{"id":"https://openalex.org/W4220851106","doi":"https://doi.org/10.3390/rs14061434","title":"MUSAK: A Multi-Scale Space Kinematic Method for Drone Detection","display_name":"MUSAK: A Multi-Scale Space Kinematic Method for Drone Detection","publication_year":2022,"publication_date":"2022-03-16","ids":{"openalex":"https://openalex.org/W4220851106","doi":"https://doi.org/10.3390/rs14061434"},"language":"en","primary_location":{"id":"doi:10.3390/rs14061434","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs14061434","pdf_url":"https://www.mdpi.com/2072-4292/14/6/1434/pdf?version=1647425492","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/2072-4292/14/6/1434/pdf?version=1647425492","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5006341703","display_name":"Sunxiangyu Liu","orcid":"https://orcid.org/0000-0003-1100-2776"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sunxiangyu Liu","raw_affiliation_strings":["Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China","School of Aerospace Engineering, Tsinghua University, Beijing 100084, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"School of Aerospace Engineering, Tsinghua University, Beijing 100084, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080881674","display_name":"Guitao Li","orcid":"https://orcid.org/0000-0001-7089-3367"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guitao Li","raw_affiliation_strings":["School of Aerospace Engineering, Tsinghua University, Beijing 100084, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Aerospace Engineering, Tsinghua University, Beijing 100084, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061720505","display_name":"Yafeng Zhan","orcid":"https://orcid.org/0000-0002-3869-4592"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yafeng Zhan","raw_affiliation_strings":["Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028272399","display_name":"Peng Gao","orcid":"https://orcid.org/0000-0003-2230-3937"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Peng Gao","raw_affiliation_strings":["School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China","institution_ids":["https://openalex.org/I125839683"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5028272399"],"corresponding_institution_ids":["https://openalex.org/I125839683"],"apc_list":{"value":2700,"currency":"CHF","value_usd":3258},"apc_paid":{"value":2700,"currency":"CHF","value_usd":3258},"fwci":0.3271,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.50062497,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"14","issue":"6","first_page":"1434","last_page":"1434"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9988999962806702,"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":0.9988999962806702,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9986000061035156,"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"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9955000281333923,"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/computer-science","display_name":"Computer science","score":0.7812856435775757},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7491225004196167},{"id":"https://openalex.org/keywords/drone","display_name":"Drone","score":0.7075586318969727},{"id":"https://openalex.org/keywords/kinematics","display_name":"Kinematics","score":0.6597126722335815},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6330877542495728},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6211830377578735},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.5947216153144836},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.5504360198974609},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.49121493101119995},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44438934326171875},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.15768542885780334},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.06927093863487244},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.060712188482284546}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7812856435775757},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7491225004196167},{"id":"https://openalex.org/C59519942","wikidata":"https://www.wikidata.org/wiki/Q650665","display_name":"Drone","level":2,"score":0.7075586318969727},{"id":"https://openalex.org/C39920418","wikidata":"https://www.wikidata.org/wiki/Q11476","display_name":"Kinematics","level":2,"score":0.6597126722335815},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6330877542495728},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6211830377578735},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.5947216153144836},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.5504360198974609},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.49121493101119995},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44438934326171875},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.15768542885780334},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.06927093863487244},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.060712188482284546},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C74650414","wikidata":"https://www.wikidata.org/wiki/Q11397","display_name":"Classical mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/rs14061434","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs14061434","pdf_url":"https://www.mdpi.com/2072-4292/14/6/1434/pdf?version=1647425492","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:88bf84e3dd2e4e60b3e5155b5a2651a3","is_oa":false,"landing_page_url":"https://doaj.org/article/88bf84e3dd2e4e60b3e5155b5a2651a3","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 14, Iss 6, p 1434 (2022)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/14/6/1434/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs14061434","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs14061434","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs14061434","pdf_url":"https://www.mdpi.com/2072-4292/14/6/1434/pdf?version=1647425492","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":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4220851106.pdf","grobid_xml":"https://content.openalex.org/works/W4220851106.grobid-xml"},"referenced_works_count":46,"referenced_works":["https://openalex.org/W764651262","https://openalex.org/W1955741794","https://openalex.org/W1990864775","https://openalex.org/W1991544872","https://openalex.org/W2000247039","https://openalex.org/W2119605622","https://openalex.org/W2144528190","https://openalex.org/W2150066425","https://openalex.org/W2163639760","https://openalex.org/W2346349911","https://openalex.org/W2518581615","https://openalex.org/W2552900565","https://openalex.org/W2559489103","https://openalex.org/W2560474170","https://openalex.org/W2625219738","https://openalex.org/W2718471533","https://openalex.org/W2765277977","https://openalex.org/W2765322173","https://openalex.org/W2766821365","https://openalex.org/W2803155838","https://openalex.org/W2886780319","https://openalex.org/W2894777979","https://openalex.org/W2922305478","https://openalex.org/W2934533499","https://openalex.org/W2942231644","https://openalex.org/W2964286567","https://openalex.org/W2989663908","https://openalex.org/W2990154685","https://openalex.org/W2991277185","https://openalex.org/W3010090948","https://openalex.org/W3014167990","https://openalex.org/W3036543015","https://openalex.org/W3038211455","https://openalex.org/W3080585449","https://openalex.org/W3086436251","https://openalex.org/W3104778224","https://openalex.org/W3105346219","https://openalex.org/W3105826819","https://openalex.org/W3153216549","https://openalex.org/W3176027594","https://openalex.org/W3181408657","https://openalex.org/W3194236473","https://openalex.org/W3197016211","https://openalex.org/W3210586215","https://openalex.org/W3215216361","https://openalex.org/W4200230955"],"related_works":["https://openalex.org/W4229448053","https://openalex.org/W4327774218","https://openalex.org/W2059768187","https://openalex.org/W4247925126","https://openalex.org/W4312858960","https://openalex.org/W4386036939","https://openalex.org/W4379143281","https://openalex.org/W2605096541","https://openalex.org/W3200286695","https://openalex.org/W4386322305"],"abstract_inverted_index":{"Accurate":[0],"and":[1,8,65,78,108,142,170],"robust":[2],"drone":[3,88],"detection":[4,51,85],"is":[5,93],"an":[6],"important":[7],"challenging":[9],"task.":[10],"However,":[11],"on":[12,19,95,165],"this":[13,37],"issue,":[14],"previous":[15,137],"research,":[16],"whether":[17],"based":[18],"appearance":[20],"or":[21],"motion":[22,58,112],"features,":[23],"has":[24],"not":[25],"yet":[26],"provided":[27],"a":[28,33,43,96,124,127,161],"satisfactory":[29],"solution,":[30],"especially":[31],"under":[32],"complex":[34],"background.":[35],"To":[36],"end,":[38],"the":[39,47,57,75,121,136,143,150],"present":[40],"work":[41],"proposes":[42],"motion-based":[44,140],"method":[45,52,92,152],"termed":[46],"Multi-Scale":[48],"Space":[49],"Kinematic":[50],"(MUSAK).":[53],"It":[54],"fully":[55],"leverages":[56],"patterns":[59],"by":[60,123],"extracting":[61],"3D,":[62],"pseudo":[63],"3D":[64],"2D":[66],"kinematic":[67],"parameters":[68],"at":[69],"three":[70,80],"scale":[71],"spaces":[72],"according":[73],"to":[74],"keypoints":[76],"quality":[77],"builds":[79],"Gated":[81],"Recurrent":[82],"Unit":[83],"(GRU)-based":[84],"branches":[86],"for":[87],"recognition.":[89],"The":[90,114],"MUSAK":[91,119,145],"evaluated":[94],"hybrid":[97,144],"dataset":[98,102],"named":[99],"multiscale":[100],"UAV":[101],"(MUD),":[103],"consisting":[104],"of":[105],"public":[106],"datasets":[107],"self-collected":[109],"data":[110],"with":[111,135,149],"labels.":[113],"experimental":[115],"results":[116],"show":[117],"that":[118],"improves":[120],"performance":[122,164],"large":[125],"margin,":[126],"95%":[128],"increase":[129],"in":[130],"average":[131],"precision":[132],"(AP),":[133],"compared":[134],"state-of-the-art":[138],"(SOTA)":[139],"methods,":[141],"method,":[146],"which":[147],"integrates":[148],"appearance-based":[151],"Faster":[153],"Region-based":[154],"Convolutional":[155],"Neural":[156],"Network":[157],"(Faster":[158],"R-CNN),":[159],"achieves":[160],"new":[162],"SOTA":[163],"AP":[166],"metrics":[167],"(AP,":[168],"APM,":[169],"APS).":[171]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2}],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2025-10-10T00:00:00"}