{"id":"https://openalex.org/W4372270123","doi":"https://doi.org/10.1109/icassp49357.2023.10095899","title":"An Augmented Gaussian Sum Filter through a mixture Decomposition","display_name":"An Augmented Gaussian Sum Filter through a mixture Decomposition","publication_year":2023,"publication_date":"2023-05-05","ids":{"openalex":"https://openalex.org/W4372270123","doi":"https://doi.org/10.1109/icassp49357.2023.10095899"},"language":"en","primary_location":{"id":"doi:10.1109/icassp49357.2023.10095899","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49357.2023.10095899","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5090247507","display_name":"Kostas Tsampourakis","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":true,"raw_author_name":"Kostas Tsampourakis","raw_affiliation_strings":["University of Edinburgh,School of Mathematics,United Kingdom","School of Mathematics, University of Edinburgh, United Kingdom"],"affiliations":[{"raw_affiliation_string":"University of Edinburgh,School of Mathematics,United Kingdom","institution_ids":["https://openalex.org/I98677209"]},{"raw_affiliation_string":"School of Mathematics, University of Edinburgh, United Kingdom","institution_ids":["https://openalex.org/I98677209"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085691944","display_name":"V\u0301\u0131ctor Elvira","orcid":"https://orcid.org/0000-0002-8967-4866"},"institutions":[{"id":"https://openalex.org/I4210128584","display_name":"The Alan Turing Institute","ror":"https://ror.org/035dkdb55","country_code":"GB","type":"facility","lineage":["https://openalex.org/I4210128584"]},{"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":"V\u00edctor Elvira","raw_affiliation_strings":["University of Edinburgh,School of Mathematics,United Kingdom","The Alan Turing Institute, United Kingdom","School of Mathematics, University of Edinburgh, United Kingdom"],"affiliations":[{"raw_affiliation_string":"University of Edinburgh,School of Mathematics,United Kingdom","institution_ids":["https://openalex.org/I98677209"]},{"raw_affiliation_string":"The Alan Turing Institute, United Kingdom","institution_ids":["https://openalex.org/I4210128584"]},{"raw_affiliation_string":"School of Mathematics, University of Edinburgh, United Kingdom","institution_ids":["https://openalex.org/I98677209"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5090247507"],"corresponding_institution_ids":["https://openalex.org/I98677209"],"apc_list":null,"apc_paid":null,"fwci":0.3497,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.63062675,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"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":0.9994000196456909,"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":0.9994000196456909,"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":0.9710999727249146,"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":0.9327999949455261,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.7249444127082825},{"id":"https://openalex.org/keywords/extended-kalman-filter","display_name":"Extended Kalman filter","score":0.5632913708686829},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.5487325191497803},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.518135666847229},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5159822106361389},{"id":"https://openalex.org/keywords/gaussian-filter","display_name":"Gaussian filter","score":0.5123292207717896},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.488489031791687},{"id":"https://openalex.org/keywords/multivariate-normal-distribution","display_name":"Multivariate normal distribution","score":0.48756498098373413},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4395748972892761},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.43635955452919006},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.4356079697608948},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.4325111210346222},{"id":"https://openalex.org/keywords/ensemble-kalman-filter","display_name":"Ensemble Kalman filter","score":0.41992878913879395},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.4155755043029785},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.33050769567489624},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.1756475269794464},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.13201633095741272},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.08391889929771423}],"concepts":[{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.7249444127082825},{"id":"https://openalex.org/C206833254","wikidata":"https://www.wikidata.org/wiki/Q5421817","display_name":"Extended Kalman filter","level":3,"score":0.5632913708686829},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.5487325191497803},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.518135666847229},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5159822106361389},{"id":"https://openalex.org/C65892221","wikidata":"https://www.wikidata.org/wiki/Q1113935","display_name":"Gaussian filter","level":3,"score":0.5123292207717896},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.488489031791687},{"id":"https://openalex.org/C177384507","wikidata":"https://www.wikidata.org/wiki/Q1149000","display_name":"Multivariate