: Range-Azimuth-Doppler based Radar Object Detection for Dynamic Road Users. Proceedings of the Conference on Robots and Vision (CRV), Burnaby, BC, Canada."},{"key":"ref_14","unstructured":"Fatih, S.B., Florian, P., Julian, W., Roman, R., Alexander, J., and Nicolaj, C.S. (2022, January 1\u20133). Classification of Vulnerable Road Users based on Range-Doppler Maps of 77 GHz MIMO Radar using Different Machine Learning Approaches. Proceedings of the International Conference on Graphics and Signal Processing, Chiba, Japan."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"80284","DOI":"10.1109\/ACCESS.2023.3299325","article-title":"Autonomous Human and Animal Classification Using Synthetic 2D Tensor Data Based on Dual-Receiver mmWave Radar System","volume":"11","author":"Arsyad","year":"2023","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1049\/iet-rsn.2019.0307","article-title":"DopplerNet: A convolutional neural network for recognizing targets in real scenarios using a persistent range-Doppler radar","volume":"14","author":"Montero","year":"2020","journal-title":"IET Radar Sonar Navig."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Shirakata, N., Iwasa, K., Yui, T., Yomo, H., Murata, T., and Sato, J. (2019, January 22\u201324). Object and Direction Classification based on Range-Doppler Map of 79 GHz MIMO Radar Using a Convolutional Neural Network. Proceedings of the Global Symposium on Millimeter Waves (GSMM), Sendai, Japan.","DOI":"10.1109\/GSMM.2019.8797649"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1109\/TIV.2023.3322353","article-title":"Semantic Segmentation-Based Occupancy Grid Map Learning With Automotive Radar Raw Data","volume":"9","author":"Jin","year":"2024","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_19","unstructured":"Stefan, H., Martin, B., and Klaus, D. (2018, January 21\u201325). Dynamic Occupancy Grid Prediction for Urban Autonomous Driving: A Deep Learning Approach with Fully Automatic Labeling. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, Australia."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Nowruzi, F.E., Kolhatkar, D., Kapoor, P., Al Hassanat, F., Heravi, E.J., Laganiere, R., Rebut, J., and Malik, W. (2020, January 23). Deep Open Space Segmentation using Automotive Radar. Proceedings of the IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM), Linz, Austria.","DOI":"10.1109\/ICMIM48759.2020.9299052"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Yue, X., Keutzer, K., and Sangiovanni Vincentelli, A. (2021, January 21\u201324). Scene-aware Learning Network for Radar Object Detection. Proceedings of the International Conference on Multimedia Retrieval (ICMR \u201921), Association for Computing Machinery, New York, NY, USA.","DOI":"10.1145\/3460426.3463655"},{"key":"ref_22","unstructured":"Rodrigo, P., Falk, S., Rasshofer, R.H., and Erwin, B. (2019, January 23\u201325). Deep Learning Radar Object Detection and Classification for Urban Automotive Scenarios. Proceedings of the Kleinheubach Conference, Kleinheubach, Germany."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Major, B., Fontijne, D., Ansari, A., Teja, S.R., Gowaikar, R., Hamilton, M., Lee, S., Grzechnik, S., and Subramanian, S. (2019, January 27\u201328). Vehicle Detection With Automotive Radar Using Deep Learning on RangeAzimuth-Doppler Tensors. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) Workshops, Seoul, Republic of Korea.","DOI":"10.1109\/ICCVW.2019.00121"},{"key":"ref_24","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_25","doi-asserted-by":"crossref","unstructured":"Ouaknine, A., Newson, A., P\u00e9rez, P., Tupin, F., and Rebut, J. (2021, January 10\u201317). Multi-View Radar Semantic Segmentation. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.01538"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Kothari, R., Kariminezhad, A., Mayr, C., and Zhang, H. (2023, January 4\u20137). Object Detection and Heading Estimation from Radar Raw data. Proceedings of the IEEE Intelligent Vehicles Symposium (IV), Anchorage, AK, USA.","DOI":"10.1109\/IV55152.2023.10186591"},{"key":"ref_27","unstructured":"Colin, D., Rufin, V.R., Didier, S., and Thomas, O. (2023). A recurrent CNN for online object detection on raw radar frames. