{"id":"https://openalex.org/W3097374144","doi":"https://doi.org/10.1109/mfi49285.2020.9235255","title":"Bayesian Extended Target Tracking with Automotive Radar using Learned Spatial Distribution Models","display_name":"Bayesian Extended Target Tracking with Automotive Radar using Learned Spatial Distribution Models","publication_year":2020,"publication_date":"2020-09-14","ids":{"openalex":"https://openalex.org/W3097374144","doi":"https://doi.org/10.1109/mfi49285.2020.9235255","mag":"3097374144"},"language":"en","primary_location":{"id":"doi:10.1109/mfi49285.2020.9235255","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mfi49285.2020.9235255","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://resolver.sub.uni-goettingen.de/purl?gro-2/135629","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5003616544","display_name":"Jens Honer","orcid":null},"institutions":[{"id":"https://openalex.org/I4210112099","display_name":"Valeo (Germany)","ror":"https://ror.org/01zkeq752","country_code":"DE","type":"company","lineage":["https://openalex.org/I220619192","https://openalex.org/I4210112099"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Jens Honer","raw_affiliation_strings":["Valeo Schalter und Sensoren GmbH, Bietigheim-Bissingen, Germany"],"affiliations":[{"raw_affiliation_string":"Valeo Schalter und Sensoren GmbH, Bietigheim-Bissingen, Germany","institution_ids":["https://openalex.org/I4210112099"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009737320","display_name":"Hauke Kaulbersch","orcid":null},"institutions":[{"id":"https://openalex.org/I74656192","display_name":"University of G\u00f6ttingen","ror":"https://ror.org/01y9bpm73","country_code":"DE","type":"education","lineage":["https://openalex.org/I74656192"]},{"id":"https://openalex.org/I4210112099","display_name":"Valeo (Germany)","ror":"https://ror.org/01zkeq752","country_code":"DE","type":"company","lineage":["https://openalex.org/I220619192","https://openalex.org/I4210112099"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Hauke Kaulbersch","raw_affiliation_strings":["Data Fusion Group, Institute of Computer Science, University of G\u00f6ttingen, Germany","Valeo Schalter und Sensoren GmbH, Bietigheim-Bissingen, Germany"],"affiliations":[{"raw_affiliation_string":"Data Fusion Group, Institute of Computer Science, University of G\u00f6ttingen, Germany","institution_ids":["https://openalex.org/I74656192"]},{"raw_affiliation_string":"Valeo Schalter und Sensoren GmbH, Bietigheim-Bissingen, Germany","institution_ids":["https://openalex.org/I4210112099"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5003616544"],"corresponding_institution_ids":["https://openalex.org/I4210112099"],"apc_list":null,"apc_paid":null,"fwci":1.6236,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.87557871,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":"2020","issue":null,"first_page":"316","last_page":"322"},"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":0.9998000264167786,"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":0.9998000264167786,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9975000023841858,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9972000122070312,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.5994109511375427},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5789439082145691},{"id":"https://openalex.org/keywords/automotive-industry","display_name":"Automotive industry","score":0.5663607716560364},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.561593770980835},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.546783983707428},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.5083274245262146},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.48899877071380615},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.4755361080169678},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4620174169540405},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4612393081188202},{"id":"https://openalex.org/keywords/bernoullis-principle","display_name":"Bernoulli's principle","score":0.45518890023231506},{"id":"https://openalex.org/keywords/radar-tracker","display_name":"Radar tracker","score":0.4310300350189209},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4128195643424988},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4099096655845642},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3788849711418152},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3727785348892212},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3685033321380615},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.20205730199813843}],"concepts":[{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.5994109511375427},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5789439082145691},{"id":"https://openalex.org/C526921623","wikidata":"https://www.wikidata.org/wiki/Q190117","display_name":"Automotive industry","level":2,"score":0.5663607716560364},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.561593770980835},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.546783983707428},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.5083274245262146},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.48899877071380615},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.4755361080169678},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4620174169540405},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4612393081188202},{"id":"https://openalex.org/C152361515","wikidata":"https://www.wikidata.org/wiki/Q181328","display_name":"Bernoulli's principle","level":2,"score":0.45518890023231506},{"id":"https://openalex.org/C32283439","wikidata":"https://www.wikidata.org/wiki/Q1407014","display_name":"Radar