{"id":"https://openalex.org/W4312717825","doi":"https://doi.org/10.1109/tgrs.2022.3213277","title":"Space Target Anomaly Detection Based on Gaussian Mixture Model and Micro-Doppler Features","display_name":"Space Target Anomaly Detection Based on Gaussian Mixture Model and Micro-Doppler Features","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4312717825","doi":"https://doi.org/10.1109/tgrs.2022.3213277"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2022.3213277","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3213277","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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 Geoscience and Remote Sensing","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/A5019758018","display_name":"Jianwen Wang","orcid":"https://orcid.org/0000-0003-0211-2519"},"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":true,"raw_author_name":"Jianwen Wang","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-0211-2519","affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100438655","display_name":"Gang Li","orcid":"https://orcid.org/0000-0001-9755-2781"},"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":"Gang Li","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-9755-2781","affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041769049","display_name":"Zhichun Zhao","orcid":"https://orcid.org/0000-0001-5037-4527"},"institutions":[{"id":"https://openalex.org/I4388482657","display_name":"Shenzhen MSU-BIT University","ror":"https://ror.org/02q963474","country_code":null,"type":"education","lineage":["https://openalex.org/I4388482657"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhichun Zhao","raw_affiliation_strings":["Department of Engineering, Shenzhen MSU-BIT University, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0001-5037-4527","affiliations":[{"raw_affiliation_string":"Department of Engineering, Shenzhen MSU-BIT University, Shenzhen, China","institution_ids":["https://openalex.org/I4388482657"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115430439","display_name":"Jian Jiao","orcid":"https://orcid.org/0000-0001-8629-6559"},"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":"Jian Jiao","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101423196","display_name":"Shuai Ding","orcid":"https://orcid.org/0000-0001-6754-5556"},"institutions":[{"id":"https://openalex.org/I2800372957","display_name":"China Electronics Technology Group Corporation","ror":"https://ror.org/0098hst83","country_code":"CN","type":"company","lineage":["https://openalex.org/I2800372957"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuai Ding","raw_affiliation_strings":["38th Research Institute, China Electronics Technology Group Corporation, Hefei, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"38th Research Institute, China Electronics Technology Group Corporation, Hefei, China","institution_ids":["https://openalex.org/I2800372957"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100676186","display_name":"Kunpeng Wang","orcid":"https://orcid.org/0000-0002-9412-7867"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kunpeng Wang","raw_affiliation_strings":["Beijing Institute of Tracking and Telecommunications Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Institute of Tracking and Telecommunications Technology, Beijing, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089242422","display_name":"Meiya Duan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Meiya Duan","raw_affiliation_strings":["Beijing Institute of Tracking and Telecommunications Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Institute of Tracking and Telecommunications Technology, Beijing, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5019758018"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":1.9422,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.88348436,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"60","issue":null,"first_page":"1","last_page":"11"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9991000294685364,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9991000294685364,"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/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.9861999750137329,"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/T11515","display_name":"Bacillus and Francisella bacterial research","score":0.9646999835968018,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7043744325637817},{"id":"https://openalex.org/keywords/probability-density-function","display_name":"Probability density function","score":0.5430977940559387},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.5031291842460632},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.4839690029621124},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48227742314338684},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47198206186294556},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4643886983394623},{"id":"https://openalex.org/keywords/doppler-effect","display_name":"Doppler effect","score":0.4515702426433563},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.44637876749038696},{"id":"https://openalex.org/keywords/expectation\u2013maximization-algorithm","display_name":"Expectation\u2013maximization