{"id":"https://openalex.org/W2070824229","doi":"https://doi.org/10.1109/glocom.2014.7037301","title":"Interacting multiple model particle filtering using new particle resampling algorithm","display_name":"Interacting multiple model particle filtering using new particle resampling algorithm","publication_year":2014,"publication_date":"2014-12-01","ids":{"openalex":"https://openalex.org/W2070824229","doi":"https://doi.org/10.1109/glocom.2014.7037301","mag":"2070824229"},"language":"en","primary_location":{"id":"doi:10.1109/glocom.2014.7037301","is_oa":false,"landing_page_url":"https://doi.org/10.1109/glocom.2014.7037301","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE Global Communications Conference","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/A5103196988","display_name":"Dah\u2010Chung Chang","orcid":"https://orcid.org/0000-0003-1583-7519"},"institutions":[{"id":"https://openalex.org/I22265921","display_name":"National Central University","ror":"https://ror.org/00944ve71","country_code":"TW","type":"education","lineage":["https://openalex.org/I22265921"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Dah-Chung Chang","raw_affiliation_strings":["Department of Communication Engineering, National Central University, Taoyuang, Taiwan","[Department of Communication Engineering, National Central University, Taoyuang 32001, Taiwan]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Communication Engineering, National Central University, Taoyuang, Taiwan","institution_ids":["https://openalex.org/I22265921"]},{"raw_affiliation_string":"[Department of Communication Engineering, National Central University, Taoyuang 32001, Taiwan]","institution_ids":["https://openalex.org/I22265921"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031149999","display_name":"Meng-Wei Fan","orcid":null},"institutions":[{"id":"https://openalex.org/I22265921","display_name":"National Central University","ror":"https://ror.org/00944ve71","country_code":"TW","type":"education","lineage":["https://openalex.org/I22265921"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Meng-Wei Fan","raw_affiliation_strings":["Department of Communication Engineering, National Central University, Taoyuang, Taiwan","[Department of Communication Engineering, National Central University, Taoyuang 32001, Taiwan]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Communication Engineering, National Central University, Taoyuang, Taiwan","institution_ids":["https://openalex.org/I22265921"]},{"raw_affiliation_string":"[Department of Communication Engineering, National Central University, Taoyuang 32001, Taiwan]","institution_ids":["https://openalex.org/I22265921"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I22265921"],"apc_list":null,"apc_paid":null,"fwci":0.4135,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.59545077,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"3215","last_page":"3219"},"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":0.9998999834060669,"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":0.9998999834060669,"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/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T11192","display_name":"Underwater Vehicles and Communication Systems","score":0.9912999868392944,"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/particle-filter","display_name":"Particle filter","score":0.8136188983917236},{"id":"https://openalex.org/keywords/resampling","display_name":"Resampling","score":0.7443951368331909},{"id":"https://openalex.org/keywords/cram\u00e9r\u2013rao-bound","display_name":"Cram\u00e9r\u2013Rao bound","score":0.7154457569122314},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.7099907398223877},{"id":"https://openalex.org/keywords/degeneracy","display_name":"Degeneracy (biology)","score":0.5344585180282593},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.5202370882034302},{"id":"https://openalex.org/keywords/independent-and-identically-distributed-random-variables","display_name":"Independent and identically distributed random variables","score":0.5062121748924255},{"id":"https://openalex.org/keywords/auxiliary-particle-filter","display_name":"Auxiliary particle filter","score":0.500391960144043},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.4519035220146179},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.44105011224746704},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.4145335555076599},{"id":"https://openalex.org/keywords/extended-kalman-filter","display_name":"Extended Kalman filter","score":0.40657132863998413},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3982791304588318},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.39040660858154297},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.33331435918807983},{"id":"https://openalex.org/keywords/ensemble-kalman-filter","display_name":"Ensemble Kalman filter","score":0.28113725781440735},{"id":"https://openalex.org/keywords/estimation-theory","display_name":"Estimation theory","score":0.2574227452278137},{"id":"https://openalex.org/keywords/random-variable","display_name":"Random