{"id":"https://openalex.org/W3033704403","doi":"https://doi.org/10.1145/3386164.3389087","title":"A Multi-Radar Track Fusion Methodology Based on Random Forest Regression","display_name":"A Multi-Radar Track Fusion Methodology Based on Random Forest Regression","publication_year":2019,"publication_date":"2019-09-25","ids":{"openalex":"https://openalex.org/W3033704403","doi":"https://doi.org/10.1145/3386164.3389087","mag":"3033704403"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1145/3386164.3389087","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-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/A5000944309","display_name":"Zhanchun Gao","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"funder","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhanchun Gao","raw_affiliation_strings":["School of Computer Science, Beijing University of Posts and Telecommunications, Beijing China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Beijing University of Posts and Telecommunications, Beijing China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100633382","display_name":"Zhiyang Zhang","orcid":"https://orcid.org/0000-0002-6766-8449"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"funder","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiyang Zhang","raw_affiliation_strings":["School of Computer Science, Beijing University of Posts and Telecommunications, Beijing China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Beijing University of Posts and Telecommunications, Beijing China","institution_ids":["https://openalex.org/I139759216"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":0,"max":61},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T13832","display_name":"Advanced Decision-Making Techniques","score":0.9713,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T13832","display_name":"Advanced Decision-Making Techniques","score":0.9713,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10320","display_name":"Neural Networks and Applications","score":0.9674,"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/T14339","display_name":"Image Processing and 3D Reconstruction","score":0.938,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor Fusion","score":0.5630368},{"id":"https://openalex.org/keywords/position","display_name":"Position (finance)","score":0.44766992}],"concepts":[{"id":"https://openalex.org/C89992363","wikidata":"https://www.wikidata.org/wiki/Q5961558","display_name":"Track (disk drive)","level":2,"score":0.72618294},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7243633},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.671245},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.6066357},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.5630368},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5504261},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.519676},{"id":"https://openalex.org/C32283439","wikidata":"https://www.wikidata.org/wiki/Q1407014","display_name":"Radar tracker","level":3,"score":0.47505662},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.45738122},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.44766992},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.43291286},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37122434},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35451603},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.32023892},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.10244474},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0818778},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1145/3386164.3389087","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/15","display_name":"Life on land","score":0.59}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":12,"referenced_works":["https://openalex.org/W1502698477","https://openalex.org/W1970491504","https://openalex.org/W1988195734","https://openalex.org/W2014337736","https://openalex.org/W2018627383","https://openalex.org/W2063757041","https://openalex.org/W2142629552","https://openalex.org/W2146960482","https://openalex.org/W2149706766","https://openalex.org/W2471955547","https://openalex.org/W273955616","https://openalex.org/W2911964244"],"related_works":["https://openalex.org/W3193043704","https://openalex.org/W2388705266","https://openalex.org/W2358167375","https://openalex.org/W2357354286","https://openalex.org/W2103761320","https://openalex.org/W1983380679","https://openalex.org/W1981649169","https://openalex.org/W1938087941","https://openalex.org/W1582120664","https://openalex.org/W116564642"],"abstract_inverted_index":{"This":[0],"paper":[1,69],"introduces":[2],"a":[3,77,81,107,158],"multi-radar":[4],"track":[5,39,79,101,109],"fusion":[6,19,40,110],"method":[7,41,65,140],"based":[8],"on":[9,95],"random":[10],"forest":[11],"regression":[12],"and":[13,17,88,126],"provides":[14],"an":[15,37],"accurate":[16,120],"stable":[18],"track.":[20],"The":[21,60,137],"increasing":[22],"number":[23],"of":[24,51,57,63,76,80,92,99,118,133,150,157,161],"aircraft":[25,53,94],"will":[26],"lead":[27],"to":[28,33,71,84,124],"congested":[29],"routes,":[30],"further":[31],"leading":[32],"safety":[34,50],"issues.":[35],"Therefore,":[36],"effective":[38],"can":[42],"accurately":[43],"locate":[44],"the":[45,49,52,55,64,73,86,90,93,96,100,103,112,116,131,148,155],"aircraft,":[46],"thereby":[47],"ensuring":[48],"in":[54,67,130,154],"case":[56,132,156],"crowded":[58],"routes.":[59],"basic":[61],"idea":[62],"proposed":[66],"this":[68],"is":[70],"select":[72],"radar":[74],"data":[75,135],"certain":[78,82],"day":[83,98],"train":[85],"model,":[87],"predict":[89],"position":[91],"next":[97],"through":[102],"trained":[104],"model.":[105],"As":[106],"traditional":[108],"algorithm,":[111],"Kalman":[113],"filtering":[114],"has":[115,147],"problem":[117],"requiring":[119],"error":[121],"estimation,":[122],"insensitivity":[123],"noise,":[125],"long":[127],"calculation":[128],"time":[129],"large":[134,159],"volume.":[136],"neural":[138],"network":[139],"that":[141],"compensates":[142],"for":[143],"these":[144],"shortcomings":[145],"also":[146],"disadvantage":[149],"poor":[151],"generalization":[152],"ability":[153],"amount":[160],"noise.":[162]},"abstract_inverted_index_v3":null,"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W3033704403","counts_by_year":[],"updated_date":"2025-02-03T10:31:57.209370","created_date":"2020-06-12"}