{"id":"https://openalex.org/W2902421750","doi":"https://doi.org/10.1109/icacci.2018.8554415","title":"A performance study of Levenberg \u2014 Marquardt (LM) Algorithm in Echo Estimation","display_name":"A performance study of Levenberg \u2014 Marquardt (LM) Algorithm in Echo Estimation","publication_year":2018,"publication_date":"2018-09-01","ids":{"openalex":"https://openalex.org/W2902421750","doi":"https://doi.org/10.1109/icacci.2018.8554415","mag":"2902421750"},"language":"en","primary_location":{"id":"doi:10.1109/icacci.2018.8554415","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icacci.2018.8554415","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI)","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/A5082060797","display_name":"Sarath Sivaprasad","orcid":null},"institutions":[{"id":"https://openalex.org/I81556334","display_name":"Amrita Vishwa Vidyapeetham","ror":"https://ror.org/03am10p12","country_code":"IN","type":"education","lineage":["https://openalex.org/I81556334"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"S Sivaprasad","raw_affiliation_strings":["Department of Electronics and Communication Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, India"],"affiliations":[{"raw_affiliation_string":"Department of Electronics and Communication Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, India","institution_ids":["https://openalex.org/I81556334"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040767675","display_name":"Prasanth M. Warrier","orcid":"https://orcid.org/0000-0002-7899-9358"},"institutions":[{"id":"https://openalex.org/I81556334","display_name":"Amrita Vishwa Vidyapeetham","ror":"https://ror.org/03am10p12","country_code":"IN","type":"education","lineage":["https://openalex.org/I81556334"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Prasanth M. Warrier","raw_affiliation_strings":["Department of Electronics and Communication Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, India"],"affiliations":[{"raw_affiliation_string":"Department of Electronics and Communication Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, India","institution_ids":["https://openalex.org/I81556334"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5082060797"],"corresponding_institution_ids":["https://openalex.org/I81556334"],"apc_list":null,"apc_paid":null,"fwci":0.3303,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.58138301,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"820","last_page":"826"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9983000159263611,"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":0.9979000091552734,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/levenberg\u2013marquardt-algorithm","display_name":"Levenberg\u2013Marquardt algorithm","score":0.7295892238616943},{"id":"https://openalex.org/keywords/additive-white-gaussian-noise","display_name":"Additive white Gaussian noise","score":0.6165645718574524},{"id":"https://openalex.org/keywords/estimation-theory","display_name":"Estimation theory","score":0.5781530141830444},{"id":"https://openalex.org/keywords/maximum-likelihood-sequence-estimation","display_name":"Maximum likelihood sequence estimation","score":0.5710747838020325},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5489330291748047},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.5153152942657471},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.4840037524700165},{"id":"https://openalex.org/keywords/maximum-a-posteriori-estimation","display_name":"Maximum a posteriori estimation","score":0.46589553356170654},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4497700035572052},{"id":"https://openalex.org/keywords/moment","display_name":"Moment (physics)","score":0.4484010338783264},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.44390079379081726},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.4226447343826294},{"id":"https://openalex.org/keywords/likelihood-function","display_name":"Likelihood function","score":0.4223908483982086},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.41938644647598267},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.41619497537612915},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.41157156229019165},{"id":"https://openalex.org/keywords/white-noise","display_name":"White noise","score":0.332721471786499},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.30459854006767273},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.28715839982032776},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2631847858428955},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.14322546124458313},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.07185414433479309}],"concepts":[{"id":"https://openalex.org/C87578567","wikidata":"https://www.wikidata.org/wiki/Q1426494","display_name":"Levenberg\u2013Marquardt algorithm","level":3,"score":0.7295892238616943},{"id":"https://openalex.org/C169334058","wikidata":"https://www.wikidata.org/wiki/Q353292","display_name":"Additive white Gaussian