normal distribution","level":3,"score":0.48756498098373413},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4395748972892761},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.43635955452919006},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.4356079697608948},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.4325111210346222},{"id":"https://openalex.org/C79334102","wikidata":"https://www.wikidata.org/wiki/Q3072268","display_name":"Ensemble Kalman filter","level":4,"score":0.41992878913879395},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.4155755043029785},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33050769567489624},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.1756475269794464},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.13201633095741272},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.08391889929771423},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","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/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp49357.2023.10095899","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49357.2023.10095899","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320320883","display_name":"Agence Nationale de la Recherche","ror":"https://ror.org/00rbzpz17"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W1749494163","https://openalex.org/W1970319972","https://openalex.org/W2022638422","https://openalex.org/W2027195108","https://openalex.org/W2043357888","https://openalex.org/W2051775893","https://openalex.org/W2054091988","https://openalex.org/W2070898270","https://openalex.org/W2081324301","https://openalex.org/W2098613108","https://openalex.org/W2099371695","https://openalex.org/W2099867508","https://openalex.org/W2105934661","https://openalex.org/W2111787305","https://openalex.org/W2151084831","https://openalex.org/W2179435707","https://openalex.org/W2495678645","https://openalex.org/W2982582821","https://openalex.org/W3000108011","https://openalex.org/W3104127448","https://openalex.org/W3171485246","https://openalex.org/W4213059792","https://openalex.org/W4232464081","https://openalex.org/W4236076921","https://openalex.org/W4255133955","https://openalex.org/W4256130019","https://openalex.org/W4292403327","https://openalex.org/W4293252588","https://openalex.org/W4302356748","https://openalex.org/W6676085441","https://openalex.org/W6676780147","https://openalex.org/W6796030627"],"related_works":["https://openalex.org/W3184792886","https://openalex.org/W2025659129","https://openalex.org/W1992295166","https://openalex.org/W2143508933","https://openalex.org/W2159766882","https://openalex.org/W2004180994","https://openalex.org/W2114171029","https://openalex.org/W4205181450","https://openalex.org/W4206030159","https://openalex.org/W1510085384"],"abstract_inverted_index":{"Bayesian":[0],"filtering":[1,73],"is":[2],"an":[3],"approach":[4,124,186,199],"to":[5,47,104,183,216],"the":[6,27,33,36,40,72,85,95,99,116,119,140,142,157,170,175,195,206],"inference":[7],"problem":[8,28,65],"for":[9,88,204],"state-space":[10],"models":[11],"(SSM),":[12],"arising":[13],"in":[14,39,54,92,164,169],"many":[15,93],"disciplines":[16],"of":[17,32,35,71,84,114,118,139,148,197],"science":[18],"and":[19,211],"engineering.":[20],"The":[21,58,75],"well":[22,128],"known":[23,129],"Gaussian":[24,30,42,59,68,121,130,189],"filters":[25,43],"tackle":[26],"using":[29,67,200],"approximations":[31,70],"distributions":[34,50],"hidden":[37],"state":[38],"model.":[41],"however":[44,77],"are":[45,154],"unable":[46],"track":[48],"multimodal":[49],"that":[51],"commonly":[52],"arise":[53],"complex":[55],"dynamical":[56],"systems.":[57],"sum":[60,190],"filter":[61,191,209],"(GSF)":[62],"addresses":[63],"this":[64,108],"by":[66,98,159],"mixture":[69,177],"distribution.":[74],"GSF":[76,100,212],"has":[78],"important":[79],"limitations,":[80],"requiring":[81],"small":[82],"width":[83],"component":[86,138],"covariances":[87,117],"good":[89],"performance.":[90],"Moreover,":[91],"SSMs":[94],"estimates":[96],"provided":[97],"blow":[101],"up,":[102],"due":[103,215],"covariance":[105,217],"inflation.":[106,218],"In":[107],"paper,":[109],"we":[110,178],"propose":[111],"a":[112,127,165,201],"way":[113],"controlling":[115],"underlying":[120],"mixture.":[122],"Our":[123],"relies":[125],"on":[126],"identity,":[131],"which":[132,162,205],"helps":[133],"us":[134],"break":[135],"down":[136],"each":[137],"GSF,":[141],"parent,":[143],"into":[144],"several":[145],"children":[146],"components":[147,153],"smaller":[149,152,166],"width.":[150],"These":[151],"propagated":[155],"through":[156],"nonlinearities":[158],"local":[160],"linearization,":[161],"results":[163],"error":[167],"than":[168],"standard":[171],"GSF.":[172],"To":[173],"reduce":[174],"resulting":[176],"use":[179],"resampling.":[180],"We":[181,193],"refer":[182],"our":[184,198],"novel":[185],"as":[187],"augmented":[188],"(AGSF).":[192],"demonstrate":[194],"advantages":[196],"toy":[202],"example,":[203],"extended":[207],"Kalman":[208],"(EKF)":[210],"perform":[213],"poorly":[214]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}