{"id":"https://openalex.org/W3200702630","doi":"https://doi.org/10.1109/tnnls.2021.3112460","title":"DynaNet: Neural Kalman Dynamical Model for Motion Estimation and Prediction","display_name":"DynaNet: Neural Kalman Dynamical Model for Motion Estimation and Prediction","publication_year":2021,"publication_date":"2021-09-24","ids":{"openalex":"https://openalex.org/W3200702630","doi":"https://doi.org/10.1109/tnnls.2021.3112460","mag":"3200702630","pmid":"https://pubmed.ncbi.nlm.nih.gov/34559667"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2021.3112460","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2021.3112460","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Changhao Chen","orcid":"https://orcid.org/0000-0002-8341-6399"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Changhao Chen","raw_affiliation_strings":["College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China"],"affiliations":[{"raw_affiliation_string":"College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Chris Xiaoxuan Lu","orcid":null},"institutions":[{"id":"https://openalex.org/I98677209","display_name":"University of Edinburgh","ror":"https://ror.org/01nrxwf90","country_code":"GB","type":"education","lineage":["https://openalex.org/I98677209"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Chris Xiaoxuan Lu","raw_affiliation_strings":["School of Informatics, University of Edinburgh, Edinburgh, U.K"],"affiliations":[{"raw_affiliation_string":"School of Informatics, University of Edinburgh, Edinburgh, U.K","institution_ids":["https://openalex.org/I98677209"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Bing Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I40120149","display_name":"University of Oxford","ror":"https://ror.org/052gg0110","country_code":"GB","type":"education","lineage":["https://openalex.org/I40120149"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Bing Wang","raw_affiliation_strings":["Department of Computer Science, University of Oxford, Oxford, U.K"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Oxford, Oxford, U.K","institution_ids":["https://openalex.org/I40120149"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Niki Trigoni","orcid":"https://orcid.org/0000-0001-6236-9645"},"institutions":[{"id":"https://openalex.org/I40120149","display_name":"University of Oxford","ror":"https://ror.org/052gg0110","country_code":"GB","type":"education","lineage":["https://openalex.org/I40120149"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Niki Trigoni","raw_affiliation_strings":["Department of Computer Science, University of Oxford, Oxford, U.K"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Oxford, Oxford, U.K","institution_ids":["https://openalex.org/I40120149"]}]},{"author_position":"last","author":{"id":null,"display_name":"Andrew Markham","orcid":null},"institutions":[{"id":"https://openalex.org/I40120149","display_name":"University of Oxford","ror":"https://ror.org/052gg0110","country_code":"GB","type":"education","lineage":["https://openalex.org/I40120149"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Andrew Markham","raw_affiliation_strings":["Department of Computer Science, University of Oxford, Oxford, U.K"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Oxford, Oxford, U.K","institution_ids":["https://openalex.org/I40120149"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I170215575"],"apc_list":null,"apc_paid":null,"fwci":3.6257,"has_fulltext":false,"cited_by_count":62,"citation_normalized_percentile":{"value":0.93134294,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"32","issue":"12","first_page":"5479","last_page":"5491"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.7717000246047974,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.7717000246047974,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.08070000261068344,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.017400000244379044,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.7376999855041504},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.6467999815940857},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5752999782562256},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.513700008392334},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4512999951839447},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.44760000705718994},{"id":"https://openalex.org/keywords/dynamical-systems-theory","display_name":"Dynamical systems theory","score":0.38530001044273376},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.3630000054836273},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.35350000858306885}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.7376999855041504},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.6467999815940857},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6337000131607056},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5752999782562256},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5547999739646912},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.513700008392334},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47130000591278076},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4512999951839447},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.44760000705718994},{"id":"https://openalex.org/C79379906","wikidata":"https://www.wikidata.org/wiki/Q3174497","display_name":"Dynamical systems theory","level":2,"score":0.38530001044273376},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.3630000054836273},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.35350000858306885},{"id":"https://openalex.org/C24574437","wikidata":"https://www.wikidata.org/wiki/Q7135228","display_name":"Parametric model","level":3,"score":0.34619998931884766},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.33739998936653137},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.32019999623298645},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.31949999928474426},{"id":"https://openalex.org/C68022304","wikidata":"https://www.wikidata.org/wiki/Q842217","display_name":"Bayes estimator","level":3,"score":0.30309998989105225},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.2978000044822693},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.2924000024795532},{"id":"https://openalex.org/C50897621","wikidata":"https://www.wikidata.org/wiki/Q2665508","display_name":"Hybrid