{"id":"https://openalex.org/W4414229314","doi":"https://doi.org/10.1109/taes.2025.3607680","title":"Distributed Robust PHD Filter for Multitarget Tracking Under Uncertainties","display_name":"Distributed Robust PHD Filter for Multitarget Tracking Under Uncertainties","publication_year":2025,"publication_date":"2025-09-16","ids":{"openalex":"https://openalex.org/W4414229314","doi":"https://doi.org/10.1109/taes.2025.3607680"},"language":"en","primary_location":{"id":"doi:10.1109/taes.2025.3607680","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taes.2025.3607680","pdf_url":null,"source":{"id":"https://openalex.org/S193624734","display_name":"IEEE Transactions on Aerospace and Electronic Systems","issn_l":"0018-9251","issn":["0018-9251","1557-9603","2371-9877"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Aerospace and Electronic Systems","raw_type":"journal-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/A5031895350","display_name":"Lihong Shi","orcid":"https://orcid.org/0000-0001-5215-2549"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lihong Shi","raw_affiliation_strings":["Department of Automation, Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0001-5215-2549","affiliations":[{"raw_affiliation_string":"Department of Automation, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062038689","display_name":"Feng Yang","orcid":"https://orcid.org/0000-0001-6991-1628"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Yang","raw_affiliation_strings":["School of Automation, Northwestern Polytechnical University, Xi’an, China","School of Automation, Northwestern Polytechnical University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0001-6991-1628","affiliations":[{"raw_affiliation_string":"School of Automation, Northwestern Polytechnical University, Xi’an, China","institution_ids":["https://openalex.org/I17145004"]},{"raw_affiliation_string":"School of Automation, Northwestern Polytechnical University, Xi'an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088425269","display_name":"Giorgio Battistelli","orcid":"https://orcid.org/0000-0002-0124-4715"},"institutions":[{"id":"https://openalex.org/I45084792","display_name":"University of Florence","ror":"https://ror.org/04jr1s763","country_code":"IT","type":"education","lineage":["https://openalex.org/I45084792"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Giorgio Battistelli","raw_affiliation_strings":["Dipartimento di Ingegneria dell’Informazione, Universita degli Studi di Firenze, Florence, Italy","Dipartimento di Ingegneria dell'Informazione, Universita degli Studi di Firenze, Florence, Italy"],"raw_orcid":"https://orcid.org/0000-0002-0124-4715","affiliations":[{"raw_affiliation_string":"Dipartimento di Ingegneria dell’Informazione, Universita degli Studi di Firenze, Florence, Italy","institution_ids":["https://openalex.org/I45084792"]},{"raw_affiliation_string":"Dipartimento di Ingegneria dell'Informazione, Universita degli Studi di Firenze, Florence, Italy","institution_ids":["https://openalex.org/I45084792"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Luigi Chisci","orcid":"https://orcid.org/0000-0003-1050-300X"},"institutions":[{"id":"https://openalex.org/I45084792","display_name":"University of Florence","ror":"https://ror.org/04jr1s763","country_code":"IT","type":"education","lineage":["https://openalex.org/I45084792"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Luigi Chisci","raw_affiliation_strings":["Dipartimento di Ingegneria dell’Informazione, Universita degli Studi di Firenze, Florence, Italy","Dipartimento di Ingegneria dell'Informazione, Universita degli Studi di Firenze, Florence, Italy"],"raw_orcid":"https://orcid.org/0000-0003-1050-300X","affiliations":[{"raw_affiliation_string":"Dipartimento di Ingegneria dell’Informazione, Universita degli Studi di Firenze, Florence, Italy","institution_ids":["https://openalex.org/I45084792"]},{"raw_affiliation_string":"Dipartimento di Ingegneria dell'Informazione, Universita degli Studi di Firenze, Florence, Italy","institution_ids":["https://openalex.org/I45084792"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Litao Zheng","orcid":"https://orcid.org/0000-0001-5049-3577"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Litao Zheng","raw_affiliation_strings":["Department of Automation, Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0001-5049-3577","affiliations":[{"raw_affiliation_string":"Department of Automation, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101967607","display_name":"Jianxun