variable","score":0.24460718035697937},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.22082990407943726},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.1358136534690857}],"concepts":[{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.8136188983917236},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.7443951368331909},{"id":"https://openalex.org/C4978587","wikidata":"https://www.wikidata.org/wiki/Q1138810","display_name":"Cram\u00e9r\u2013Rao bound","level":3,"score":0.7154457569122314},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.7099907398223877},{"id":"https://openalex.org/C2777727622","wikidata":"https://www.wikidata.org/wiki/Q5251772","display_name":"Degeneracy (biology)","level":2,"score":0.5344585180282593},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.5202370882034302},{"id":"https://openalex.org/C141513077","wikidata":"https://www.wikidata.org/wiki/Q378542","display_name":"Independent and identically distributed random variables","level":3,"score":0.5062121748924255},{"id":"https://openalex.org/C52483021","wikidata":"https://www.wikidata.org/wiki/Q4827310","display_name":"Auxiliary particle filter","level":5,"score":0.500391960144043},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.4519035220146179},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.44105011224746704},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.4145335555076599},{"id":"https://openalex.org/C206833254","wikidata":"https://www.wikidata.org/wiki/Q5421817","display_name":"Extended Kalman filter","level":3,"score":0.40657132863998413},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3982791304588318},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.39040660858154297},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.33331435918807983},{"id":"https://openalex.org/C79334102","wikidata":"https://www.wikidata.org/wiki/Q3072268","display_name":"Ensemble Kalman filter","level":4,"score":0.28113725781440735},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.2574227452278137},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.24460718035697937},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.22082990407943726},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.1358136534690857},{"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/C60644358","wikidata":"https://www.wikidata.org/wiki/Q128570","display_name":"Bioinformatics","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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/glocom.2014.7037301","is_oa":false,"landing_page_url":"https://doi.org/10.1109/glocom.2014.7037301","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE Global Communications Conference","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1531532259","https://openalex.org/W1542912205","https://openalex.org/W1568122762","https://openalex.org/W2035188723","https://openalex.org/W2104130465","https://openalex.org/W2119539043","https://openalex.org/W2126736494","https://openalex.org/W2171254182","https://openalex.org/W2171618211","https://openalex.org/W2484866470"],"related_works":["https://openalex.org/W2004544955","https://openalex.org/W2743206091","https://openalex.org/W1824810860","https://openalex.org/W2380146358","https://openalex.org/W2370090115","https://openalex.org/W3144709167","https://openalex.org/W1970247010","https://openalex.org/W2758742130","https://openalex.org/W2372707641","https://openalex.org/W2353687059"],"abstract_inverted_index":{"The":[0,17,52,87],"state":[1],"estimation":[2],"technique":[3],"based":[4],"on":[5],"the":[6,40,47,77,81,106,110,128],"Kaiman":[7],"filter":[8,54],"(KF)":[9],"is":[10,19,43,50,74,91,124],"widely":[11],"used":[12],"in":[13],"many":[14],"communication":[15],"applications.":[16],"KF":[18,113],"only":[20],"optimal":[21],"for":[22,76,93],"linear":[23],"modeling":[24],"with":[25,57,99],"independent":[26],"and":[27,33,46,115],"identically":[28],"distributed":[29],"(i.i.d.)":[30],"random":[31],"variables":[32],"Gaussian":[34],"noises.":[35],"In":[36,66],"some":[37],"complicated":[38],"problems,":[39],"system":[41],"model":[42],"not":[44],"unique":[45],"measurement":[48],"equation":[49],"nonlinear.":[51],"particle":[53,71,85],"(PF)":[55],"along":[56],"interacting":[58],"multiple":[59],"models":[60],"(IMM)":[61],"becomes":[62],"an":[63,94],"attractive":[64],"solution.":[65],"this":[67],"paper,":[68],"a":[69,117],"new":[70,88],"resampling":[72],"method":[73],"proposed":[75],"PF":[78],"to":[79,127],"alleviate":[80],"degeneracy":[82],"effect":[83],"of":[84],"propagation.":[86],"IMMPF":[89,107],"algorithm":[90,108,114],"developed":[92],"angle-of-arrival":[95],"(AOA)":[96],"tracking":[97,121],"problem":[98],"bearings-only":[100],"measurements.":[101],"Simulation":[102],"results":[103],"show":[104],"that":[105],"outperforms":[109],"IMM":[111],"extended":[112],"achieves":[116],"root":[118],"mean":[119],"square":[120],"performance":[122],"which":[123],"quite":[125],"close":[126],"posterior":[129],"Cramer-Rao":[130],"lower":[131],"bound":[132],"(CRLB).":[133]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
