{"id":"https://openalex.org/W2786393837","doi":"https://doi.org/10.1109/iccis.2017.8274885","title":"A multi-feature fusion moving target recognition method based on believability regression reasoning","display_name":"A multi-feature fusion moving target recognition method based on believability regression reasoning","publication_year":2017,"publication_date":"2017-11-01","ids":{"openalex":"https://openalex.org/W2786393837","doi":"https://doi.org/10.1109/iccis.2017.8274885","mag":"2786393837"},"language":"en","primary_location":{"id":"doi:10.1109/iccis.2017.8274885","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccis.2017.8274885","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM)","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":null,"display_name":"Tang Xiaogang","orcid":null},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Tang Xiaogang","raw_affiliation_strings":["Department of Information Equipment, Academy of Equipment, Beijing, P.R. China","School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, P.R. China"],"affiliations":[{"raw_affiliation_string":"Department of Information Equipment, Academy of Equipment, Beijing, P.R. China","institution_ids":[]},{"raw_affiliation_string":"School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, P.R. China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Wang Sun'an","orcid":null},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wang Sun'an","raw_affiliation_strings":["School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, P.R. China"],"affiliations":[{"raw_affiliation_string":"School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, P.R. China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042439984","display_name":"Hongyu Di","orcid":null},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Di Hongyu","raw_affiliation_strings":["School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, P.R. China"],"affiliations":[{"raw_affiliation_string":"School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, P.R. China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"last","author":{"id":null,"display_name":"Liu Litian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu Litian","raw_affiliation_strings":["Department of Information Equipment, Academy of Equipment, Beijing, P.R. China"],"affiliations":[{"raw_affiliation_string":"Department of Information Equipment, Academy of Equipment, Beijing, P.R. China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I87445476"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.21080517,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"28","issue":null,"first_page":"820","last_page":"825"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T14257","display_name":"Advanced Measurement and Detection Methods","score":0.9958000183105469,"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/T12389","display_name":"Infrared Target Detection Methodologies","score":0.9898999929428101,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7526577711105347},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6661645174026489},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6516913175582886},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5650390386581421},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5506982207298279},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.49434423446655273},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.4169892966747284},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.4162774682044983},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37625062465667725}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7526577711105347},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6661645174026489},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6516913175582886},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5650390386581421},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5506982207298279},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.49434423446655273},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.4169892966747284},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.4162774682044983},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37625062465667725},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccis.2017.8274885","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccis.2017.8274885","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM)","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":7,"referenced_works":["https://openalex.org/W1965733394","https://openalex.org/W1995903777","https://openalex.org/W2043129204","https://openalex.org/W2051186205","https://openalex.org/W2151199326","https://openalex.org/W2343521073","https://openalex.org/W2556644206"],"related_works":["https://openalex.org/W2090763504","https://openalex.org/W148178222","https://openalex.org/W2104657898","https://openalex.org/W1948992892","https://openalex.org/W1886884218","https://openalex.org/W1910826599","https://openalex.org/W2012353789","https://openalex.org/W2530420969","https://openalex.org/W2051187167","https://openalex.org/W1980100242"],"abstract_inverted_index":{"Features":[0],"of":[1,35,53,60,100,120,144,165,191,197],"dynamic":[2,54,78,198],"target":[3,42,55,79,88,133,159,166,192,199],"under":[4,162],"the":[5,22,26,36,41,51,57,96,111,125,128,150,163,177,189,195],"complex":[6,171],"natural":[7],"background":[8,46],"change":[9],"drastically,":[10],"and":[11,45,116,132,140,188],"methods":[12],"based":[13,28,63,80,103,152],"on":[14,29,64,81,104,153],"single":[15],"feature":[16,122],"recognition":[17,32,75,118,156,193],"could":[18],"not":[19],"adapt":[20],"to":[21,110,203],"drastic":[23],"changes,":[24],"while":[25],"method":[27,151],"multi-feature":[30,73],"fusion":[31,65,74,155],"is":[33,67],"one":[34],"important":[37],"research":[38],"directions.":[39],"However,":[40],"distance,":[43,134],"scale":[44],"environment":[47],"vary":[48],"widely":[49],"in":[50,194],"process":[52,196],"tracking;":[56],"basic":[58,97,129],"reliability":[59,82,141],"multifeature":[61,154],"classifiers":[62,106],"reasoning":[66,102],"unpredictable.":[68],"This":[69],"paper":[70],"proposes":[71],"a":[72,204],"algorithm":[76,178],"for":[77,157],"regression":[83,142],"reasoning.":[84],"To":[85],"begin":[86],"with,":[87],"multi-dimensional":[89],"independent":[90],"features":[91],"were":[92],"extracted;":[93],"what's":[94],"more,":[95],"probability":[98,130],"distribution":[99,131],"D-S":[101,145],"SVM":[105,117],"was":[107,147,160,201],"designed":[108],"according":[109],"mixed":[112],"matrix":[113],"distance":[114,167],"measure":[115],"rate":[119],"each":[121],"classifier;":[123],"furthermore,":[124],"relationship":[126],"between":[127],"founded":[135],"by":[136],"least":[137],"square":[138],"fitting":[139],"model":[143],"reasoning,":[146],"acquired.":[148],"Finally,":[149],"moving":[158],"fulfilled":[161],"condition":[164],"continuous":[168],"variation":[169],"at":[170],"environment.":[172],"Comparative":[173],"experiments":[174],"showed":[175],"that":[176],"has":[179],"good":[180],"generalization":[181],"ability":[182],"as":[183,185],"well":[184],"higher":[186],"efficiency,":[187],"uncertainty":[190],"tracking":[200],"reduced":[202],"large":[205],"extent.":[206]},"counts_by_year":[],"updated_date":"2026-04-16T08:26:57.006410","created_date":"2025-10-10T00:00:00"}
