{"id":"https://openalex.org/W1975835710","doi":"https://doi.org/10.1109/ssp.2014.6884672","title":"Sparse Bayesian SAR imaging of moving target via the EXCOV method","display_name":"Sparse Bayesian SAR imaging of moving target via the EXCOV method","publication_year":2014,"publication_date":"2014-06-01","ids":{"openalex":"https://openalex.org/W1975835710","doi":"https://doi.org/10.1109/ssp.2014.6884672","mag":"1975835710"},"language":"en","primary_location":{"id":"doi:10.1109/ssp.2014.6884672","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssp.2014.6884672","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE Workshop on Statistical Signal Processing (SSP)","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":"https://openalex.org/A5110371706","display_name":"Wuge Su","orcid":null},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wuge Su","raw_affiliation_strings":["College of Electronic Science and Engineering, National University of Defense Technology, Changsha, China","Coll. of Electron. Sci. & Eng., Nat. Univ. of Defense Technol., ChangSha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Electronic Science and Engineering, National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]},{"raw_affiliation_string":"Coll. of Electron. Sci. & Eng., Nat. Univ. of Defense Technol., ChangSha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100757637","display_name":"Hongqiang Wang","orcid":"https://orcid.org/0000-0002-2522-9552"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongqiang Wang","raw_affiliation_strings":["College of Electronic Science and Engineering, National University of Defense Technology, Changsha, China","Coll. of Electron. Sci. & Eng., Nat. Univ. of Defense Technol., ChangSha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Electronic Science and Engineering, National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]},{"raw_affiliation_string":"Coll. of Electron. Sci. & Eng., Nat. Univ. of Defense Technol., ChangSha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018318591","display_name":"Bin Deng","orcid":"https://orcid.org/0000-0002-0289-3469"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Deng","raw_affiliation_strings":["College of Electronic Science and Engineering, National University of Defense Technology, Changsha, China","Coll. of Electron. Sci. & Eng., Nat. Univ. of Defense Technol., ChangSha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Electronic Science and Engineering, National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]},{"raw_affiliation_string":"Coll. of Electron. Sci. & Eng., Nat. Univ. of Defense Technol., ChangSha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100617180","display_name":"Ruijun Wang","orcid":"https://orcid.org/0000-0001-6277-0113"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruijun Wang","raw_affiliation_strings":["College of Electronic Science and Engineering, National University of Defense Technology, Changsha, China","Coll. of Electron. Sci. & Eng., Nat. Univ. of Defense Technol., ChangSha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Electronic Science and Engineering, National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]},{"raw_affiliation_string":"Coll. of Electron. Sci. & Eng., Nat. Univ. of Defense Technol., ChangSha, China","institution_ids":["https://openalex.org/I170215575"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.05646635,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"26","issue":null,"first_page":"448","last_page":"451"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.9997000098228455,"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/T11739","display_name":"Microwave Imaging and Scattering Analysis","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/computer-science","display_name":"Computer science","score":0.7106671333312988},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.6570402383804321},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6466144919395447},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5750385522842407},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.5476362705230713},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5056960582733154},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4925154149532318},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.44175320863723755},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.43694791197776794},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4294569492340088},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.41703125834465027},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4106208086013794}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7106671333312988},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.6570402383804321},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6466144919395447},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5750385522842407},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.5476362705230713},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5056960582733154},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4925154149532318},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.44175320863723755},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.43694791197776794},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4294569492340088},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.41703125834465027},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4106208086013794},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ssp.2014.6884672","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssp.2014.6884672","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE Workshop on Statistical Signal Processing (SSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2006832476","https://openalex.org/W2071284784","https://openalex.org/W2098543829","https://openalex.org/W2106133606","https://openalex.org/W2125045489","https://openalex.org/W2129904408","https://openalex.org/W2142653080","https://openalex.org/W2145096794","https://openalex.org/W2147016563","https://openalex.org/W2147549783","https://openalex.org/W2148154358","https://openalex.org/W2152498207","https://openalex.org/W2159217520","https://openalex.org/W2170850527","https://openalex.org/W2289917018","https://openalex.org/W6652170451"],"related_works":["https://openalex.org/W2158224665","https://openalex.org/W2379589510","https://openalex.org/W4300044672","https://openalex.org/W2810730439","https://openalex.org/W1881631164","https://openalex.org/W2358292267","https://openalex.org/W2378166785","https://openalex.org/W1964277756","https://openalex.org/W2537887767","https://openalex.org/W2390140919"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,19],"method":[4,81,87,125],"for":[5,91],"imaging":[6,17],"of":[7,21,62,135,151],"moving":[8,43],"targets":[9,44],"via":[10],"the":[11,16,38,49,53,60,70,83,133,139,149],"compress":[12],"sensing":[13],"by":[14,68],"treating":[15],"as":[18,148],"problem":[20],"signal":[22,33],"representation":[23,34],"in":[24,48,117,132],"an":[25],"over-complete":[26],"dictionary.":[27],"The":[28,120],"essential":[29],"idea":[30],"behind":[31],"sparse":[32,78,113],"models":[35],"comes":[36],"from":[37],"fact":[39],"that":[40],"SAR":[41,55],"ground":[42],"are":[45,66],"sparsely":[46],"distributed":[47],"observation":[50],"scene":[51],"and":[52,74,98,138,145],"received":[54],"echo":[56],"is":[57,89,96,109,153],"decomposed":[58],"into":[59],"sum":[61],"basis":[63],"sub-signals,":[64],"which":[65,108],"generated":[67],"discretizing":[69],"target":[71,140],"spatial":[72],"domain":[73],"velocity":[75],"domain.":[76],"A":[77],"Bayesian":[79,114],"recovering":[80],"named":[82],"expansion-compression":[84],"variance-component":[85],"based":[86],"(ExCoV)":[88],"used":[90],"image":[92,141],"reconstruction":[93],"since":[94],"it":[95],"automatic":[97],"demands":[99],"no":[100],"prior":[101],"knowledge":[102],"about":[103],"signal-sparsity":[104],"or":[105],"measure-noise":[106],"levels,":[107],"significantly":[110],"faster":[111],"than":[112],"learning,":[115],"particularly":[116],"large-scale":[118],"problems.":[119],"numerical":[121],"experiments":[122],"using":[123],"ExCoV":[124],"have":[126],"estimated":[127],"moving-targets":[128],"at":[129],"different":[130],"velocities":[131],"case":[134],"low":[136],"SNR,":[137],"has":[142],"higher":[143],"resolution":[144],"lower":[146],"side-lobe":[147],"number":[150],"measurements":[152],"small":[154],"compared":[155],"with":[156],"traditional":[157],"algorithms.":[158]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