system","level":2,"score":0.2838999927043915},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.26980000734329224},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.2680000066757202},{"id":"https://openalex.org/C40343088","wikidata":"https://www.wikidata.org/wiki/Q3059012","display_name":"Recursive Bayesian estimation","level":3,"score":0.2630000114440918},{"id":"https://openalex.org/C206833254","wikidata":"https://www.wikidata.org/wiki/Q5421817","display_name":"Extended Kalman filter","level":3,"score":0.2524999976158142}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2021.3112460","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2021.3112460","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:34559667","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/34559667","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on neural networks and learning systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4253207184","display_name":null,"funder_award_id":"EP/S030832/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G7675125483","display_name":null,"funder_award_id":"62073331","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8490148696","display_name":null,"funder_award_id":"62103427","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8632079472","display_name":"ACE-OPS: From Autonomy to Cognitive assistance in Emergency OPerationS","funder_award_id":"EP/S030832/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":56,"referenced_works":["https://openalex.org/W612478963","https://openalex.org/W1612997784","https://openalex.org/W1689711448","https://openalex.org/W1735317348","https://openalex.org/W1970504153","https://openalex.org/W1976439748","https://openalex.org/W1980586072","https://openalex.org/W2056358962","https://openalex.org/W2064480843","https://openalex.org/W2064675550","https://openalex.org/W2091790851","https://openalex.org/W2105934661","https://openalex.org/W2108134361","https://openalex.org/W2115579991","https://openalex.org/W2121546278","https://openalex.org/W2126877537","https://openalex.org/W2140599684","https://openalex.org/W2152671441","https://openalex.org/W2153054365","https://openalex.org/W2168676389","https://openalex.org/W2214788824","https://openalex.org/W2598706937","https://openalex.org/W2609883120","https://openalex.org/W2745859992","https://openalex.org/W2749379418","https://openalex.org/W2772294549","https://openalex.org/W2795645133","https://openalex.org/W2798483995","https://openalex.org/W2802025241","https://openalex.org/W2962816904","https://openalex.org/W2963371290","https://openalex.org/W2963706662","https://openalex.org/W2964203186","https://openalex.org/W2964248288","https://openalex.org/W2976669726","https://openalex.org/W3044667778","https://openalex.org/W4243425824","https://openalex.org/W4246614213","https://openalex.org/W6636774829","https://openalex.org/W6640963894","https://openalex.org/W6677571061","https://openalex.org/W6711952718","https://openalex.org/W6712395597","https://openalex.org/W6712730493","https://openalex.org/W6728354068","https://openalex.org/W6730329805","https://openalex.org/W6731334075","https://openalex.org/W6741853627","https://openalex.org/W6744063608","https://openalex.org/W6749453440","https://openalex.org/W6750106230","https://openalex.org/W6752110675","https://openalex.org/W6752763597","https://openalex.org/W6753773310","https://openalex.org/W6754779804","https://openalex.org/W6767088534"],"related_works":[],"abstract_inverted_index":{"Dynamical":[0],"models":[1,12,117],"estimate":[2],"and":[3,35,37,60,110,140,163,174,191],"predict":[4],"the":[5,17,33,40,46,57,68,157,172,209],"temporal":[6],"evolution":[7],"of":[8,89,160,179,205,211],"physical":[9],"systems.":[10],"State-space":[11],"(SSMs)":[13],"in":[14,31,92,123,171],"particular":[15],"represent":[16],"system":[18],"dynamics":[19],"with":[20,83],"many":[21],"desirable":[22],"properties,":[23,206],"such":[24,126,207],"as":[25,78,127,208],"being":[26],"able":[27],"to":[28,55,64,81,95,99,112,120,155],"model":[29,34,152],"uncertainty":[30],"both":[32,161],"measurements,":[36],"optimal":[38],"(in":[39],"Bayesian":[41],"sense)":[42],"recursive":[43],"formulations,":[44],"e.g.,":[45,72],"Kalman":[47,150],"filter.":[48],"However,":[49],"they":[50],"require":[51],"significant":[52],"domain":[53],"knowledge":[54],"derive":[56],"parametric":[58],"form":[59],"considerable":[61],"hand":[62],"tuning":[63],"correctly":[65],"set":[66],"all":[67],"parameters.":[69],"Data-driven":[70],"techniques,":[71],"recurrent":[73],"neural":[74,149,165],"networks,":[75],"have":[76],"emerged":[77],"compelling":[79],"alternatives":[80],"SSMs":[82],"wide":[84],"success":[85],"across":[86],"a":[87,136,177],"number":[88,178],"challenging":[90,181],"tasks,":[91,182],"part":[93],"due":[94],"their":[96],"impressive":[97],"capability":[98],"extract":[100],"relevant":[101],"features":[102],"from":[103],"rich":[104],"inputs.":[105],"They,":[106],"however,":[107],"lack":[108],"interpretability":[109],"robustness":[111],"unseen":[113],"conditions.":[114],"Thus,":[115],"data-driven":[116],"are":[118],"hard":[119],"be":[121,145],"applied":[122],"safety-critical":[124],"applications,":[125],"self-driving":[128],"vehicles.":[129],"In":[130,194],"this":[131],"work,":[132],"we":[133,196],"present":[134],"DynaNet,":[135],"hybrid":[137],"deep":[138,164],"learning":[139],"time-varying":[141],"SSM,":[142],"which":[143],"can":[144,200],"trained":[146],"end-to-end.":[147],"Our":[148],"dynamical":[151],"allows":[153],"us":[154],"exploit":[156],"relative":[158],"merits":[159],"SSM":[162],"networks.":[166],"We":[167],"demonstrate":[168],"its":[169],"effectiveness":[170],"estimation":[173],"prediction":[175],"on":[176],"physically":[180],"including":[183],"visual":[184],"odometry,":[185],"sensor":[186],"fusion":[187],"for":[188],"visual-inertial":[189],"navigation,":[190],"motion":[192],"prediction.":[193],"addition,":[195],"show":[197],"how":[198],"DynaNet":[199],"indicate":[201],"failures":[202],"through":[203],"investigation":[204],"rate":[210],"innovation":[212],"(Kalman":[213],"gain).":[214]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":22},{"year":2024,"cited_by_count":17},{"year":2023,"cited_by_count":14},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":1}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2021-09-27T00:00:00"}