tracker","level":3,"score":0.4310300350189209},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4128195643424988},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4099096655845642},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3788849711418152},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3727785348892212},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3685033321380615},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.20205730199813843},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/mfi49285.2020.9235255","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mfi49285.2020.9235255","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","raw_type":"proceedings-article"},{"id":"pmh:oai:publications.goettingen-research-online.de:2/135629","is_oa":true,"landing_page_url":"https://resolver.sub.uni-goettingen.de/purl?gro-2/135629","pdf_url":null,"source":{"id":"https://openalex.org/S4306401634","display_name":"GoeScholar  The Publication Server of the Georg-August-Universit\u00e4t G\u00f6ttingen (Georg-August-Universit\u00e4t G\u00f6ttingen)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210122495","host_organization_name":"Asklepios Klinik St. Georg","host_organization_lineage":["https://openalex.org/I4210122495"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"mag:3185655660","is_oa":false,"landing_page_url":"https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=202002249044398413","pdf_url":null,"source":{"id":"https://openalex.org/S4306512817","display_name":"IEEE Conference Proceedings","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":"IEEE Conference Proceedings","raw_type":null}],"best_oa_location":{"id":"pmh:oai:publications.goettingen-research-online.de:2/135629","is_oa":true,"landing_page_url":"https://resolver.sub.uni-goettingen.de/purl?gro-2/135629","pdf_url":null,"source":{"id":"https://openalex.org/S4306401634","display_name":"GoeScholar  The Publication Server of the Georg-August-Universit\u00e4t G\u00f6ttingen (Georg-August-Universit\u00e4t G\u00f6ttingen)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210122495","host_organization_name":"Asklepios Klinik St. Georg","host_organization_lineage":["https://openalex.org/I4210122495"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/conferenceObject"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W34992941","https://openalex.org/W652333456","https://openalex.org/W1585398504","https://openalex.org/W1604155682","https://openalex.org/W1642300317","https://openalex.org/W1728842521","https://openalex.org/W1941363674","https://openalex.org/W1967583480","https://openalex.org/W1973789816","https://openalex.org/W2034999760","https://openalex.org/W2115579991","https://openalex.org/W2120004402","https://openalex.org/W2122469428","https://openalex.org/W2140242774","https://openalex.org/W2142300547","https://openalex.org/W2163015601","https://openalex.org/W2170790908","https://openalex.org/W2229633322","https://openalex.org/W2242464395","https://openalex.org/W2298674625","https://openalex.org/W2588070846","https://openalex.org/W2740326037","https://openalex.org/W2745130738","https://openalex.org/W2892055118","https://openalex.org/W2963194653","https://openalex.org/W2963272312","https://openalex.org/W3012381239","https://openalex.org/W3035574168","https://openalex.org/W3147228951","https://openalex.org/W4297789063","https://openalex.org/W6618308950","https://openalex.org/W6634824184","https://openalex.org/W6637572315","https://openalex.org/W6640408507","https://openalex.org/W6677727547","https://openalex.org/W6678239302","https://openalex.org/W6683745938","https://openalex.org/W6689428209","https://openalex.org/W6697381183","https://openalex.org/W6745260972","https://openalex.org/W6760782946","https://openalex.org/W6774789169","https://openalex.org/W6808926695"],"related_works":["https://openalex.org/W1619264321","https://openalex.org/W2899588557","https://openalex.org/W4254565718","https://openalex.org/W3094386089","https://openalex.org/W2180382450","https://openalex.org/W2129020938","https://openalex.org/W4298392611","https://openalex.org/W4233030079","https://openalex.org/W1992295166","https://openalex.org/W2143508933"],"abstract_inverted_index":{"We":[0,53],"apply":[1],"the":[2,45,65,104,108],"concept":[3],"of":[4,23,48,67,99],"random":[5,68],"set":[6,69],"cluster":[7,70],"processes":[8],"in":[9,107],"combination":[10],"with":[11],"a":[12,27,33,49,55,96],"learned":[13,31],"measurement":[14,46],"model":[15],"to":[16,85],"extended":[17,62],"target":[18,28,63],"tracking.":[19],"The":[20,39],"spatial":[21],"distribution":[22],"measurements":[24],"generated":[25],"by":[26,64],"vehicle":[29],"is":[30,41,74,83],"via":[32],"variational":[34],"Gaussian":[35],"mixture":[36],"(VGM)":[37],"model.":[38],"VGM":[40],"then":[42],"interpreted":[43],"as":[44],"likelihood":[47],"Multi-Bernoulli":[50],"(MB)":[51],"distribution.":[52],"derive":[54],"closed-form":[56],"Bayesian":[57],"recursion":[58],"for":[59,77],"tracking":[60],"an":[61],"use":[66],"process.":[71],"This":[72],"formulation":[73],"particularly":[75],"successful":[76],"sparse":[78],"and":[79,82,89],"noisy":[80],"measurements,":[81],"applied":[84],"automotive":[86],"Radio":[87],"Detection":[88],"Ranging":[90],"(RADAR)":[91],"detections.":[92],"Last,":[93],"we":[94],"provide":[95],"large-scale":[97],"evaluation":[98],"our":[100],"approach":[101],"based":[102],"on":[103],"data":[105,110],"published":[106],"Nuscenes":[109],"set.":[111]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":2}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}