{"id":"https://openalex.org/W3086356866","doi":"https://doi.org/10.23919/fusion45008.2020.9190484","title":"Time-Dependent State Prediction for the Kalman Filter Based on Recurrent Neural Networks","display_name":"Time-Dependent State Prediction for the Kalman Filter Based on Recurrent Neural Networks","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3086356866","doi":"https://doi.org/10.23919/fusion45008.2020.9190484","mag":"3086356866"},"language":"en","primary_location":{"id":"doi:10.23919/fusion45008.2020.9190484","is_oa":false,"landing_page_url":"https://doi.org/10.23919/fusion45008.2020.9190484","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 23rd International Conference on Information Fusion (FUSION)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102815542","display_name":"Steffen Jung","orcid":"https://orcid.org/0000-0001-9644-4930"},"institutions":[{"id":"https://openalex.org/I4210166245","display_name":"Fraunhofer Institute for Communication, Information Processing and Ergonomics","ror":"https://ror.org/05nn0gw40","country_code":"DE","type":"facility","lineage":["https://openalex.org/I4210166245","https://openalex.org/I4923324"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Steffen Jung","raw_affiliation_strings":["Fraunhofer Institute for Communication, Information Processing and Ergonomics (FKIE),Department Sensor Data and Information Fusion (SDF),Fraunhoferstr, Wachtberg,Germany,53343"],"affiliations":[{"raw_affiliation_string":"Fraunhofer Institute for Communication, Information Processing and Ergonomics (FKIE),Department Sensor Data and Information Fusion (SDF),Fraunhoferstr, Wachtberg,Germany,53343","institution_ids":["https://openalex.org/I4210166245"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024026597","display_name":"Isabel Schlangen","orcid":"https://orcid.org/0000-0002-1583-5989"},"institutions":[{"id":"https://openalex.org/I4210166245","display_name":"Fraunhofer Institute for Communication, Information Processing and Ergonomics","ror":"https://ror.org/05nn0gw40","country_code":"DE","type":"facility","lineage":["https://openalex.org/I4210166245","https://openalex.org/I4923324"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Isabel Schlangen","raw_affiliation_strings":["Fraunhofer Institute for Communication, Information Processing and Ergonomics (FKIE),Department Sensor Data and Information Fusion (SDF),Fraunhoferstr, Wachtberg,Germany,53343"],"affiliations":[{"raw_affiliation_string":"Fraunhofer Institute for Communication, Information Processing and Ergonomics (FKIE),Department Sensor Data and Information Fusion (SDF),Fraunhoferstr, Wachtberg,Germany,53343","institution_ids":["https://openalex.org/I4210166245"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018784354","display_name":"Alexander Charlish","orcid":"https://orcid.org/0000-0003-0511-2426"},"institutions":[{"id":"https://openalex.org/I4210166245","display_name":"Fraunhofer Institute for Communication, Information Processing and Ergonomics","ror":"https://ror.org/05nn0gw40","country_code":"DE","type":"facility","lineage":["https://openalex.org/I4210166245","https://openalex.org/I4923324"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Alexander Charlish","raw_affiliation_strings":["Fraunhofer Institute for Communication, Information Processing and Ergonomics (FKIE),Department Sensor Data and Information Fusion (SDF),Fraunhoferstr, Wachtberg,Germany,53343"],"affiliations":[{"raw_affiliation_string":"Fraunhofer Institute for Communication, Information Processing and Ergonomics (FKIE),Department Sensor Data and Information Fusion (SDF),Fraunhoferstr, Wachtberg,Germany,53343","institution_ids":["https://openalex.org/I4210166245"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5102815542"],"corresponding_institution_ids":["https://openalex.org/I4210166245"],"apc_list":null,"apc_paid":null,"fwci":0.3977,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.6886359,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9821000099182129,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11622","display_name":"Maritime Navigation and Safety","score":0.9596999883651733,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.825945258140564},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6838551163673401},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5681617259979248},{"id":"https://openalex.org/keywords/extended-kalman-filter","display_name":"Extended Kalman filter","score":0.5329670906066895},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5137351751327515},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.5021028518676758},{"id":"https://openalex.org/keywords/position","display_name":"Position (finance)","score":0.48990052938461304},{"id":"https://openalex.org/keywords/ensemble-kalman-filter","display_name":"Ensemble Kalman filter","score":0.4777861535549164},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.4746837317943573},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4693782329559326},{"id":"https://openalex.org/keywords/alpha-beta-filter","display_name":"Alpha beta filter","score":0.4644944965839386},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.45842137932777405},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.4416095018386841},{"id":"https://openalex.org/keywords/state-vector","display_name":"State vector","score":0.4381418228149414},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.41673800349235535},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.415780246257782},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38604307174682617},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3563488721847534},{"id":"https://openalex.org/keywords/moving-horizon-estimation","display_name":"Moving horizon