{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T04:14:08Z","timestamp":1772424848884,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2024,4,18]],"date-time":"2024-04-18T00:00:00Z","timestamp":1713398400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Beijing Natural Science Foundation","award":["L231012"],"award-info":[{"award-number":["L231012"]}]},{"name":"Beijing Natural Science Foundation","award":["62062021"],"award-info":[{"award-number":["62062021"]}]},{"name":"National Natural Science Foundation of China","award":["L231012"],"award-info":[{"award-number":["L231012"]}]},{"name":"National Natural Science Foundation of China","award":["62062021"],"award-info":[{"award-number":["62062021"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In the realm of short-range radar applications, the focus on detecting \u201clow, slow, and small\u201d (LSS) targets has escalated, marking a pivotal aspect of critical area defense. This study pioneers the use of one-dimensional convolutional neural networks (1D-CNNs) for direct slow-time dimension radar feature extraction, sidestepping the complexity tied to frequency and wavelet domain transformations. It innovatively employs a network architecture enriched with multi-frequency multi-scale deformable convolution (MFMSDC) layers for nuanced feature extraction, integrates attention modules to foster comprehensive feature connectivity, and leverages linear operations to curtail overfitting. Through comparative evaluations and ablation studies, our methodology not only simplifies the analytic process but also demonstrates superior classification capabilities. This establishes a new benchmark for efficiently classifying low-altitude entities, such as birds and unmanned aerial vehicles (UAVs), thereby enhancing the precision and operational efficiency of radar detection systems.<\/jats:p>","DOI":"10.3390\/rs16081431","type":"journal-article","created":{"date-parts":[[2024,4,18]],"date-time":"2024-04-18T06:21:12Z","timestamp":1713421272000},"page":"1431","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Radar Signal Classification with Multi-Frequency Multi-Scale Deformable Convolutional Networks and Attention Mechanisms"],"prefix":"10.3390","volume":"16","author":[{"given":"Ruofei","family":"Liang","sequence":"first","affiliation":[{"name":"Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China"}]},{"given":"Yigang","family":"Cen","sequence":"additional","affiliation":[{"name":"Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China"},{"name":"Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing 100044, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,18]]},"reference":[{"key":"ref_1","first-page":"687","article-title":"Non-cooperative UAV Target Recognition in Low-altitude Airspace Based on Motion Model","volume":"45","author":"Chen","year":"2019","journal-title":"J. Beijing Univ. Aeronaut. Astronaut."},{"key":"ref_2","first-page":"55","article-title":"Research on Civil UAV Countermeasure Technology","volume":"3","author":"Wu","year":"2018","journal-title":"China Radio"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"9654","DOI":"10.1109\/JSTARS.2022.3216564","article-title":"A comprehensive review for typical applications based upon unmanned aerial vehicle platform","volume":"15","author":"Han","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4075","DOI":"10.1109\/TIP.2019.2905984","article-title":"State-aware anti-drift object tracking","volume":"28","author":"Han","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1109\/LSP.2019.2895962","article-title":"Spatial-temporal context-aware tracking","volume":"26","author":"Han","year":"2019","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1230","DOI":"10.1109\/LSP.2021.3086675","article-title":"Learning dynamic spatial-temporal regularization for UAV object tracking","volume":"28","author":"Deng","year":"2021","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1056","DOI":"10.1049\/el.2020.1096","article-title":"Towards long\u2013term UAV object tracking via effective feature matching","volume":"56","author":"Zhao","year":"2020","journal-title":"Electron. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"873","DOI":"10.1049\/el.2020.1170","article-title":"Boundary\u2013aware vehicle tracking upon UAV","volume":"56","author":"Han","year":"2020","journal-title":"Electron. Lett."},{"key":"ref_9","first-page":"32","article-title":"Ability Status and Development Trend of Anti-\u201clow, slow and small\u201d UAVs","volume":"6","author":"Luo","year":"2019","journal-title":"Aerodyn. Missile J."},{"key":"ref_10","first-page":"7","article-title":"Review on Development and Applications of Avian Radar Technology","volume":"39","author":"Chen","year":"2017","journal-title":"Mod. Radar"},{"key":"ref_11","first-page":"30","article-title":"Radar Low-observable Target Detection","volume":"35","author":"Chen","year":"2017","journal-title":"Sci. Technol. Rev."