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Abadpour, S., Pauli, M., Long, X., and Zwick, T. (2023, January 22\u201325). Automotive Radar Channel Simulation based on a High-Resolution Backscattering Model of a Motorcyclist. Proceedings of the IEEE Radio and Wireless Symposium (RWS), Las Vegas, NV, USA.","DOI":"10.1109\/RWS55624.2023.10046206"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Bouwmeester, W., Fioranelli, F., and Yarovoy, A. (2023, January 20\u201322). Statistical Polarimetric RCS Model of an Asphalt Road Surface for mm-Wave Automotive Radar. Proceedings of the European Radar Conference (EuRAD), Berlin, Germany.","DOI":"10.23919\/EuRAD58043.2023.10289307"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1160","DOI":"10.1109\/TCOMM.2019.2956928","article-title":"Multi-Target Position and Velocity Estimation Using OFDM Communication Signals","volume":"68","author":"Li","year":"2020","journal-title":"IEEE Trans. Commun."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"954","DOI":"10.1109\/JSTSP.2021.3058895","article-title":"RODNet: A Real-Time Radar Object Detection Network Cross-Supervised by Camera-Radar Fused Object 3D Localization","volume":"15","author":"Wang","year":"2021","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Majang, B.C., Zaini, N., Mazalan, L., and Latip, M.F.A. (2023, January 16). Rainfall Nowcasting: A Convolutional LSTM Approach with Various Grayscale Representations of Weather Radar Images. Proceedings of the IEEE 11th Conference on Systems, Process & Control (ICSPC), Malacca, Malaysia.","DOI":"10.1109\/ICSPC59664.2023.10420076"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Jia, F., Tan, J., Lu, X., and Qian, J. (2023). Radar Timing Range\u2013Doppler Spectral Target Detection Based on Attention ConvLSTM in Traffic Scenes. Remote Sens., 15.","DOI":"10.3390\/rs15174150"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"13607","DOI":"10.1109\/JSEN.2020.3006386","article-title":"Continuous Human Activity Classification From FMCW Radar With Bi-LSTM Networks","volume":"20","author":"Shrestha","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_35","unstructured":"Bai, S.J., Kolter, J.Z., and Koltun, V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Guirguis, K., Schorn, C., Guntoro, A., Abdulatif, S., and Yang, B. (2021, January 18\u201321). SELD-TCN: Sound Event Localization & Detection via Temporal Convolutional Networks. Proceedings of the European Signal Processing Conference (EUSIPCO), Amsterdam, Netherlands.","DOI":"10.23919\/Eusipco47968.2020.9287716"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"10336","DOI":"10.1109\/JIOT.2021.3067382","article-title":"TinyRadarNN: Combining Spatial and Temporal Convolutional Neural Networks for Embedded Gesture Recognition With Short Range Radars","volume":"8","author":"Scherer","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1000","DOI":"10.1109\/JBHI.2023.3339703","article-title":"Eat-Radar: Continuous Fine-Grained Eating Gesture Detection Using FMCW Radar and 3D Temporal Convolutional Network","volume":"28","author":"Wang","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Gao, X., Xing, G., Roy, S., and Liu, H. (2019, January 3\u20136). Experiments with mmWave Automotive Radar Test-bed. Proceedings of the Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA.","DOI":"10.1109\/IEEECONF44664.2019.9048939"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Addabbo, P., Bernardi, M.L., Biondi, F., Cimitile, M., Clemente, C., and Orlando, D. (2020, January 22\u201324). Gait Recognition using FMCW Radar and Temporal Convolutional Deep Neural Networks. Proceedings of the IEEE 7th International Workshop on Metrology for AeroSpace (Metro Aero Space), Pisa, Italy.","DOI":"10.1109\/MetroAeroSpace48742.2020.9160199"},{"key":"ref_41","unstructured":"Ioffe, S., and Szegedy, C. (2015). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Alsobhi, W., Alafif, T., Zong, W., and Abdel-Hakim, A.E. (2023, January 5\u20136). Adaptive Batch Normalization for Training Data with Heterogeneous Features. Proceedings of the International Conference on Smart Computing and Application (ICSCA), Hail, Saudi Arabia.","DOI":"10.1109\/ICSCA57840.2023.10087711"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zhang, H., Xie, H., and Yao, H. (2022). Spatio-Temporal Deformable Attention Network for Video Deblurring. arXiv.","DOI":"10.1007\/978-3-031-19787-1_33"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/9\/2813\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:35:09Z","timestamp":1760106909000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/9\/2813"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,28]]},"references-count":43,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["s24092813"],"URL":"https:\/\/doi.org\/10.3390\/s24092813","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,28]]}}}