Li","orcid":"https://orcid.org/0000-0003-4205-8561"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianxun Li","raw_affiliation_strings":["Department of Automation, Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0003-4205-8561","affiliations":[{"raw_affiliation_string":"Department of Automation, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.7588,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.88547534,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":"61","issue":"6","first_page":"17740","last_page":"17757"},"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":0.9843999743461609,"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":0.9843999743461609,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9758999943733215,"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"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9272000193595886,"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/clutter","display_name":"Clutter","score":0.8253999948501587},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6238999962806702},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.5820000171661377},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.4706999957561493},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4571000039577484},{"id":"https://openalex.org/keywords/inverse-wishart-distribution","display_name":"Inverse-Wishart distribution","score":0.4390000104904175},{"id":"https://openalex.org/keywords/posterior-probability","display_name":"Posterior probability","score":0.42399999499320984},{"id":"https://openalex.org/keywords/fisher-information","display_name":"Fisher information","score":0.41179999709129333},{"id":"https://openalex.org/keywords/radar-tracker","display_name":"Radar tracker","score":0.4090999960899353},{"id":"https://openalex.org/keywords/noise-measurement","display_name":"Noise measurement","score":0.3873000144958496}],"concepts":[{"id":"https://openalex.org/C132094186","wikidata":"https://www.wikidata.org/wiki/Q641585","display_name":"Clutter","level":3,"score":0.8253999948501587},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6238999962806702},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.5820000171661377},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5659000277519226},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5303000211715698},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.4706999957561493},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4571000039577484},{"id":"https://openalex.org/C40851411","wikidata":"https://www.wikidata.org/wiki/Q3258368","display_name":"Inverse-Wishart distribution","level":4,"score":0.4390000104904175},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.42399999499320984},{"id":"https://openalex.org/C29406490","wikidata":"https://www.wikidata.org/wiki/Q1420659","display_name":"Fisher information","level":2,"score":0.41179999709129333},{"id":"https://openalex.org/C32283439","wikidata":"https://www.wikidata.org/wiki/Q1407014","display_name":"Radar tracker","level":3,"score":0.4090999960899353},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.3873000144958496},{"id":"https://openalex.org/C173028925","wikidata":"https://www.wikidata.org/wiki/Q7831461","display_name":"Track-before-detect","level":4,"score":0.37880000472068787},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3725000023841858},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.35760000348091125},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.33230000734329224},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3192000091075897},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.3059999942779541},{"id":"https://openalex.org/C207467116","wikidata":"https://www.wikidata.org/wiki/Q4385666","display_name":"Inverse","level":2,"score":0.3018999993801117},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2937000095844269},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.29100000858306885},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.28110000491142273},{"id":"https://openalex.org/C87117476","wikidata":"https://www.wikidata.org/wiki/Q362383","display_name":"Cardinality (data modeling)","level":2,"score":0.2782999873161316},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2709999978542328},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.2703000009059906},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26980000734329224},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.26010000705718994},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.2599000036716461},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.25929999351501465},{"id":"https://openalex.org/C86369673","wikidata":"https://www.wikidata.org/wiki/Q1203659","display_name":"Simultaneous