},{"key":"ref_12","unstructured":"Wang, F.Y., Guo, R.J., and Hao, M. (2011). Balloon Borne Radar Target Detection within Ground Clutter Based on Fractal Character. (Application No.201110015890.X), National Defense Invention Patent."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.optcom.2018.04.042","article-title":"Spatial Resolution Enhancement of Coherent Doppler Wind Lidar Using Joint Time-frequency Analysis","volume":"424","author":"Wang","year":"2018","journal-title":"Opt. Commun."},{"key":"ref_14","first-page":"687","article-title":"MMRGait-1.0: A Radar Time-frequency Spectrogram Dataset for Gait Recognition under Multi-view and Multi-wearing Conditions","volume":"45","author":"Du","year":"2019","journal-title":"J. Radars"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A Fast Learning Algorithm for Deep Belief Nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"ref_16","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). ImageNet Classification with Deep Convolutional Neural Networks. Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"7220","DOI":"10.1109\/TPAMI.2022.3221486","article-title":"Contrastive Bayesian Analysis for Deep Metric Learning","volume":"45","author":"Kan","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.tre.2019.03.013","article-title":"Flight Delay Prediction for Commercial Air Transport: A Deep Learning Approach","volume":"125","author":"Yu","year":"2019","journal-title":"Transp. Res. Part E Logist. Transp. Rev."},{"key":"ref_19","first-page":"565","article-title":"Detection and Classification of Maritime Target with Micro Motion Based on CNNs","volume":"7","author":"Su","year":"2018","journal-title":"J. Radars"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the Dimensionality of Data with Neural Networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/MSP.2012.2205597","article-title":"Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups","volume":"29","author":"Hinton","year":"2012","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_22","first-page":"1076","article-title":"Specific Emitter Identification Using Wavelet Transform Feature Extraction","volume":"34","author":"Yu","year":"2018","journal-title":"Signal Process."},{"key":"ref_23","first-page":"16","article-title":"Signal Recognition Method Based on Joint Time-frequency Radiation Source","volume":"33","author":"Ye","year":"2018","journal-title":"Electron. Warf. Technol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2404","DOI":"10.1049\/iet-com.2013.0865","article-title":"Specific Emitter Identification Based on Hilbert-Huang Transform-based-time Frequency-energy Distribution Features","volume":"8","author":"Wu","year":"2014","journal-title":"IET Commun."},{"key":"ref_25","unstructured":"Yang, Y., Lian, J.J., Zhou, G.G., and Chen, Z.H. (2020, January 19). Steel Truss Structure Damage Identification Based on One-dimensional Convolutional Neural Network. Proceedings of the Tianjin University, Tianjin Steel Structure Society, Academic Committee of the National Symposium on Modern Structural Engineering, Tianjin, China."},{"key":"ref_26","first-page":"51","article-title":"Gearbox Fault Diagnosis Based on One-dimensional Convolutional Neural Network","volume":"37","author":"Wu","year":"2018","journal-title":"Vib. Shock"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1109\/TAES.2017.2761229","article-title":"Sparsity-driven MicroDoppler Feature Extraction for Dynamic Hand Gesture Recognition","volume":"54","author":"Li","year":"2018","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2438","DOI":"10.1109\/LGRS.2015.2482520","article-title":"Unsupervised Spectral-spatial Feature Learning with Stacked Sparse Autoencoder for Hyperspectral Imagery Classification","volume":"12","author":"Tao","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., and Zhu, P. (2020, January 14\u201319). Supplementary Material for ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"ref_30","first-page":"294","article-title":"Flight Passenger Load Factors Prediction Based on RNN Using Multi Granularity Time Attention","volume":"46","author":"Deng","year":"2020","journal-title":"Comput. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4199","DOI":"10.1007\/s00382-022-06514-8","article-title":"Correction to: The Area Prediction of Western North Pacific Subtropical High in Summer Based on Gaussian Naive Bayes","volume":"60","author":"Li","year":"2022","journal-title":"Clim. Dyn."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1947","DOI":"10.1021\/ci034160g","article-title":"Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling","volume":"43","author":"Svetnik","year":"2003","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"ref_33","unstructured":"Triebe, O., Laptev, N., and Rajagopal, R. (2019). AR-Net: A Simple Auto-Regressive Neural Network for Time-series. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Yang, L., Han, Y.Z., and Chen, X. (2020, January 14\u201319). Resolution Adaptive Networks for Efficient Inference. Proceedings of the Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00244"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"He, K.M., Zhang, X.Y., and Ren, S.Q. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_36","unstructured":"Devlin, J., Chang, M.W., and Lee, K. (2019, January 2\u20137). Bert: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics, Human Language Technologies, Minneapolis, MN, USA."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1109\/JSTSP.2018.2793761","article-title":"Cognitive Target Tracking Via Angle-range-Doppler Estimation with Transmit Subaperturing FDA Radar","volume":"12","author":"Gui","year":"2018","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1109\/LGRS.2016.2624820","article-title":"Drone Classification Using Convolutional Neural Networks with Merged Doppler Images","volume":"14","author":"Kim","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Mendis, G.J., Wei, J., and Madanayake, A. (2017, January 27\u201328). Deep Learning Cognitive Radar for Micro UAS Detection and Classification. Proceedings of the 2017 Cognitive Communications for Aerospace Applications Workshop, Cleveland, OH, USA.","DOI":"10.1109\/CCAAW.2017.8001610"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1109\/LGRS.2023.3330466","article-title":"Type-aspect Disentanglement Network for HRRP Target Recognition with Missing Aspects","volume":"20","author":"Wang","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"7884","DOI":"10.1109\/JSEN.2020.3044314","article-title":"Polarimetric HRRP Recognition Based on ConvLSTM with Self-Attention","volume":"21","author":"Zhang","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_42","first-page":"7766","article-title":"Radar HRRP Recognition Based on CNN","volume":"21","author":"Song","year":"2019","journal-title":"J. Eng."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Jiang, W., Wu, X., Wang, Y., Chen, B., Feng, W., and Jin, Y. (2021). Time\u2013Frequency-Analysis-Based Blind Modulation Classification for Multiple-Antenna Systems. Sensors, 21.","DOI":"10.3390\/s21010231"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Park, D., Lee, S., Park, S., and Kwak, N. (2021). Radar-Spectrogram-Based UAV Classification Using Convolutional Neural Networks. Sensors, 21.","DOI":"10.3390\/s21010210"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Huang, C.-Y., and Dzulfikri, Z. (2021). Stamping Monitoring by Using an Adaptive 1D Convolutional Neural Network. Sensors, 21.","DOI":"10.3390\/s21010262"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Wang, X., Chen, H., Liu, W., Zhang, L., Li, B., and Ni, M. (2023). Echo Preprocessing-Based Smeared Spectrum Interference Suppression. Electronics, 12.","DOI":"10.3390\/electronics12173690"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Zhang, Z., Li, B., Zhou, B., Chen, H., and Wang, Y. (2023). Analysis of Characteristics and Suppression Methods for Self-Defense Smart Noise Jamming. Electronics, 12.","DOI":"10.3390\/electronics12153270"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Zou, B., Feng, W., and Zhu, H. (2023). Airborne Radar STAP Method Based on Deep Unfolding and Convolutional Neural Networks. Electronics, 12.","DOI":"10.3390\/electronics12143140"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.rse.2016.11.002","article-title":"Postfailure Evolution Analysis of a Rainfall-triggered Landslide by Multi-temporal Interferometry SAR Approaches Integrated with Geotechnical Analysis","volume":"188","author":"Confuorto","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Lund\u00e9n, J., and Koivunen, V. (2016, January 2\u20136). Deep Learning for HRRP-based Target Recognition in Multistatic Radar Systems. Proceedings of the 2016 IEEE Radar Conference, Philadelphia, PA, USA.","DOI":"10.1109\/RADAR.2016.7485271"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Liu, Y., Long, T., Zhang, L., Wang, Y., Zhang, X., and Li, Y. (IEEE Trans. Aerosp. Electron. Syst., 2024). SDHC: Joint semantic-data guided hierarchical classification for fine-grained HRRP target recognition, IEEE Trans. Aerosp. Electron. Syst., early access.","DOI":"10.1109\/TAES.2024.3373378"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/8\/1431\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:29:51Z","timestamp":1760106591000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/8\/1431"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,18]]},"references-count":51,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["rs16081431"],"URL":"https:\/\/doi.org\/10.3390\/rs16081431","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,18]]}}}