{"id":"https://openalex.org/W2889335330","doi":"https://doi.org/10.1109/sam.2018.8448400","title":"Hybrid Particle Filtering Based on an Elitist Resampling Scheme","display_name":"Hybrid Particle Filtering Based on an Elitist Resampling Scheme","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2889335330","doi":"https://doi.org/10.1109/sam.2018.8448400","mag":"2889335330"},"language":"en","primary_location":{"id":"doi:10.1109/sam.2018.8448400","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sam.2018.8448400","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop (SAM)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5008815790","display_name":"Mhd Modar Halimeh","orcid":"https://orcid.org/0000-0001-6113-3067"},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Mhd Modar Halimeh","raw_affiliation_strings":["Multimedia Communications and Signal Processing, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, Erlangen, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Multimedia Communications and Signal Processing, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, Erlangen, Germany","institution_ids":["https://openalex.org/I181369854"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039041764","display_name":"Christian Huemmer","orcid":"https://orcid.org/0000-0002-4341-4217"},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Christian Huemmer","raw_affiliation_strings":["Multimedia Communications and Signal Processing, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, Erlangen, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Multimedia Communications and Signal Processing, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, Erlangen, Germany","institution_ids":["https://openalex.org/I181369854"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078105594","display_name":"Andreas Brendel","orcid":"https://orcid.org/0000-0002-6051-6346"},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Andreas Brendel","raw_affiliation_strings":["Multimedia Communications and Signal Processing, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, Erlangen, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Multimedia Communications and Signal Processing, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, Erlangen, Germany","institution_ids":["https://openalex.org/I181369854"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068787682","display_name":"Walter Kellermann","orcid":"https://orcid.org/0000-0002-6501-3174"},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Walter Kellermann","raw_affiliation_strings":["Multimedia Communications and Signal Processing, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, Erlangen, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Multimedia Communications and Signal Processing, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, Erlangen, Germany","institution_ids":["https://openalex.org/I181369854"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I181369854"],"apc_list":null,"apc_paid":null,"fwci":0.3395,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.60657301,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"19","issue":null,"first_page":"257","last_page":"261"},"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":0.9995999932289124,"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":0.9995999932289124,"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/T11220","display_name":"Water Systems and Optimization","score":0.9968000054359436,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/T10876","display_name":"Fault Detection and Control Systems","score":0.9839000105857849,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/resampling","display_name":"Resampling","score":0.7089618444442749},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6649560332298279},{"id":"https://openalex.org/keywords/particle-filter","display_name":"Particle filter","score":0.5026450157165527},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4958430230617523},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.49232709407806396},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4750095307826996},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4266383647918701},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.42284858226776123},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4052138030529022},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2250097393989563},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.08889308571815491},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.07457566261291504}],"concepts":[{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.7089618444442749},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6649560332298279},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.5026450157165527},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4958430230617523},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.49232709407806396},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4750095307826996},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4266383647918701},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.42284858226776123},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4052138030529022},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2250097393989563},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.08889308571815491},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.07457566261291504},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/sam.2018.8448400","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sam.2018.8448400","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop (SAM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W62719046","https://openalex.org/W120758471","https://openalex.org/W1568122762","https://openalex.org/W2016120704","https://openalex.org/W2028341025","https://openalex.org/W2040196349","https://openalex.org/W2048796971","https://openalex.org/W2055936398","https://openalex.org/W2058756796","https://openalex.org/W2073117938","https://openalex.org/W2098613108","https://openalex.org/W2109375023","https://openalex.org/W2111787305","https://openalex.org/W2119539043","https://openalex.org/W2124156864","https://openalex.org/W2131598171","https://openalex.org/W2137043637","https://openalex.org/W2140745919","https://openalex.org/W2160337655","https://openalex.org/W2170780515","https://openalex.org/W2178829616","https://openalex.org/W2179435707","https://openalex.org/W2254448426","https://openalex.org/W2735102987","https://openalex.org/W2963349082","https://openalex.org/W4240108704","https://openalex.org/W4244486013","https://openalex.org/W4285719527","https://openalex.org/W6604897594","https://openalex.org/W6676780147","https://openalex.org/W6677777831"],"related_works":["https://openalex.org/W2927378857","https://openalex.org/W2801696468","https://openalex.org/W2406829934","https://openalex.org/W1824810860","https://openalex.org/W2185006999","https://openalex.org/W2162253570","https://openalex.org/W1583020711","https://openalex.org/W2138381686","https://openalex.org/W1486580556","https://openalex.org/W2010934810"],"abstract_inverted_index":{"In":[0],"this":[1,80],"paper,":[2],"the":[3,22,41,45,48,53,62,75,82,87,93,109,121],"elitist":[4],"resampling":[5],"particle":[6,19,26,100],"filter":[7],"(ERPF)":[8],"is":[9,13],"introduced.":[10],"The":[11,102],"ERPF":[12,49,76,84,89],"an":[14,30,131],"approach":[15],"to":[16,40,92,108,118],"combine":[17],"generic":[18],"filters,":[20],"i.e.,":[21],"SIS":[23],"and":[24,57,60,69,86,129],"SIR":[25],"filter,":[27,66],"based":[28],"on":[29],"evolutionary":[31,122],"selection":[32],"of":[33,44,64,74,99],"particles,":[34],"which":[35,91],"introduces":[36],"a":[37,125],"longterm":[38],"memory":[39],"selected":[42],"group":[43],"particles.":[46],"Thereby,":[47],"aims":[50],"at":[51],"fusing":[52],"advantages,":[54],"e.g.,":[55,67],"robustness":[56,133],"computational":[58],"efficiency,":[59],"mitigating":[61],"drawbacks":[63],"each":[65],"degeneracy":[68],"sample":[70],"impoverishment.":[71],"Two":[72],"variants":[73],"are":[77,106],"presented":[78],"in":[79],"paper:":[81],"discrete":[83],"(DERPF)":[85],"continuous":[88],"(CERPF),":[90],"authors'":[94],"knowledge":[95],"represent":[96],"new":[97],"forms":[98],"filters.":[101],"two":[103,114],"proposed":[104],"hybrids":[105],"compared":[107],"corresponding":[110],"original":[111],"filters":[112],"using":[113],"well-established":[115],"benchmark":[116],"models":[117],"illustrate":[119],"that":[120],"combinations":[123],"provide":[124],"better":[126],"estimation":[127],"accuracy":[128],"exhibit":[130],"increased":[132],"against":[134],"outliers.":[135]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}