{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T21:53:03Z","timestamp":1766267583250,"version":"3.37.3"},"reference-count":42,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61663010","61963017"],"award-info":[{"award-number":["61663010","61963017"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013064","name":"Key Research and Development Project of Jiangxi Province, China","doi-asserted-by":"publisher","award":["20171BBH80024"],"award-info":[{"award-number":["20171BBH80024"]}],"id":[{"id":"10.13039\/501100013064","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Outstanding Youth Planning Project of Jiangxi Province, China","award":["20192BCBL23004"],"award-info":[{"award-number":["20192BCBL23004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/access.2020.3031618","type":"journal-article","created":{"date-parts":[[2020,10,16]],"date-time":"2020-10-16T19:45:59Z","timestamp":1602877559000},"page":"188475-188487","source":"Crossref","is-referenced-by-count":3,"title":["Mobile Robot Localization Based on Gradient Propagation Particle Filter Network"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9027-3261","authenticated-orcid":false,"given":"Heng","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiemao","family":"Wen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2691-034X","authenticated-orcid":false,"given":"Yanli","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9721-1565","authenticated-orcid":false,"given":"Wenqing","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0394-4635","authenticated-orcid":false,"given":"Naixue","family":"Xiong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","first-page":"2629","article-title":"Neural adaptive sequential Monte Carlo","author":"guz ghahramani","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref38","first-page":"1","article-title":"Auto-encoding sequential Monte Carlo","author":"le","year":"2017","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref33","first-page":"5484","article-title":"Task-based end-to-end model learning in stochastic optimization","author":"donti","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref32","first-page":"1822","article-title":"Learning to Search with MCTSnets","author":"guez","year":"2018","journal-title":"Proc 35th Int Conf Mach Learn"},{"key":"ref31","article-title":"Tutorial on variational autoencoders","author":"doersch","year":"2016","journal-title":"arXiv 1606 05908"},{"key":"ref30","first-page":"1","article-title":"Auto-encoding variational Bayes","author":"kingma","year":"2013","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref37","first-page":"6573","article-title":"Filtering variational objectives","author":"maddison","year":"2017","journal-title":"Proc Neural Inf Process Syst"},{"key":"ref36","first-page":"968","article-title":"Variational sequential Monte Carlo","author":"naesseth","year":"2018","journal-title":"Proc Int Conf Artifi Intell"},{"key":"ref35","first-page":"2117","article-title":"Deep variational reinforcement learning for POMDPs","author":"igl","year":"2018","journal-title":"Proc 35th Int Conf Mach Learing"},{"key":"ref34","article-title":"Path integral networks: End-to-end differentiable optimal control","author":"okada","year":"2017","journal-title":"arXiv 1706 09597"},{"key":"ref10","first-page":"353","article-title":"Model-based online learning of POMDPs","author":"shanir","year":"2005","journal-title":"Proc Eur Conf Mach Learn"},{"key":"ref40","first-page":"1","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014","journal-title":"Proc of the Int Conf on Learning Representations (ICLR)"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1177\/0278364911404092"},{"key":"ref12","first-page":"679","article-title":"Learning probabilistic models of link structure","volume":"3","author":"getoor","year":"2003","journal-title":"J Mach Learn Res"},{"key":"ref13","first-page":"4694","article-title":"QMDP-Net: Deep learning for planning under partial observability","author":"karkus","year":"2017","journal-title":"Proc Neural Inf Process Syst"},{"key":"ref14","article-title":"End-to-end learnable histogram filters","author":"jonschkowski","year":"2016","journal-title":"NIPS workshop on Deep Learning for Action and Interaction"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.15607\/RSS.2018.XIV.001"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2015.2463671"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2017.2705103"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2952161"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2010.2046366"},{"key":"ref28","first-page":"1","article-title":"Discriminative particle filter reinforcement learning for complex partial observations","author":"ma","year":"2020","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2019.2906640"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5952"},{"key":"ref3","article-title":"DualSMC: Tunneling differentiable filtering and planning under continuous POMDPs","author":"wang","year":"2019","journal-title":"arXiv 1909 13003"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1080\/01691864.2018.1509726"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.769"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2961740"},{"key":"ref8","first-page":"259","article-title":"Online algorithms for POMDPs with continuous state, action, and observation spaces","author":"sunberg","year":"2017","journal-title":"Proc Int Conf Plan Sched"},{"key":"ref7","first-page":"1772","article-title":"DESPOT: Online POMDP planning with regularization","author":"somani","year":"2013","journal-title":"Proc Neural Inf Process Syst"},{"key":"ref2","first-page":"1","volume":"830","author":"gordonb ristic","year":"2004","journal-title":"Beyond the Kalman Filter Particle Filters for Tracking Applications"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2910163"},{"key":"ref1","first-page":"361","article-title":"The condensation algorithm-conditional density propagation and applications to visual tracking","author":"blake","year":"1997","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref20","first-page":"2154","article-title":"Value iteration networks","author":"tamar","year":"2016","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref22","article-title":"TreeQN and ATreeC: Differentiable tree planning for deep reinforcement learning","author":"farquhar","year":"2018","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref21","first-page":"6118","article-title":"Value prediction network","author":"oh","year":"2017","journal-title":"Proc Neural Inf Process Syst"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref24","first-page":"1","article-title":"Deep variational bayes filters: Unsupervised learning of state space models from raw data","author":"karl","year":"2017","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref41","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"J Mach Learn Res"},{"key":"ref23","first-page":"136","article-title":"OptNet: Differentiable optimization as a layer in neural networks","author":"amos","year":"2017","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref26","first-page":"169","article-title":"Particle filter networks with application to visual localization","author":"karkus","year":"2018","journal-title":"Proc Conf Robot Learn"},{"key":"ref25","first-page":"4376","article-title":"Backprop KF: Learning discriminative deterministic state estimators","author":"haarnoja","year":"2016","journal-title":"Proc Adv Neural Inf Process Syst"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8948470\/09226533.pdf?arnumber=9226533","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T15:57:43Z","timestamp":1642003063000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9226533\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":42,"URL":"https:\/\/doi.org\/10.1109\/access.2020.3031618","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2020]]}}}