{"id":"https://openalex.org/W7203483526","doi":"https://doi.org/10.48550/arxiv.2608.13294","title":"Deterministic Maximum Likelihood Direction Finding in the Mixture Noise of Gaussian and Spherically Invariant Components","display_name":"Deterministic Maximum Likelihood Direction Finding in the Mixture Noise of Gaussian and Spherically Invariant Components","publication_year":2026,"publication_date":"2026-08-13","ids":{"openalex":"https://openalex.org/W7203483526","doi":"https://doi.org/10.48550/arxiv.2608.13294"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2608.13294","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.13294","pdf_url":"https://arxiv.org/pdf/2608.13294","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2608.13294","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134511884","display_name":"Mingyan Gong","orcid":"https://orcid.org/0000-0002-4052-1498","observed_orcids":["https://orcid.org/0000-0002-4052-1498"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Gong, Mingyan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5134511884"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10931","display_name":"Direction-of-Arrival Estimation Techniques","score":0.9990000128746033,"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":0.9990000128746033,"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/T10860","display_name":"Speech and Audio Processing","score":0.0003000000142492354,"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":9.999999747378752e-05,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/doa-estimation","display_name":"direction of arrival estimation","score":0.8859999775886536},{"id":"https://openalex.org/keywords/spherically-invariant-random-process","display_name":"spherically invariant random process","score":0.8489999771118164},{"id":"https://openalex.org/keywords/impulse-noise","display_name":"impulsive noise","score":0.972000002861023},{"id":"https://openalex.org/keywords/ecm-algorithm","display_name":"ECM algorithm","score":0.6290000081062317},{"id":"https://openalex.org/keywords/ecme-algorithm","display_name":"ECME algorithm","score":0.9929999709129333},{"id":"https://openalex.org/keywords/expectation-conditional-maximization","display_name":"expectation conditional maximization","score":0.8679999709129333},{"id":"https://openalex.org/keywords/expectation-maximization-algorithm","display_name":"expectation-maximization algorithm","score":0.7250000238418579},{"id":"https://openalex.org/keywords/cramer-rao-lower-bound","display_name":"Cramer-Rao lower bound","score":0.8849999904632568},{"id":"https://openalex.org/keywords/log-likelihood","display_name":"log-likelihood","score":0.7639999985694885}],"concepts":[{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.7207000255584717},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6402999758720398},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.5471000075340271},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5371999740600586},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.5217999815940857},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5180000066757202},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.48500001430511475},{"id":"https://openalex.org/C2776330181","wikidata":"https://www.wikidata.org/wiki/Q18358244","display_name":"Maximization","level":2,"score":0.4821000099182129},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.46470001339912415},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.4449000060558319},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.41690000891685486},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4156000018119812},{"id":"https://openalex.org/C89106044","wikidata":"https://www.wikidata.org/wiki/Q45284","display_name":"Likelihood function","level":3,"score":0.4059000015258789},{"id":"https://openalex.org/C182081679","wikidata":"https://www.wikidata.org/wiki/Q1275153","display_name":"Expectation\u2013maximization algorithm","level":3,"score":0.40470001101493835},{"id":"https://openalex.org/C2778029271","wikidata":"https://www.wikidata.org/wiki/Q5421931","display_name":"Extension (predicate logic)","level":2,"score":0.3968999981880188},{"id":"https://openalex.org/C191462741","wikidata":"https://www.wikidata.org/wiki/Q6795902","display_name":"Maximum likelihood sequence