{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T10:41:12Z","timestamp":1787568072823,"version":"build-2736575974"},"reference-count":37,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T00:00:00Z","timestamp":1787443200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2024YFF1400500"],"award-info":[{"award-number":["2024YFF1400500"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Under ground-based observation geometry, infrared radiant-intensity sequences of space objects are jointly influenced by object micromotion, thermal radiation, time-varying viewing conditions, and atmospheric propagation. Existing simulation studies often prescribe the line of sight or simplify the coupling between viewing geometry and atmospheric attenuation, which limits long-duration ground-based sequence analysis. This study develops a physics-informed framework for generating atmosphere-attenuated infrared radiant-intensity sequences of space objects undergoing precession or tumbling. The framework reconstructs observation geometry from azimuth\u2013elevation\u2013range trajectories, updates facet normals through a unified micromotion attitude model, computes visible projected area and transient facet temperature, and incorporates MODTRAN-derived elevation-dependent atmospheric transmittance. Using this framework, we construct IRPeriodic, an eight-class simulated dataset for long-duration univariate time-series classification. We further propose LPD-Net, which integrates large-kernel residual feature extraction, prototype-guided dynamic temporal alignment, and differential periodic representation to capture long-range waveform morphology, sample-dependent temporal correspondence, and segment-level local variation. On IRPeriodic, LPD-Net achieves an accuracy of 0.8618 \u00b1 0.0057, a macro-F1 of 0.8615 \u00b1 0.0061, and a Matthews correlation coefficient of 0.8426 \u00b1 0.0065, outperforming the evaluated neural-network and ROCKET-type baselines. Ablation and synthetic-noise sensitivity analyses indicate that the performance gain is mainly associated with long-context feature extraction, with additional improvements from dynamic alignment and differential periodic statistics. Auxiliary experiments on selected public UCR datasets suggest that the representation is also competitive for univariate time-series classification. These results demonstrate the effectiveness of LPD-Net on the proposed physics-informed benchmark for long-duration ground-based infrared radiant-intensity sequence classification.<\/jats:p>","DOI":"10.3390\/s26175335","type":"journal-article","created":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T10:07:53Z","timestamp":1787566073000},"page":"5335","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Physics-Informed Simulation and Time-Series Classification of Ground-Based Infrared Radiant-Intensity Sequences for Space Objects"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-3677-5479","authenticated-orcid":false,"given":"Yubo","family":"Wang","sequence":"first","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Jilin Provincial Key Laboratory of Intelligent Wavefront Sensing and Control, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5461-3900","authenticated-orcid":false,"given":"Shijun","family":"Song","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chun","family":"Jiang","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-3134-1934","authenticated-orcid":false,"given":"Qiyang","family":"Gui","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Chen","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuai","family":"Wang","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengwei","family":"Li","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"103795","DOI":"10.1016\/j.infrared.2021.103795","article-title":"Modeling and Analysis of Infrared Radiation Dynamic Characteristics for Space Micromotion Target Recognition","volume":"116","author":"Zhang","year":"2021","journal-title":"Infrared Phys. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wu, D., Lu, H., Zhao, B., Liu, J., and Zhao, M. (2019). Micro-Motion Dynamics and Shape Parameters Estimation Based on an Infrared Signature Model of Spatial Targets. Electronics, 8.","DOI":"10.3390\/electronics8070755"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.cja.2023.08.008","article-title":"State-of-the-Art Development about Cryogenic Technologies to Support Space-Based Infrared Detection","volume":"36","author":"Wang","year":"2023","journal-title":"Chin. J. Aeronaut."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Jiao, J., Zhao, L., Pan, W., and Li, X. (2023). Development and Core Technologies for Intelligent SWaP3 Infrared Cameras: A Comprehensive Review and Analysis. Sensors, 23.","DOI":"10.3390\/s23094189"},{"key":"ref_5","first-page":"20220823","article-title":"Stray-Light Analysis and Suppression for a Long-Wave Infrared Dewar Assembly Used in Cold Optics","volume":"52","author":"Zhu","year":"2023","journal-title":"Infrared Laser Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.infrared.2018.11.017","article-title":"Recurrent Neural Networks for Discrimination of Exo-Atmospheric Targets Based on Infrared Radiation Signature","volume":"96","author":"Ma","year":"2019","journal-title":"Infrared Phys. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1364\/AO.58.000164","article-title":"Exo-Atmospheric Infrared Objects Classification Using Recurrence-Plots-Based Convolutional Neural Networks","volume":"58","author":"Deng","year":"2019","journal-title":"Appl. Opt."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"103535","DOI":"10.1016\/j.infrared.2020.103535","article-title":"Exo-Atmospheric Infrared Objects Classification Based on Dual-Channel LSTM Network","volume":"111","author":"Zhao","year":"2020","journal-title":"Infrared Phys. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"7578","DOI":"10.1364\/AO.569034","article-title":"Infrared Cone-Shaped Spatial Target Recognition Based on an Improved MKELM","volume":"64","author":"Wang","year":"2025","journal-title":"Appl. Opt."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"28113","DOI":"10.1109\/ACCESS.2019.2898028","article-title":"Multi-Scale Convolutional Neural Networks for Space Infrared Point Objects Discrimination","volume":"7","author":"Deng","year":"2019","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"104670","DOI":"10.1016\/j.infrared.2023.104670","article-title":"Spatial Infrared Objects Discrimination Based on Multi-Channel CNN with Attention Mechanism","volume":"132","author":"Zhang","year":"2023","journal-title":"Infrared Phys. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Chen, X., Zhang, H., Zhang, S., Feng, J., Xia, H., Rao, P., and Ai, J. (2024). A Space Infrared Dim Target Recognition Algorithm Based on Improved DS Theory and Multi-Dimensional Feature Decision Level Fusion Ensemble Classifier. Remote Sens., 16.","DOI":"10.3390\/rs16030510"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Cheng, Y., Lai, X., Xia, Y., and Zhou, J. (2024). Infrared Dim Small Target Detection Networks: A Review. Sensors, 24.","DOI":"10.3390\/s24123885"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Sun, J., Wei, M., Wang, J., Zhu, M., Lin, H., Nie, H., and Deng, X. (2024). CenterADNet: Infrared Video Target Detection Based on Central Point Regression. Sensors, 24.","DOI":"10.3390\/s24061778"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"105145","DOI":"10.1016\/j.infrared.2024.105145","article-title":"An FCN-Based Transfer-Learning Method for Spatial Infrared Moving-Target Recognition","volume":"137","author":"Zhang","year":"2024","journal-title":"Infrared Phys. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1007\/s10618-019-00619-1","article-title":"Deep Learning for Time Series Classification: A Review","volume":"33","author":"Forestier","year":"2019","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_17","first-page":"217","article-title":"Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey","volume":"56","author":"Foumani","year":"2024","journal-title":"ACM Comput. Surv."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1007\/s10618-016-0483-9","article-title":"The Great Time Series Classification Bake Off: A Review and Experimental Evaluation of Recent Algorithmic Advances","volume":"31","author":"Bagnall","year":"2017","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"162","DOI":"10.4236\/jcc.2018.611016","article-title":"Analysis of Infrared Radiation Characteristics and Penetration Measures of Warhead","volume":"6","author":"Liu","year":"2018","journal-title":"J. Comput. Commun."},{"key":"ref_20","first-page":"134","article-title":"Calculation of Temperature and Radiation Characteristics of Midcourse Ballistic Missiles","volume":"44","author":"Li","year":"2022","journal-title":"Infrared Technol."},{"key":"ref_21","first-page":"90880H","article-title":"MODTRAN6: A Major Upgrade of the MODTRAN Radiative Transfer Code","volume":"9088","author":"Berk","year":"2014","journal-title":"Proc. SPIE"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1016\/j.jqsrt.2017.03.004","article-title":"Validation of MODTRAN\u00ae6 and Its Line-by-Line Algorithm","volume":"203","author":"Berk","year":"2017","journal-title":"J. Quant. Spectrosc. Radiat. Transf."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1088\/1674-4527\/19\/10\/149","article-title":"Lijiang 2.4-Meter Telescope and Its Instruments","volume":"19","author":"Wang","year":"2019","journal-title":"Res. Astron. Astrophys."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/TASSP.1978.1163055","article-title":"Dynamic Programming Algorithm Optimization for Spoken Word Recognition","volume":"26","author":"Sakoe","year":"1978","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"ref_25","first-page":"894","article-title":"Soft-DTW: A Differentiable Loss Function for Time-Series","volume":"Volume 70","author":"Cuturi","year":"2017","journal-title":"Proceedings of the 34th International Conference on Machine Learning"},{"key":"ref_26","unstructured":"Kingma, D.P., and Ba, J. (2015, January 7\u20139). Adam: A Method for Stochastic Optimization. Proceedings of the International Conference on Learning Representations, San Diego, CA, USA."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1016\/0005-2795(75)90109-9","article-title":"Comparison of the Predicted and Observed Secondary Structure of T4 Phage Lysozyme","volume":"405","author":"Matthews","year":"1975","journal-title":"Biochim. Biophys. Acta"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wang, Z., Yan, W., and Oates, T. (2017). Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline. 2017 International Joint Conference on Neural Networks, IEEE.","DOI":"10.1109\/IJCNN.2017.7966039"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1936","DOI":"10.1007\/s10618-020-00710-y","article-title":"InceptionTime: Finding AlexNet for Time Series Classification","volume":"34","author":"Lucas","year":"2020","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1454","DOI":"10.1007\/s10618-020-00701-z","article-title":"ROCKET: Exceptionally Fast and Accurate Time Series Classification Using Random Convolutional Kernels","volume":"34","author":"Dempster","year":"2020","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Dempster, A., Schmidt, D.F., and Webb, G.I. (2021). MiniRocket: A Very Fast Almost Deterministic Transform for Time Series Classification. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, ACM.","DOI":"10.1145\/3447548.3467231"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1623","DOI":"10.1007\/s10618-022-00844-1","article-title":"MultiRocket: Multiple Pooling Operators and Transformations for Fast and Effective Time Series Classification","volume":"36","author":"Tan","year":"2022","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1293","DOI":"10.1109\/JAS.2019.1911747","article-title":"The UCR Time Series Archive","volume":"6","author":"Dau","year":"2019","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1958","DOI":"10.1007\/s10618-024-01022-1","article-title":"Bake Off Redux: A Review and Experimental Evaluation of Recent Time Series Classification Algorithms","volume":"38","author":"Middlehurst","year":"2024","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ismail-Fawaz, A., Devanne, M., Weber, J., and Forestier, G. (2022). Deep Learning for Time Series Classification Using New Hand-Crafted Convolution Filters. 2022 IEEE International Conference on Big Data, IEEE.","DOI":"10.1109\/BigData55660.2022.10020496"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1007\/978-3-031-49896-1_14","article-title":"Back to Basics: A Sanity Check on Modern Time Series Classification Algorithms","volume":"Volume 14343","author":"Ifrim","year":"2023","journal-title":"Advanced Analytics and Learning on Temporal Data"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/17\/5335\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T10:11:40Z","timestamp":1787566300000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/17\/5335"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,23]]},"references-count":37,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["s26175335"],"URL":"https:\/\/doi.org\/10.3390\/s26175335","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,23]]}}}