{"id":"https://openalex.org/W2158126964","doi":"https://doi.org/10.1109/icassp.2006.1661161","title":"Underwater Noise Modeling and Direction-Finding Based on Conditional Heteroscedastic Time Series","display_name":"Underwater Noise Modeling and Direction-Finding Based on Conditional Heteroscedastic Time Series","publication_year":2006,"publication_date":"2006-01-01","ids":{"openalex":"https://openalex.org/W2158126964","doi":"https://doi.org/10.1109/icassp.2006.1661161","mag":"2158126964"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2006.1661161","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2006.1661161","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings","raw_type":"proceedings-article"},"type":"conference-paper","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/A5074007015","display_name":"Hadi Amiri","orcid":"https://orcid.org/0000-0003-3278-0729"},"institutions":[{"id":"https://openalex.org/I158248296","display_name":"Amirkabir University of Technology","ror":"https://ror.org/04gzbav43","country_code":"IR","type":"education","lineage":["https://openalex.org/I158248296"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"H. Amiri","raw_affiliation_strings":["Department of Electrical Engineering, Amirkabir University of Technology\uc2a0, Iran","Ministry of J-Agriculture, Engineering Research Institute, Tehran, Iran"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Amirkabir University of Technology\uc2a0, Iran","institution_ids":["https://openalex.org/I158248296"]},{"raw_affiliation_string":"Ministry of J-Agriculture, Engineering Research Institute, Tehran, Iran","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060423856","display_name":"Hamidreza Amindavar","orcid":"https://orcid.org/0000-0002-0954-0674"},"institutions":[{"id":"https://openalex.org/I158248296","display_name":"Amirkabir University of Technology","ror":"https://ror.org/04gzbav43","country_code":"IR","type":"education","lineage":["https://openalex.org/I158248296"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"H. Amindavar","raw_affiliation_strings":["Department of Electrical Engineering, Amirkabir University of Technology\uc2a0, Iran"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Amirkabir University of Technology\uc2a0, Iran","institution_ids":["https://openalex.org/I158248296"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088743433","display_name":"M. Kamarei","orcid":"https://orcid.org/0000-0003-3977-5113"},"institutions":[{"id":"https://openalex.org/I23946033","display_name":"University of Tehran","ror":"https://ror.org/05vf56z40","country_code":"IR","type":"education","lineage":["https://openalex.org/I23946033"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"M. Kamarei","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Tehran, Iran"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Tehran, Iran","institution_ids":["https://openalex.org/I23946033"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3158,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.52311009,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"IV","last_page":"IV"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9991000294685364,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9991000294685364,"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/T11698","display_name":"Underwater Acoustics Research","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/1910","display_name":"Oceanography"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9980000257492065,"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/heteroscedasticity","display_name":"Heteroscedasticity","score":0.8125864267349243},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.7117605209350586},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.70758056640625},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.68250572681427},{"id":"https://openalex.org/keywords/autoregressive-conditional-heteroskedasticity","display_name":"Autoregressive conditional heteroskedasticity","score":0.6117933392524719},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5513265132904053},{"id":"https://openalex.org/keywords/underwater","display_name":"Underwater","score":0.5298020243644714},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4621835947036743},{"id":"https://openalex.org/keywords/noise-measurement","display_name":"Noise measurement","score":0.4614156186580658},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.46002286672592163},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4407029151916504},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.41691768169403076},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3247804045677185},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3187491297721863},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2594448924064636},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.17814043164253235},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.15638047456741333},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.14207860827445984},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.11288774013519287},{"id":"https://openalex.org/keywords/volatility","display_name":"Volatility (finance)","score":0.07995620369911194},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.0768062174320221},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.07059413194656372}],"concepts":[{"id":"https://openalex.org/C101104100","wikidata":"https://www.wikidata.org/wiki/Q1063540","display_name":"Heteroscedasticity","level":2,"score":0.8125864267349243},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.7117605209350586},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.70758056640625},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.68250572681427},{"id":"https://openalex.org/C23922673","wikidata":"https://www.wikidata.org/wiki/Q180752","display_name":"Autoregressive conditional heteroskedasticity","level":3,"score":0.6117933392524719},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5513265132904053},{"id":"https://openalex.org/C98083399","wikidata":"https://www.wikidata.org/wiki/Q3246517","display_name":"Underwater","level":2,"score":0.5298020243644714},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4621835947036743},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.4614156186580658},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.46002286672592163},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4407029151916504},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.41691768169403076},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3247804045677185},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3187491297721863},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2594448924064636},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.17814043164253235},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.15638047456741333},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.14207860827445984},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.11288774013519287},{"id":"https://openalex.org/C91602232","wikidata":"https://www.wikidata.org/wiki/Q756115","display_name":"Volatility (finance)","level":2,"score":0.07995620369911194},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0768062174320221},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.07059413194656372},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"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/icassp.2006.1661161","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2006.1661161","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/14","score":0.7900000214576721,"display_name":"Life below water"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W609405272","https://openalex.org/W653761051","https://openalex.org/W1835099111","https://openalex.org/W1979575715","https://openalex.org/W1999996900","https://openalex.org/W2040637789","https://openalex.org/W2041168275","https://openalex.org/W2044503966","https://openalex.org/W2060148193","https://openalex.org/W2063884165","https://openalex.org/W2110657565","https://openalex.org/W2138273633","https://openalex.org/W2158541895","https://openalex.org/W2161234930","https://openalex.org/W2162654459","https://openalex.org/W2313953460","https://openalex.org/W3146166473","https://openalex.org/W4241115065"],"related_works":["https://openalex.org/W1608601224","https://openalex.org/W2017138702","https://openalex.org/W1978494725","https://openalex.org/W2776656900","https://openalex.org/W1968843728","https://openalex.org/W2031589205","https://openalex.org/W3029813487","https://openalex.org/W2986378528","https://openalex.org/W3178345791","https://openalex.org/W2092661960"],"abstract_inverted_index":{"In":[0,20],"this":[1,21,61,116],"paper,":[2],"we":[3],"propose":[4],"a":[5,88],"new":[6],"method":[7],"for":[8,54,71,101,120],"practical":[9],"non-Gaussian":[10,37],"and":[11,17,38,75,81,110],"non-stationary":[12],"underwater":[13,126],"ambient":[14,25],"noise":[15,26,32,94,123],"modeling":[16],"direction-finding":[18,50],"approach.":[19],"application,":[22],"measurement":[23],"of":[24,78,104,107],"in":[27,60,96,124],"natural":[28],"environment":[29,127],"shows":[30],"that":[31,51,115],"can":[33],"sometimes":[34],"be":[35],"significantly":[36,59],"time-varying":[39,76],"features":[40],"such":[41,48],"as":[42,49],"variances.":[43],"Therefore,":[44],"signal":[45],"processing":[46],"algorithms":[47],"are":[52,69],"optimized":[53],"Gaussian":[55],"noise,":[56],"may":[57],"degrade":[58],"environment.":[62],"Generalized":[63],"autoregressive":[64],"conditional":[65],"heteroscedasticity":[66],"(GARCH)":[67],"models":[68],"feasible":[70],"heavy":[72],"tailed":[73],"PDFs":[74],"variances":[77],"stochastic":[79],"process":[80],"also":[82],"has":[83],"flexible":[84],"forms.":[85],"We":[86],"use":[87],"more":[89],"realistic":[90],"GARCH":[91],"(1,1)":[92],"based":[93],"model":[95,117],"the":[97,102,121],"maximum":[98],"likelihood":[99],"approach":[100],"estimation":[103],"direction-of-arrivals":[105],"(DOAs)":[106],"impinging":[108],"sources":[109],"show":[111],"using":[112],"experimental":[113],"data":[114],"is":[118],"suitable":[119],"additive":[122],"an":[125]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2013,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