estimation","level":3,"score":0.3619000017642975},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3361000120639801},{"id":"https://openalex.org/C11577676","wikidata":"https://www.wikidata.org/wiki/Q134237","display_name":"Square root","level":2,"score":0.3330000042915344},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.3176000118255615},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.3140999972820282},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.30730000138282776},{"id":"https://openalex.org/C95763700","wikidata":"https://www.wikidata.org/wiki/Q578985","display_name":"Convergence of random variables","level":3,"score":0.2752000093460083},{"id":"https://openalex.org/C51267290","wikidata":"https://www.wikidata.org/wiki/Q5527848","display_name":"Gaussian random field","level":4,"score":0.2727999985218048},{"id":"https://openalex.org/C71907059","wikidata":"https://www.wikidata.org/wiki/Q223323","display_name":"Root mean square","level":2,"score":0.271699994802475},{"id":"https://openalex.org/C18294631","wikidata":"https://www.wikidata.org/wiki/Q2621185","display_name":"Location parameter","level":3,"score":0.27149999141693115},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.265500009059906},{"id":"https://openalex.org/C87698059","wikidata":"https://www.wikidata.org/wiki/Q1808960","display_name":"LTI system theory","level":3,"score":0.2612999975681305},{"id":"https://openalex.org/C2986577269","wikidata":"https://www.wikidata.org/wiki/Q11306265","display_name":"Random noise","level":2,"score":0.2522999942302704}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2608.13294","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.13294","pdf_url":"https://arxiv.org/pdf/2608.13294","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2608.13294","doi":"https://doi.org/10.48550/arxiv.2608.13294","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2608.13294","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2608.13294","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.13294","pdf_url":"https://arxiv.org/pdf/2608.13294","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7203483526.pdf","grobid_xml":"https://content.openalex.org/works/W7203483526.grobid-xml"},"referenced_works_count":1,"referenced_works":["https://openalex.org/W7160312349"],"related_works":["https://openalex.org/W7160312349","https://openalex.org/W4382341346","https://openalex.org/W4386697012","https://openalex.org/W4308505137","https://openalex.org/W3170252779","https://openalex.org/W2075721610","https://openalex.org/W1932422152","https://openalex.org/W1571522380","https://openalex.org/W7155500453","https://openalex.org/W4417102503"],"abstract_inverted_index":{"Spherically":[0],"invariant":[1],"(SI)":[2],"random":[3],"processes":[4],"can":[5],"model":[6],"impulsive":[7],"noise":[8,15],"and":[9,18,47,76,94,115,127],"unreliable":[10],"measurements.":[11],"Recently,":[12],"the":[13,33,41,54,63,71,81,104,121,138,141,146],"mixture":[14],"of":[16,40,70,110,133],"Gaussian":[17],"SI":[19],"components":[20],"has":[21,44],"been":[22,45],"used":[23],"in":[24],"deterministic":[25,105],"maximum":[26],"likelihood":[27],"direction":[28],"finding.":[29],"In":[30,60],"this":[31,61],"context,":[32],"Expectation-Conditional":[34],"Maximization":[35],"(ECM)":[36],"algorithm,":[37,43,67,73],"an":[38,68],"extension":[39,69],"expectation-maximization":[42],"applied":[46,75],"designed.":[48],"However,":[49],"simulation":[50],"results":[51,118],"show":[52],"that":[53,120],"ECM":[55,64,72],"algorithm":[56,123],"always":[57],"improperly":[58],"converges.":[59],"article,":[62],"Either":[65],"(ECME)":[66],"is":[74],"designed,":[77],"which":[78],"additionally":[79],"utilizes":[80],"actual":[82],"log-likelihood":[83],"function":[84],"to":[85,98],"first":[86],"update":[87],"partial":[88],"parameter":[89,101],"estimates":[90,135],"at":[91],"every":[92],"iteration":[93],"does":[95],"not":[96],"need":[97],"initialize":[99],"all":[100],"estimates.":[102],"Moreover,":[103],"Cramer-Rao":[106],"low":[107],"bounds":[108],"(CRLBs)":[109],"DOA":[111,134],"estimators":[112],"are":[113,149],"derived":[114,147],"compared.":[116],"Simulation":[117],"indicate":[119],"ECME":[122],"exhibits":[124],"proper":[125],"convergence":[126],"its":[128],"root":[129],"mean":[130],"square":[131],"errors":[132],"asymptotically":[136],"approach":[137],"CRLBs":[139,148],"as":[140],"signal":[142],"powers":[143],"increase,":[144],"i.e.,":[145],"correct.":[150]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2026-08-15T00:00:00"}