localization and mapping","level":4,"score":0.25850000977516174},{"id":"https://openalex.org/C47446073","wikidata":"https://www.wikidata.org/wiki/Q5165890","display_name":"Control theory (sociology)","level":3,"score":0.2583000063896179},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.25679999589920044},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.25360000133514404}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/taes.2025.3607680","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taes.2025.3607680","pdf_url":null,"source":{"id":"https://openalex.org/S193624734","display_name":"IEEE Transactions on Aerospace and Electronic Systems","issn_l":"0018-9251","issn":["0018-9251","1557-9603","2371-9877"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Aerospace and Electronic Systems","raw_type":"journal-article"},{"id":"pmh:oai:flore.unifi.it:2158/1442496","is_oa":false,"landing_page_url":"https://hdl.handle.net/2158/1442496","pdf_url":null,"source":{"id":"https://openalex.org/S4306402033","display_name":"Florence Research (University of Florence)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I45084792","host_organization_name":"University of Florence","host_organization_lineage":["https://openalex.org/I45084792"],"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":"info:eu-repo/semantics/article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2365896611","display_name":"\u9762\u5411OTHR\u76ee\u6807\u8ddf\u8e2a\u7684\u591a\u8def\u5f84PHD\u6ee4\u6ce2\u7b97\u6cd5\u7814\u7a76","funder_award_id":"61374159","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4287518590","display_name":null,"funder_award_id":"PE00000001","funder_id":"https://openalex.org/F7311471023","funder_display_name":"NextGenerationEU"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F7311471023","display_name":"NextGenerationEU","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0],"paper":[1],"addresses":[2],"the":[3,22,59,93,104,108,136,163,174,180,194,201],"issue":[4],"of":[5,18,95,106,115,167,183,200],"multi-target":[6],"tracking":[7],"in":[8,185],"a":[9,40,47,52,124,130],"distributed":[10,125,131,205],"sensor":[11,126,169],"network,":[12,127],"where":[13],"sensors":[14],"have":[15],"different":[16],"fields":[17],"view":[19],"(FoVs)":[20],"and":[21,34,51,62,75,85,99,103,147,150,159,197,204],"scenario":[23],"is":[24,111,171],"characterized":[25],"by":[26,113],"multiple":[27,68],"uncertainties":[28],"on":[29,46],"detection":[30,73],"probability,":[31],"clutter":[32,53,76,100],"rate":[33],"noise":[35],"covariances.":[36],"First,":[37],"we":[38,128],"design":[39,177],"robust":[41],"PHD":[42],"(RPHD)":[43],"filter,":[44],"based":[45],"target":[48,60,96],"augmented":[49,97],"state":[50,98],"feature":[54],"state,":[55,101],"to":[56,142,151,178,192],"jointly":[57],"estimate":[58],"cardinality":[61],"kinematic":[63],"states":[64],"as":[65,67],"well":[66],"unknown":[69,109],"parameters":[70],"(noise":[71],"covariances,":[72],"probability":[74],"rate).":[77],"Gaussian":[78],"Inverse":[79,81],"Gamma":[80,87],"Wishart":[82],"Mixture":[83],"(GIGIWM)":[84],"inverse":[86],"mixture":[88],"representations":[89],"are":[90,190],"used":[91],"for":[92,123],"intensities":[94],"respectively,":[102],"estimation":[105],"all":[107],"variables":[110],"achieved":[112],"means":[114],"variational":[116],"Bayesian":[117],"inference":[118],"incorporating":[119],"constrained":[120],"distributions.":[121],"Secondly,":[122],"propose":[129],"fusion":[132,175,206],"algorithm":[133],"that":[134],"extends":[135],"weighted":[137],"arithmetic":[138],"average":[139],"(WAA)":[140],"strategy":[141],"fuse":[143],"information":[144,165,184],"within":[145],"overlapping":[146],"non-overlapping":[148],"regions":[149],"deal":[152],"with":[153],"local":[154,160,164],"posteriors":[155],"containing":[156],"both":[157],"global":[158],"variables.":[161],"Lastly,":[162],"confidence":[166],"each":[168],"posterior":[170],"incorporated":[172],"into":[173],"weight":[176],"enhance":[179],"effective":[181],"utilization":[182],"fusion.":[186],"Numerical":[187],"simulation":[188],"experiments":[189],"performed":[191],"demonstrate":[193],"superior":[195],"performance":[196],"strong":[198],"robustness":[199],"presented":[202],"filter":[203],"algorithm.":[207]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}