{"id":"https://openalex.org/W2890824287","doi":"https://doi.org/10.23919/eusipco.2018.8553536","title":"Range Estimation from Single-Photon Lidar Data Using a Stochastic Em Approach","display_name":"Range Estimation from Single-Photon Lidar Data Using a Stochastic Em Approach","publication_year":2018,"publication_date":"2018-09-01","ids":{"openalex":"https://openalex.org/W2890824287","doi":"https://doi.org/10.23919/eusipco.2018.8553536","mag":"2890824287"},"language":"en","primary_location":{"id":"doi:10.23919/eusipco.2018.8553536","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco.2018.8553536","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 26th European Signal Processing Conference (EUSIPCO)","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/A5067973917","display_name":"Yoann Altmann","orcid":"https://orcid.org/0000-0002-3177-9884"},"institutions":[{"id":"https://openalex.org/I32062511","display_name":"Heriot-Watt University","ror":"https://ror.org/04mghma93","country_code":"GB","type":"education","lineage":["https://openalex.org/I32062511"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Yoann Altmann","raw_affiliation_strings":["School of Engineering and Physical Sciences, Heriot Watt University, Edinburgh, UK"],"affiliations":[{"raw_affiliation_string":"School of Engineering and Physical Sciences, Heriot Watt University, Edinburgh, UK","institution_ids":["https://openalex.org/I32062511"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010538525","display_name":"Stephen McLaughlin","orcid":"https://orcid.org/0000-0002-9558-8294"},"institutions":[{"id":"https://openalex.org/I32062511","display_name":"Heriot-Watt University","ror":"https://ror.org/04mghma93","country_code":"GB","type":"education","lineage":["https://openalex.org/I32062511"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Stephen McLaughlin","raw_affiliation_strings":["School of Engineering and Physical Sciences, Heriot Watt University, Edinburgh, UK"],"affiliations":[{"raw_affiliation_string":"School of Engineering and Physical Sciences, Heriot Watt University, Edinburgh, UK","institution_ids":["https://openalex.org/I32062511"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5067973917"],"corresponding_institution_ids":["https://openalex.org/I32062511"],"apc_list":null,"apc_paid":null,"fwci":6.8887,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.96371126,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1112","last_page":"1116"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12153","display_name":"Advanced Optical Sensing Technologies","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/3105","display_name":"Instrumentation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12153","display_name":"Advanced Optical Sensing Technologies","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/3105","display_name":"Instrumentation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.991100013256073,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10540","display_name":"Advanced Fluorescence Microscopy Techniques","score":0.9771000146865845,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.6910253763198853},{"id":"https://openalex.org/keywords/maximum-a-posteriori-estimation","display_name":"Maximum a posteriori estimation","score":0.6890888810157776},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.6269313097000122},{"id":"https://openalex.org/keywords/photon","display_name":"Photon","score":0.5721973776817322},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5522267818450928},{"id":"https://openalex.org/keywords/bayes-estimator","display_name":"Bayes estimator","score":0.5146077871322632},{"id":"https://openalex.org/keywords/nuisance-parameter","display_name":"Nuisance parameter","score":0.49574145674705505},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.479727178812027},{"id":"https://openalex.org/keywords/estimation-theory","display_name":"Estimation theory","score":0.47742781043052673},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4495472013950348},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.43570807576179504},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.416475385427475},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3881019949913025},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.2893866002559662},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.25966906547546387},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2087118923664093},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.19633802771568298},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.16365104913711548},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12116625905036926}],"concepts":[{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.6910253763198853},{"id":"https://openalex.org/C9810830","wikidata":"https://www.wikidata.org/wiki/Q635384","display_name":"Maximum a posteriori estimation","level":3,"score":0.6890888810157776},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.6269313097000122},{"id":"https://openalex.org/C159317903","wikidata":"https://www.wikidata.org/wiki/Q3198","display_name":"Photon","level":2,"score":0.5721973776817322},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5522267818450928},{"id":"https://openalex.org/C68022304","wikidata":"https://www.wikidata.org/wiki/Q842217","display_name":"Bayes estimator","level":3,"score":0.5146077871322632},{"id":"https://openalex.org/C194531419","wikidata":"https://www.wikidata.org/wiki/Q17104825","display_name":"Nuisance parameter","level":3,"score":0.49574145674705505},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.479727178812027},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.47742781043052673},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4495472013950348},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.43570807576179504},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.416475385427475},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3881019949913025},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.2893866002559662},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25966906547546387},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2087118923664093},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.19633802771568298},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.16365104913711548},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12116625905036926},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/eusipco.2018.8553536","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco.2018.8553536","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 26th European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.5199999809265137,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W851256203","https://openalex.org/W1852016712","https://openalex.org/W2005089986","https://openalex.org/W2049633694","https://openalex.org/W2050892949","https://openalex.org/W2076364662","https://openalex.org/W2076914453","https://openalex.org/W2103559027","https://openalex.org/W2148791483","https://openalex.org/W2156805234","https://openalex.org/W2539233672","https://openalex.org/W2749389153","https://openalex.org/W2788324256","https://openalex.org/W2963260018","https://openalex.org/W2964223102","https://openalex.org/W3102929960","https://openalex.org/W3106237033","https://openalex.org/W3121742466","https://openalex.org/W3121908085"],"related_works":["https://openalex.org/W2361668087","https://openalex.org/W2064991026","https://openalex.org/W2963177712","https://openalex.org/W2908991466","https://openalex.org/W2886634649","https://openalex.org/W2968949765","https://openalex.org/W4300058318","https://openalex.org/W2015071961","https://openalex.org/W1977863502","https://openalex.org/W2151525783"],"abstract_inverted_index":{"This":[0],"paper":[1],"addresses":[2],"the":[3,13,28,38,56,61,69,74,99,112],"problem":[4],"of":[5,30,64,105],"estimating":[6],"range":[7,62,88,117],"profiles":[8],"from":[9,68],"single-photon":[10],"waveforms":[11],"in":[12,120],"photon-starved":[14],"regime,":[15],"with":[16,108,123],"a":[17,24,43,48,83],"background":[18],"illumination":[19],"both":[20],"high":[21],"and":[22,51],"unknown":[23,57],"priori":[25],"such":[26],"that":[27],"influence":[29],"nuisance":[31,80,102],"photons":[32,128],"cannot":[33],"be":[34],"neglected.":[35],"We":[36],"reformulate":[37],"classical":[39],"observation":[40],"model":[41,58,71,76],"into":[42],"new":[44],"mixture":[45],"model,":[46],"adopt":[47],"Bayesian":[49,70],"approach":[50],"assign":[52],"prior":[53],"distributions":[54],"to":[55,72,114],"parameters.":[59,103],"First,":[60],"profile":[63,89],"interest":[65],"is":[66,90],"marginalised":[67],"estimate":[73],"remaining":[75],"parameters":[77],"(considered":[78],"as":[79],"parameters)":[81],"using":[82],"stochastic":[84],"EM":[85],"algorithm.":[86],"The":[87],"then":[91],"estimated":[92,101],"via":[93],"Monte":[94],"Carlo":[95],"simulation,":[96],"conditioned":[97],"on":[98,131],"previously":[100],"Results":[104],"simulations":[106],"conducted":[107],"controlled":[109],"data":[110],"demonstrate":[111],"possibility":[113],"maintain":[115],"satisfactory":[116],"estimation":[118],"performance":[119],"high-background":[121],"scenarios":[122],"less":[124],"than":[125],"10":[126],"signal":[127],"per":[129],"pixel":[130],"average.":[132]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}