noise","level":3,"score":0.6165645718574524},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.5781530141830444},{"id":"https://openalex.org/C191462741","wikidata":"https://www.wikidata.org/wiki/Q6795902","display_name":"Maximum likelihood sequence estimation","level":3,"score":0.5710747838020325},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5489330291748047},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.5153152942657471},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.4840037524700165},{"id":"https://openalex.org/C9810830","wikidata":"https://www.wikidata.org/wiki/Q635384","display_name":"Maximum a posteriori estimation","level":3,"score":0.46589553356170654},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4497700035572052},{"id":"https://openalex.org/C179254644","wikidata":"https://www.wikidata.org/wiki/Q13222844","display_name":"Moment (physics)","level":2,"score":0.4484010338783264},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.44390079379081726},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.4226447343826294},{"id":"https://openalex.org/C89106044","wikidata":"https://www.wikidata.org/wiki/Q45284","display_name":"Likelihood function","level":3,"score":0.4223908483982086},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.41938644647598267},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.41619497537612915},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.41157156229019165},{"id":"https://openalex.org/C112633086","wikidata":"https://www.wikidata.org/wiki/Q381287","display_name":"White noise","level":2,"score":0.332721471786499},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.30459854006767273},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.28715839982032776},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2631847858428955},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.14322546124458313},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.07185414433479309},{"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/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C74650414","wikidata":"https://www.wikidata.org/wiki/Q11397","display_name":"Classical mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icacci.2018.8554415","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icacci.2018.8554415","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4099999964237213,"display_name":"Life below water","id":"https://metadata.un.org/sdg/14"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W55295504","https://openalex.org/W90675431","https://openalex.org/W222645141","https://openalex.org/W1525535255","https://openalex.org/W1546727052","https://openalex.org/W1963481388","https://openalex.org/W2008278511","https://openalex.org/W2026073402","https://openalex.org/W2056215075","https://openalex.org/W2100271114","https://openalex.org/W2149647226","https://openalex.org/W2167323806","https://openalex.org/W2171074980","https://openalex.org/W2539193841","https://openalex.org/W2746249019","https://openalex.org/W2769012601","https://openalex.org/W4206180546","https://openalex.org/W4241758273"],"related_works":["https://openalex.org/W1967494390","https://openalex.org/W1503532423","https://openalex.org/W2149123792","https://openalex.org/W2019724159","https://openalex.org/W1965111750","https://openalex.org/W245717845","https://openalex.org/W2107666555","https://openalex.org/W2989299900","https://openalex.org/W2371672232","https://openalex.org/W1906819494"],"abstract_inverted_index":{"Estimation":[0,43,58],"of":[1,48,53,132],"backscattered":[2,18],"echoes":[3,19],"have":[4,147],"found":[5],"many":[6,37],"applications,":[7],"in":[8,123],"sonars,":[9],"radars,":[10],"medical":[11],"applications":[12],"etc.":[13,51],"For":[14],"the":[15,61,77,130,145],"estimation":[16,38,83],"purpose,":[17],"are":[20,31,36],"modeled":[21],"as":[22,118,120],"a":[23],"nonlinear":[24,33,95],"function,":[25],"using":[26,32],"Gaussian":[27,74],"model.":[28],"Since":[29,68],"we":[30],"function":[34],"there":[35],"techniques":[39],"like":[40],"Maximum":[41,56,85],"Likelihood":[42,57,86],"(MLE),":[44],"Bayes":[45],"Estimation,":[46],"Method":[47],"Moment":[49],"estimation,":[50],"Out":[52],"which":[54,124],"is":[55,60,70],"(MLE)":[59],"one":[62],"that":[63],"has":[64,91,112],"been":[65,92,113,148],"chosen":[66],"here.":[67],"it":[69,90],"added":[71],"with":[72,94,100,116,129],"White":[73],"Noise":[75],"(WGN)":[76],"problem":[78],"reduces":[79],"to":[80],"least":[81,96],"squares":[82],"from":[84],"Estimation.":[87],"In":[88],"this,":[89],"optimized":[93],"square":[97],"curve":[98],"fitting":[99],"Levenberg-":[101],"Marquardt":[102],"(LM)":[103],"technique":[104,111],"(Damped":[105],"Least":[106],"Square":[107],"Technique).":[108],"The":[109],"same":[110],"tried":[114],"along":[115],"noisy":[117],"well":[119],"de-noised":[121],"signal,":[122],"noise":[125],"removal":[126],"was":[127],"performed":[128],"help":[131],"Principal":[133],"Component":[134,138],"Analysis":[135,139],"(PCA),":[136],"Independent":[137],"(ICA)":[140],"and":[141,144],"Kalman":[142],"filter":[143],"results":[146],"compared.":[149]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
