{"id":"https://openalex.org/W4409883092","doi":"https://doi.org/10.1109/tim.2025.3565060","title":"High-Resolution Off-Grid Direction-of-Arrival Estimation Using Laplacian Scale Mixture Prior Under Low SNR Conditions","display_name":"High-Resolution Off-Grid Direction-of-Arrival Estimation Using Laplacian Scale Mixture Prior Under Low SNR Conditions","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4409883092","doi":"https://doi.org/10.1109/tim.2025.3565060"},"language":"en","primary_location":{"id":"doi:10.1109/tim.2025.3565060","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2025.3565060","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"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 Instrumentation and Measurement","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/A5048247903","display_name":"Yiding Wang","orcid":"https://orcid.org/0009-0006-4936-996X"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yiding Wang","raw_affiliation_strings":["School of Artificial Intelligence, Xidian University, Xi&#x2019;an, China"],"raw_orcid":"https://orcid.org/0009-0006-4936-996X","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Xidian University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068467613","display_name":"Jiongda Song","orcid":"https://orcid.org/0009-0003-2821-7607"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiongda Song","raw_affiliation_strings":["School of Artificial Intelligence, Xidian University, Xi&#x2019;an, China"],"raw_orcid":"https://orcid.org/0009-0003-2821-7607","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Xidian University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030644029","display_name":"Yuanhao Li","orcid":"https://orcid.org/0009-0009-6822-4629"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanhao Li","raw_affiliation_strings":["School of Artificial Intelligence, Xidian University, Xi&#x2019;an, China"],"raw_orcid":"https://orcid.org/0009-0009-6822-4629","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Xidian University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063069006","display_name":"Guanghui Zhao","orcid":"https://orcid.org/0000-0002-2348-0532"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guanghui Zhao","raw_affiliation_strings":["School of Artificial Intelligence, Xidian University, Xi&#x2019;an, China"],"raw_orcid":"https://orcid.org/0000-0002-2348-0532","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Xidian University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":3.6097,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.92851354,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":"74","issue":null,"first_page":"1","last_page":"13"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10931","display_name":"Direction-of-Arrival Estimation Techniques","score":0.9979000091552734,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10931","display_name":"Direction-of-Arrival Estimation Techniques","score":0.9979000091552734,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10891","display_name":"Radar Systems and Signal Processing","score":0.9965000152587891,"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/T10860","display_name":"Speech and Audio Processing","score":0.996399998664856,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/scale","display_name":"Scale (ratio)","score":0.5935218334197998},{"id":"https://openalex.org/keywords/grid","display_name":"Grid","score":0.5070927143096924},{"id":"https://openalex.org/keywords/resolution","display_name":"Resolution (logic)","score":0.4184998571872711},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.34488439559936523},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.32179898023605347},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.3172188699245453},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.1829427182674408},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.11070087552070618}],"concepts":[{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5935218334197998},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.5070927143096924},{"id":"https://openalex.org/C138268822","wikidata":"https://www.wikidata.org/wiki/Q1051925","display_name":"Resolution (logic)","level":2,"score":0.4184998571872711},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.34488439559936523},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.32179898023605347},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.3172188699245453},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.1829427182674408},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.11070087552070618},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tim.2025.3565060","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2025.3565060","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"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 