algorithm","score":0.4367932975292206},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4284690320491791},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.39554134011268616},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3410833775997162},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.23824161291122437},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.18996772170066833}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7043744325637817},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.5430977940559387},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.5031291842460632},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.4839690029621124},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48227742314338684},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47198206186294556},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4643886983394623},{"id":"https://openalex.org/C142757262","wikidata":"https://www.wikidata.org/wiki/Q76436","display_name":"Doppler effect","level":2,"score":0.4515702426433563},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.44637876749038696},{"id":"https://openalex.org/C182081679","wikidata":"https://www.wikidata.org/wiki/Q1275153","display_name":"Expectation\u2013maximization algorithm","level":3,"score":0.4367932975292206},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4284690320491791},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.39554134011268616},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3410833775997162},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.23824161291122437},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.18996772170066833},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","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/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2022.3213277","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3213277","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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 Geoscience and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4541440056","display_name":null,"funder_award_id":"62101304","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8362816022","display_name":null,"funder_award_id":"61925106","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W1723619723","https://openalex.org/W2049633694","https://openalex.org/W2071472167","https://openalex.org/W2102372511","https://openalex.org/W2125799222","https://openalex.org/W2144182447","https://openalex.org/W2153594606","https://openalex.org/W2312351803","https://openalex.org/W2793121084","https://openalex.org/W2805777666","https://openalex.org/W2830057768","https://openalex.org/W2884252068","https://openalex.org/W2891657227","https://openalex.org/W2910714756","https://openalex.org/W2915512257","https://openalex.org/W2942388639","https://openalex.org/W2998321685","https://openalex.org/W3003376863","https://openalex.org/W3029679819","https://openalex.org/W3045521046","https://openalex.org/W3107126660","https://openalex.org/W3110415688","https://openalex.org/W3115515760","https://openalex.org/W3126621391","https://openalex.org/W3130991151","https://openalex.org/W3136467379","https://openalex.org/W3137199127","https://openalex.org/W3154732924","https://openalex.org/W3160939982","https://openalex.org/W3176585657","https://openalex.org/W3186743108","https://openalex.org/W3197789437","https://openalex.org/W3200102517","https://openalex.org/W3217775520","https://openalex.org/W4206897147","https://openalex.org/W4210318143","https://openalex.org/W4210702177","https://openalex.org/W4213364527","https://openalex.org/W4226498398","https://openalex.org/W4230765542","https://openalex.org/W4256141317","https://openalex.org/W4299406773","https://openalex.org/W4302367531","https://openalex.org/W4322703851","https://openalex.org/W6801575038"],"related_works":["https://openalex.org/W3186512740","https://openalex.org/W3017266184","https://openalex.org/W3194885736","https://openalex.org/W3046391934","https://openalex.org/W4363671829","https://openalex.org/W2473373438","https://openalex.org/W2368486525","https://openalex.org/W2077224612","https://openalex.org/W2153481672","https://openalex.org/W84255947"],"abstract_inverted_index":{"With":[0],"the":[1,39,44,54,64,69,82,85,89,115,129,135,145,150,154],"dramatic":[2],"increase":[3],"in":[4,14],"human":[5],"space":[6,16,45],"activities,":[7],"anomaly":[8,23,105,131],"detection":[9,24,132],"becomes":[10],"an":[11,22,104],"important":[12],"issue":[13],"passive":[15],"target":[17],"surveillance.":[18],"In":[19],"this":[20],"article,":[21],"algorithm":[25],"based":[26],"on":[27,53],"Gaussian":[28,60,97],"mixture":[29,98],"model":[30,99],"and":[31,50,76,88,94,100,120,141,153],"radar":[32,55],"micro-Doppler":[33,72],"features":[34,73,87],"is":[35,107,138],"proposed":[36,136],"to":[37,63],"detect":[38],"abnormal":[40],"motion":[41,66],"status":[42],"of":[43,68,84,134],"target.":[46],"By":[47],"coherent":[48],"sampling":[49],"time-frequency":[51],"analysis":[52],"echo":[56],"with":[57],"additive":[58],"white":[59],"noise":[61],"corresponding":[62,90],"normal":[65,79],"statuses":[67],"target,":[70],"four":[71],"are":[74,92],"extracted":[75],"tested":[77],"for":[78,111],"distribution.":[80],"Furthermore,":[81],"distribution":[83],"multi-dimensional":[86],"parameters":[91],"fitted":[93,116],"estimated":[95],"by":[96,109],"expectation-maximization":[101],"algorithm.":[102],"Then,":[103],"detector":[106],"derived":[108],"solving":[110],"decision":[112],"region":[113],"using":[114],"probability":[117],"density":[118],"function":[119],"a":[121],"preset":[122],"confidence":[123],"level.":[124],"Experimental":[125],"results":[126],"show":[127],"that":[128],"average":[130],"rate":[133],"method":[137],"16.7%,":[139],"19.1%,":[140],"34.0%":[142],"higher":[143],"than":[144],"one-class":[146],"support":[147],"vector":[148],"machine,":[149],"convex":[151],"hull,":[152],"convolutional":[155],"autoencoder-based":[156],"methods,":[157],"respectively.":[158]},"counts_by_year":[{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}