{"id":"https://openalex.org/W4391361982","doi":"https://doi.org/10.3390/rs16030506","title":"A Robust TCPHD Filter for Multi-Sensor Multitarget Tracking Based on a Gaussian\u2013Student\u2019s t-Mixture Model","display_name":"A Robust TCPHD Filter for Multi-Sensor Multitarget Tracking Based on a Gaussian\u2013Student\u2019s t-Mixture Model","publication_year":2024,"publication_date":"2024-01-28","ids":{"openalex":"https://openalex.org/W4391361982","doi":"https://doi.org/10.3390/rs16030506"},"language":"en","primary_location":{"id":"doi:10.3390/rs16030506","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs16030506","pdf_url":"https://www.mdpi.com/2072-4292/16/3/506/pdf?version=1706613242","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2072-4292/16/3/506/pdf?version=1706613242","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5020555064","display_name":"Shaoming Wei","orcid":"https://orcid.org/0000-0002-2729-1106"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shaoming Wei","raw_affiliation_strings":["School of Electronics and Information Engineering, Beihang University, Beijing 100191, China"],"raw_orcid":"https://orcid.org/0000-0002-2729-1106","affiliations":[{"raw_affiliation_string":"School of Electronics and Information Engineering, Beihang University, Beijing 100191, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082997357","display_name":"Yingbin Lin","orcid":"https://orcid.org/0000-0003-1793-5649"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yingbin Lin","raw_affiliation_strings":["School of Electronics and Information Engineering, Beihang University, Beijing 100191, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronics and Information Engineering, Beihang University, Beijing 100191, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114191514","display_name":"Jun Wang","orcid":"https://orcid.org/0000-0002-0733-4359"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jun Wang","raw_affiliation_strings":["Hangzhou Innovation Institute, Beihang University, Hangzhou 310000, China","School of Electronics and Information Engineering, Beihang University, Beijing 100191, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hangzhou Innovation Institute, Beihang University, Hangzhou 310000, China","institution_ids":["https://openalex.org/I82880672"]},{"raw_affiliation_string":"School of Electronics and Information Engineering, Beihang University, Beijing 100191, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049107629","display_name":"Yajun Zeng","orcid":"https://orcid.org/0000-0002-3928-8270"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yajun Zeng","raw_affiliation_strings":["School of Electronics and Information Engineering, Beihang University, Beijing 100191, China"],"raw_orcid":"https://orcid.org/0000-0002-3928-8270","affiliations":[{"raw_affiliation_string":"School of Electronics and Information Engineering, Beihang University, Beijing 100191, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077339617","display_name":"Fangrui Qu","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fangrui Qu","raw_affiliation_strings":["School of Electronics and Information Engineering, Beihang University, Beijing 100191, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronics and Information Engineering, Beihang University, Beijing 100191, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048225004","display_name":"Xuan Zhou","orcid":"https://orcid.org/0000-0002-2806-9654"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuan Zhou","raw_affiliation_strings":["School of Electronics and Information Engineering, Beihang University, Beijing 100191, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronics and Information Engineering, Beihang University, Beijing 100191, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113108223","display_name":"Zhuotong Lu","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhuotong Lu","raw_affiliation_strings":["School of Electronics and Information Engineering, Beihang University, Beijing 100191, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronics and Information Engineering, Beihang University, Beijing 100191, China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5114191514"],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2784},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2784},"fwci":0.2434,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.59557889,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"16","issue":"3","first_page":"506","last_page":"506"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9883999824523926,"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"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9769999980926514,"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/tracking","display_name":"Tracking (education)","score":0.5788002014160156},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5218122005462646},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5028509497642517},{"id":"https://openalex.org/keywords/gaussian-filter","display_name":"Gaussian filter","score":0.41714999079704285},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.32483750581741333},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.15677466988563538},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.052260756492614746}],"concepts":[{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.5788002014160156},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5218122005462646},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5028509497642517},{"id":"https://openalex.org/C65892221","wikidata":"https://www.wikidata.org/wiki/Q1113935","display_name":"Gaussian