Instrumentation and Measurement","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","score":0.4399999976158142,"display_name":"Climate action"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W653761051","https://openalex.org/W1977423147","https://openalex.org/W2000721204","https://openalex.org/W2028823365","https://openalex.org/W2029938263","https://openalex.org/W2047130136","https://openalex.org/W2060108923","https://openalex.org/W2065513175","https://openalex.org/W2071284784","https://openalex.org/W2097670707","https://openalex.org/W2100556411","https://openalex.org/W2103519107","https://openalex.org/W2113638573","https://openalex.org/W2122315118","https://openalex.org/W2122466069","https://openalex.org/W2127870457","https://openalex.org/W2128131274","https://openalex.org/W2140641933","https://openalex.org/W2146571341","https://openalex.org/W2162654459","https://openalex.org/W2162876243","https://openalex.org/W2166543153","https://openalex.org/W2198155329","https://openalex.org/W2290170958","https://openalex.org/W2323168390","https://openalex.org/W2497431972","https://openalex.org/W2513373031","https://openalex.org/W2593128366","https://openalex.org/W2767083247","https://openalex.org/W2794383492","https://openalex.org/W2807494282","https://openalex.org/W2887288499","https://openalex.org/W2915414989","https://openalex.org/W3001003127","https://openalex.org/W3015525590","https://openalex.org/W3083023184","https://openalex.org/W3194646605","https://openalex.org/W4250955649","https://openalex.org/W4309263262","https://openalex.org/W4320001371","https://openalex.org/W6681380847","https://openalex.org/W6695750744"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2935759653","https://openalex.org/W3105167352","https://openalex.org/W54078636","https://openalex.org/W2954470139","https://openalex.org/W1501425562","https://openalex.org/W2902782467","https://openalex.org/W3084825885","https://openalex.org/W2298861036","https://openalex.org/W2271181815"],"abstract_inverted_index":{"As":[0],"an":[1,57,65,150],"crucial":[2],"branch":[3],"of":[4,9,30,60,107,129,132,215],"array":[5],"signal":[6,135],"processing,":[7],"direction":[8],"arrival":[10],"(DOA)":[11],"has":[12,20],"been":[13],"widely":[14],"applied":[15],"in":[16,24,42,145,212],"various":[17],"fields":[18],"and":[19,109,120,202],"garnered":[21],"significant":[22],"attention":[23],"recent":[25],"years.":[26],"However,":[27],"the":[28,31,94,117,121,127,130,133,173,178,193,213],"performance":[29],"DOA":[32,50,67,85,180],"estimation":[33,51,68,86,181],"is":[34,56,76,143,166,196],"severely":[35],"affected":[36],"by":[37,92],"low":[38,53,89,216],"signal-to-noise":[39],"ratio":[40],"(SNR)":[41],"practical":[43],"applications.":[44],"Therefore,":[45,140],"how":[46],"to":[47,112,148,168,205],"achieve":[48,83],"high-resolution":[49,84],"under":[52,88,199],"SNR":[54,90],"condition":[55,91],"issue":[58],"worthy":[59],"attention.":[61],"In":[62],"this":[63],"paper,":[64],"off-grid":[66,170],"method":[69],"based":[70,186],"on":[71,187],"variational":[72],"Bayesian":[73,208],"inference":[74],"(VBI)":[75],"proposed,":[77],"denoted":[78],"as":[79],"OG-LSMVBI,":[80],"which":[81],"can":[82],"results":[87,185],"introducing":[93],"Laplacian":[95,108,118],"scale":[96],"mixture":[97],"(LSM)":[98],"priors.":[99],"First,":[100],"we":[101],"introduce":[102],"a":[103,162],"hierarchical":[104],"prior":[105,119],"consisting":[106],"inverse":[110],"gamma":[111],"model":[113],"sparse":[114,134,207],"signals.":[115],"Since":[116],"Gaussian":[122,155],"likelihood":[123],"are":[124],"not":[125],"conjugate,":[126],"form":[128],"posterior":[131,152],"cannot":[136],"be":[137],"determined":[138],"directly.":[139],"Laplace":[141],"approximation":[142],"employed":[144],"VBI,":[146],"aiming":[147],"derive":[149],"approximate":[151],"distribution":[153,156],"obeying":[154],"through":[157],"second-order":[158],"Taylor":[159],"expansion.":[160],"Finally,":[161],"grid":[163],"refinement":[164],"process":[165],"implemented":[167],"estimate":[169],"errors":[171],"within":[172],"VBI":[174],"iteration,":[175],"thus":[176],"refining":[177],"final":[179],"results.":[182],"Numerical":[183],"experimental":[184],"simulated":[188],"data":[189],"have":[190],"substantiated":[191],"that":[192],"proposed":[194],"algorithm":[195],"more":[197],"effective":[198],"both":[200],"single-snapshot":[201],"multi-snapshots":[203],"compared":[204],"other":[206],"learning":[209],"methods,":[210],"especially":[211],"case":[214],"SNR.":[217]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-04-29T00:00:00"}