estimation","score":0.1512901484966278},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.09610304236412048}],"concepts":[{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.825945258140564},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6838551163673401},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5681617259979248},{"id":"https://openalex.org/C206833254","wikidata":"https://www.wikidata.org/wiki/Q5421817","display_name":"Extended Kalman filter","level":3,"score":0.5329670906066895},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5137351751327515},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.5021028518676758},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.48990052938461304},{"id":"https://openalex.org/C79334102","wikidata":"https://www.wikidata.org/wiki/Q3072268","display_name":"Ensemble Kalman filter","level":4,"score":0.4777861535549164},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.4746837317943573},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4693782329559326},{"id":"https://openalex.org/C11588082","wikidata":"https://www.wikidata.org/wiki/Q4735154","display_name":"Alpha beta filter","level":5,"score":0.4644944965839386},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.45842137932777405},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.4416095018386841},{"id":"https://openalex.org/C2777798563","wikidata":"https://www.wikidata.org/wiki/Q7603916","display_name":"State vector","level":2,"score":0.4381418228149414},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.41673800349235535},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.415780246257782},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38604307174682617},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3563488721847534},{"id":"https://openalex.org/C50050547","wikidata":"https://www.wikidata.org/wiki/Q6927137","display_name":"Moving horizon estimation","level":4,"score":0.1512901484966278},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.09610304236412048},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C74650414","wikidata":"https://www.wikidata.org/wiki/Q11397","display_name":"Classical mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.23919/fusion45008.2020.9190484","is_oa":false,"landing_page_url":"https://doi.org/10.23919/fusion45008.2020.9190484","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 23rd International Conference on Information Fusion (FUSION)","raw_type":"proceedings-article"},{"id":"pmh:oai:publica.fraunhofer.de:publica/409558","is_oa":false,"landing_page_url":"https://publica.fraunhofer.de/handle/publica/409558","pdf_url":null,"source":{"id":"https://openalex.org/S4306400318","display_name":"Fraunhofer-Publica (Fraunhofer-Gesellschaft)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4923324","host_organization_name":"Fraunhofer-Gesellschaft","host_organization_lineage":["https://openalex.org/I4923324"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"conference paper"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1483307070","https://openalex.org/W1519202893","https://openalex.org/W1541167037","https://openalex.org/W1985687306","https://openalex.org/W1994461138","https://openalex.org/W1995562189","https://openalex.org/W2020934227","https://openalex.org/W2024228246","https://openalex.org/W2064675550","https://openalex.org/W2076448662","https://openalex.org/W2077934202","https://openalex.org/W2105934661","https://openalex.org/W2136016850","https://openalex.org/W2140242774","https://openalex.org/W2143612262","https://openalex.org/W2413904250","https://openalex.org/W2463955103","https://openalex.org/W2515297606","https://openalex.org/W2607941059","https://openalex.org/W3011555667","https://openalex.org/W3034826969","https://openalex.org/W3045086295","https://openalex.org/W6680568406","https://openalex.org/W6726101916","https://openalex.org/W6775531992"],"related_works":["https://openalex.org/W3190919334","https://openalex.org/W2263628162","https://openalex.org/W2336351623","https://openalex.org/W4324121876","https://openalex.org/W2273094032","https://openalex.org/W2354442010","https://openalex.org/W2137760344","https://openalex.org/W1593244281","https://openalex.org/W2129008173","https://openalex.org/W2150837402"],"abstract_inverted_index":{"Traditional":[0],"formulations":[1],"of":[2,27,68,101],"the":[3,20,54,64,102,111,135],"well-established":[4],"Kalman":[5,58,92],"filter":[6],"build":[7],"upon":[8],"prediction":[9,100],"models":[10],"which":[11,23,62,96],"are":[12,114],"linear":[13],"and":[14],"Gaussian,":[15],"moreover":[16],"they":[17],"usually":[18],"adopt":[19],"Markov":[21],"property":[22],"excludes":[24],"any":[25],"form":[26],"long-term":[28],"temporal":[29],"dependencies.":[30],"However,":[31],"targets":[32],"might":[33],"follow":[34],"specific":[35],"behavioural":[36],"patterns":[37],"based":[38,71],"on,":[39],"e.g.,":[40],"their":[41],"origin":[42],"or":[43,124],"destination,":[44],"therefore":[45],"time":[46],"dependencies":[47],"become":[48],"highly":[49,115],"relevant.":[50],"In":[51],"this":[52],"article,":[53],"recently":[55],"developed":[56],"Mnemonic":[57],"Filter":[59,93],"is":[60,94],"analysed":[61],"predicts":[63],"full":[65],"Gaussian":[66],"density":[67],"a":[69,77,87,99],"target":[70,103],"on":[72],"its":[73],"previous":[74],"position":[75],"using":[76],"recurrent":[78],"neural":[79],"network":[80],"with":[81,120],"Long":[82,89],"Short-Term":[83,90],"Memory.":[84],"For":[85],"comparison,":[86],"simpler":[88],"Memory":[91],"introduced":[95],"only":[97],"provides":[98],"state":[104],"vector.":[105],"The":[106],"presented":[107],"experiments":[108],"suggest":[109],"that":[110],"learning-based":[112],"approaches":[113],"relevant":[116],"for":[117],"time-dependent":[118],"scenarios":[119],"low":[121],"detection":[122],"rates":[123],"possible":[125],"occlusions.":[126],"Furthermore,":[127],"uncertainty":[128],"estimation":[129],"plays":[130],"an":[131],"important":[132],"role":[133],"in":[134],"filtering":[136],"process.":[137]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
