{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T00:26:00Z","timestamp":1787012760674,"version":"build-2736575974"},"reference-count":58,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62301031"],"award-info":[{"award-number":["62301031"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Aerosp. Electron. Syst."],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1109\/taes.2024.3460752","type":"journal-article","created":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T14:05:10Z","timestamp":1726668310000},"page":"1725-1741","source":"Crossref","is-referenced-by-count":2,"title":["MsPF-Trans: A Generative Transformer for Multistep Probabilistic Forecasting of Radar Pulse Repetition Interval Sequences"],"prefix":"10.1109","volume":"61","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-2086-9658","authenticated-orcid":false,"given":"Zihao","family":"Wang","sequence":"first","affiliation":[{"name":"School of Information and Electronics, Beijing Institute of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3721-0178","authenticated-orcid":false,"given":"Yunjie","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information and Electronics, Beijing Institute of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-0338-4630","authenticated-orcid":false,"given":"Zheng","family":"Gong","sequence":"additional","affiliation":[{"name":"School of Information and Electronics, Beijing Institute of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0502-3386","authenticated-orcid":false,"given":"Mengtao","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Information and Electronics, Beijing Institute of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Syntactic modeling of multi-function radars","author":"Visnevski","year":"2005","journal-title":"Elect. Comput. Eng."},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2007.893252"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2007.908949"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TAES.2021.3082660"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/7.249124"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1049\/ip-rsn:20050067"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2006.1593338"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1049\/iet-rsn:20070003"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.ast.2007.01.006"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2010.06.008"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2013.2285014"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2017.2716935"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1049\/iet-rsn.2018.5276"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.3390\/s17030632"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/PIERS.2017.8262193"},{"key":"ref16","article-title":"Research on behavior recognition and prediction techniques against multi-function radar","author":"Ou","year":"2017"},{"issue":"6","key":"ref17","first-page":"1404","article-title":"MFR state prediction method based on aircraft maneuvering features assistance","volume":"46","author":"Chen","year":"2018","journal-title":"ACTA Electronica Sinica"},{"key":"ref18","first-page":"203","article-title":"Support vector regression","volume":"11","author":"Basak","year":"2007","journal-title":"Neural Inf. Process. Lett. Rev."},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2015.2390914"},{"issue":"4","key":"ref20","first-page":"559","article-title":"Semantic coding and model reconstruction of multifunction radar pulse train","volume":"10","author":"Liu","year":"2021","journal-title":"J. Radars"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/4643.001.0001"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0180944"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICACCI.2017.8126078"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2014.2345663"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3054840"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/s13351-022-1174-7"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbx044"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.3390\/healthcare10060981"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/RadarConf2248738.2022.9764283"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2019.00057"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/RadarConf2147009.2021.9455264"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICSIP55141.2022.9887351"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICSIP57908.2023.10270835"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.3390\/rs14215439"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1093\/acprof:oso\/9780199641178.001.0001"},{"key":"ref36","article-title":"Attention is all you need","author":"Vaswani"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/s00034-023-02454-8"},{"key":"ref38","article-title":"ELINT: The interception and analysis of radar signals","author":"Wiley","year":"2006"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/PIERS.2017.8262194"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/RADAR.2018.8378764"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/tsp.2023.3326650"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/2701126.2701179"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2017.06.035"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2018.2802924"},{"key":"ref45","article-title":"Auto-encoding variational Bayes","volume-title":"Proc. 2nd Int. Conf. Learn. Representations","author":"Kingma","year":"2014"},{"key":"ref46","article-title":"Stochastic backpropagation and approximate inference in deep generative models","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Rezende","year":"2014"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1561\/9781680839135"},{"key":"ref48","article-title":"Layer normalization","author":"Ba","year":"2016"},{"key":"ref49","first-page":"2207","article-title":"Sequential neural models with stochastic layers","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Fraccaro","year":"2016"},{"key":"ref50","first-page":"6551","article-title":"BIVA: A very deep hierarchy of latent variables for generative modeling","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Maale","year":"2019"},{"key":"ref51","article-title":"NVAE: A deep hierarchical variational autoencoder","volume-title":"Proc. 34th Int. Conf. Neural Inf. Process. Syst.","author":"Vahdat","year":"2020"},{"key":"ref52","article-title":"Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting","volume-title":"Proc. 33rd Int. Conf. Neural Inf. Process. Syst.","author":"LI","year":"2019"},{"key":"ref53","first-page":"23592","article-title":"Probabilistic transformer for time series analysis","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Tang","year":"2021"},{"key":"ref54","volume-title":"Radar Handbook","author":"Skolnik","year":"2008"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1049\/iet-rsn.2020.0060"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/ACOMP.2015.24"},{"key":"ref57","article-title":"A closer look at deep learning heuristics: Learning rate restarts, warmup and distillation","author":"Gotmare","year":"2018"},{"key":"ref58","article-title":"A comprehensive survey of regression based loss functions for time series forecasting","author":"Jadon","year":"2022"}],"container-title":["IEEE Transactions on Aerospace and Electronic Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/7\/10963970\/10682802.pdf?arnumber=10682802","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,14]],"date-time":"2025-04-14T13:43:06Z","timestamp":1744638186000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10682802\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4]]},"references-count":58,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/taes.2024.3460752","relation":{},"ISSN":["0018-9251","1557-9603","2371-9877"],"issn-type":[{"value":"0018-9251","type":"print"},{"value":"1557-9603","type":"electronic"},{"value":"2371-9877","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4]]}}}