filter","level":3,"score":0.41714999079704285},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32483750581741333},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.15677466988563538},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.052260756492614746},{"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},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/rs16030506","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs16030506","pdf_url":"https://www.mdpi.com/2072-4292/16/3/506/pdf?version=1706613242","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:413608f5cec140c68ecaeee520f4ce86","is_oa":true,"landing_page_url":"https://doaj.org/article/413608f5cec140c68ecaeee520f4ce86","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 16, Iss 3, p 506 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/rs16030506","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs16030506","pdf_url":"https://www.mdpi.com/2072-4292/16/3/506/pdf?version=1706613242","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2297807074","display_name":"\u5206\u5e03\u5f0f\u96f7\u8fbe\u7a7a\u95f4\u76ee\u6807\u4e09\u7ef4\u6210\u50cf\u65b9\u6cd5\u7814\u7a76","funder_award_id":"61671035","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7754491960","display_name":null,"funder_award_id":"62171029","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4391361982.pdf"},"referenced_works_count":43,"referenced_works":["https://openalex.org/W1567156622","https://openalex.org/W1579925870","https://openalex.org/W1881770166","https://openalex.org/W1978705731","https://openalex.org/W2014787937","https://openalex.org/W2015563078","https://openalex.org/W2019034316","https://openalex.org/W2023428235","https://openalex.org/W2053286025","https://openalex.org/W2062100941","https://openalex.org/W2077611006","https://openalex.org/W2080777453","https://openalex.org/W2088735810","https://openalex.org/W2110354007","https://openalex.org/W2120378065","https://openalex.org/W2137585588","https://openalex.org/W2354440614","https://openalex.org/W2395026957","https://openalex.org/W2398554122","https://openalex.org/W2737188128","https://openalex.org/W2795713352","https://openalex.org/W2883067374","https://openalex.org/W2889452703","https://openalex.org/W2901974889","https://openalex.org/W2944846513","https://openalex.org/W2964293148","https://openalex.org/W2993465864","https://openalex.org/W3007456408","https://openalex.org/W3011085525","https://openalex.org/W3046336220","https://openalex.org/W3084747182","https://openalex.org/W3157789478","https://openalex.org/W3173144569","https://openalex.org/W3175101966","https://openalex.org/W3180125350","https://openalex.org/W4285166489","https://openalex.org/W4285308698","https://openalex.org/W4313854586","https://openalex.org/W4321194442","https://openalex.org/W4380450972","https://openalex.org/W6676462664","https://openalex.org/W6774644827","https://openalex.org/W6801395996"],"related_works":["https://openalex.org/W2609066529","https://openalex.org/W1970319972","https://openalex.org/W2082319654","https://openalex.org/W2150541705","https://openalex.org/W2389710210","https://openalex.org/W4386840168","https://openalex.org/W2921413925","https://openalex.org/W2566989475","https://openalex.org/W2379831516","https://openalex.org/W1998409879"],"abstract_inverted_index":{"To":[0,124],"realize":[1],"multitarget":[2],"trajectory":[3,15,70],"tracking":[4,31,77,176],"under":[5],"non-Gaussian":[6,55],"heavy-tailed":[7,54,90],"noise,":[8,91],"we":[9,116,134],"propose":[10],"a":[11,97,107,121,136],"Gaussian\u2013Student":[12],"t-mixture":[13],"distribution-based":[14],"cardinality":[16,34],"probability":[17,35],"hypothesis":[18,36],"density":[19,37],"filter":[20,28,65,83,119,155,159,164],"(GSTM-TCPHD).":[21],"We":[22],"introduce":[23],"the":[24,68,72,85,118,126,145,157,170],"multi-sensor":[25,122],"GSTM-TCPHD":[26,82],"(MS-GSTM-TCPHD)":[27],"to":[29,46,60,88,120,140],"enhance":[30],"performance.":[32],"Conventional":[33],"(CPHD)":[38],"filters":[39],"typically":[40],"assume":[41],"Gaussian":[42],"noise":[43,58],"and":[44,64,78,95,112,143,160,182],"struggle":[45],"accurately":[47,168],"establish":[48],"target":[49,73,101],"trajectories":[50,171],"when":[51],"faced":[52],"with":[53],"distributions.":[56],"Heavy-tailed":[57],"leads":[59],"significant":[61],"estimation":[62],"errors":[63,94],"dispersion.":[66],"Moreover,":[67],"exact":[69],"of":[71,99,110,128,167,172],"is":[74],"crucial":[75],"for":[76],"prediction.":[79],"Our":[80],"proposed":[81,154],"utilizes":[84],"GSTM":[86],"distribution":[87],"model":[89],"reducing":[92],"modeling":[93],"generating":[96],"set":[98],"potential":[100],"trajectories.":[102],"Since":[103],"single":[104],"sensors":[105],"have":[106],"limited":[108,113],"field":[109],"view":[111],"measurement":[114],"information,":[115],"extend":[117],"scenario.":[123],"tackle":[125],"issue":[127],"data":[129],"explosion":[130],"from":[131],"multiple":[132,173],"sensors,":[133],"employed":[135],"greedy":[137],"approximation":[138],"method":[139],"assess":[141],"measurements":[142],"introduced":[144],"MS-GSTM-TCPHD":[146],"filter.":[147],"The":[148],"simulation":[149],"results":[150],"demonstrate":[151],"that":[152],"our":[153],"outperforms":[156],"CPHD/TCPHD":[158],"Student\u2019s":[161],"t-based":[162],"TCPHD":[163],"in":[165],"terms":[166],"estimating":[169],"targets":[174],"during":[175],"while":[177],"also":[178],"achieving":[179],"improved":[180],"accuracy":[181],"shorter":[183],"processing":[184],"time.":[185]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2024-01-31T00:00:00"}