{"id":"https://openalex.org/W7123467533","doi":"https://doi.org/10.1109/sdf67080.2025.11330848","title":"Robust Real-Time Multistatic Sonar Tracking with Missed Detections and Measurement Bias Using Gaussian Long Short-Term Memory Networks","display_name":"Robust Real-Time Multistatic Sonar Tracking with Missed Detections and Measurement Bias Using Gaussian Long Short-Term Memory Networks","publication_year":2025,"publication_date":"2025-11-24","ids":{"openalex":"https://openalex.org/W7123467533","doi":"https://doi.org/10.1109/sdf67080.2025.11330848"},"language":"en","primary_location":{"id":"doi:10.1109/sdf67080.2025.11330848","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sdf67080.2025.11330848","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE Sensor Data Fusion: Trends, Solutions, Applications (SDF)","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/A5122911281","display_name":"Thomas Alexander Beck","orcid":null},"institutions":[{"id":"https://openalex.org/I4210166245","display_name":"Fraunhofer Institute for Communication, Information Processing and Ergonomics","ror":"https://ror.org/05nn0gw40","country_code":"DE","type":"facility","lineage":["https://openalex.org/I4210166245","https://openalex.org/I4923324"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Thomas Alexander Beck","raw_affiliation_strings":["Fraunhofer Institute for Communication, Information Processing and Ergonomics FKIE,Dept. Sensor Data &#x0026; Information Fusion,Wachtberg,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fraunhofer Institute for Communication, Information Processing and Ergonomics FKIE,Dept. Sensor Data &#x0026; Information Fusion,Wachtberg,Germany","institution_ids":["https://openalex.org/I4210166245"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068628905","display_name":"Martina Brotje","orcid":null},"institutions":[{"id":"https://openalex.org/I4210166245","display_name":"Fraunhofer Institute for Communication, Information Processing and Ergonomics","ror":"https://ror.org/05nn0gw40","country_code":"DE","type":"facility","lineage":["https://openalex.org/I4210166245","https://openalex.org/I4923324"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Martina Br\u00f6tje","raw_affiliation_strings":["Fraunhofer Institute for Communication, Information Processing and Ergonomics FKIE,Dept. Sensor Data &#x0026; Information Fusion,Wachtberg,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fraunhofer Institute for Communication, Information Processing and Ergonomics FKIE,Dept. Sensor Data &#x0026; Information Fusion,Wachtberg,Germany","institution_ids":["https://openalex.org/I4210166245"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210166245"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.76226849,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.4828999936580658,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.4828999936580658,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.1720999926328659,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T11192","display_name":"Underwater Vehicles and Communication Systems","score":0.1298000067472458,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/gaussian","display_name":"Gaussian","score":0.6687999963760376},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6172000169754028},{"id":"https://openalex.org/keywords/sonar","display_name":"Sonar","score":0.546500027179718},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.5220000147819519},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.503600001335144},{"id":"https://openalex.org/keywords/cram\u00e9r\u2013rao-bound","display_name":"Cram\u00e9r\u2013Rao bound","score":0.476500004529953},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4438999891281128},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4099999964237213},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.4018999934196472},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.38440001010894775}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6704999804496765},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.6687999963760376},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6172000169754028},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5781000256538391},{"id":"https://openalex.org/C555745239","wikidata":"https://www.wikidata.org/wiki/Q133220","display_name":"Sonar","level":2,"score":0.546500027179718},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.5220000147819519},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.503600001335144},{"id":"https://openalex.org/C4978587","wikidata":"https://www.wikidata.org/wiki/Q1138810","display_name":"Cram\u00e9r\u2013Rao bound","level":3,"score":0.476500004529953},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4438999891281128},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4099999964237213},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.40290001034736633},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.4018999934196472},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.38440001010894775},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.37450000643730164},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3686999976634979},{"id":"https://openalex.org/C65892221","wikidata":"https://www.wikidata.org/wiki/Q1113935","display_name":"Gaussian filter","level":3,"score":0.36500000953674316},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.3278999924659729},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.32199999690055847},{"id":"https://openalex.org/C137209882","wikidata":"https://www.wikidata.org/wiki/Q1403517","display_name":"Measurement uncertainty","level":2,"score":0.3215000033378601},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.321399986743927},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.3102000057697296},{"id":"https://openalex.org/C137184094","wikidata":"https://www.wikidata.org/wiki/Q6764300","display_name":"Marine mammals and sonar","level":3,"score":0.3100000023841858},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3089999854564667},{"id":"https://openalex.org/C206833254","wikidata":"https://www.wikidata.org/wiki/Q5421817","display_name":"Extended Kalman filter","level":3,"score":0.3034000098705292},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.2874000072479248},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.2870999872684479},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.26669999957084656},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2655999958515167},{"id":"https://openalex.org/C32283439","wikidata":"https://www.wikidata.org/wiki/Q1407014","display_name":"Radar tracker","level":3,"score":0.26339998841285706},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.26170000433921814},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.26159998774528503},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.2540999948978424}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/sdf67080.2025.11330848","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sdf67080.2025.11330848","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE Sensor Data Fusion: Trends, Solutions, Applications (SDF)","raw_type":"proceedings-article"},{"id":"pmh:oai:publica.fraunhofer.de:publica/513837","is_oa":false,"landing_page_url":"https://publica.fraunhofer.de/handle/publica/513837","pdf_url":null,"source":{"id":"https://openalex.org/S4306400318","display_name":"Fraunhofer-Publica (Fraunhofer-Gesellschaft)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4923324","host_organization_name":"Fraunhofer-Gesellschaft","host_organization_lineage":["https://openalex.org/I4923324"],"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":"conference paper"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.756705105304718,"display_name":"Life below water","id":"https://metadata.un.org/sdg/14"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2052102540","https://openalex.org/W2123487311","https://openalex.org/W2158090912","https://openalex.org/W2803754083","https://openalex.org/W2947451847","https://openalex.org/W2949856406","https://openalex.org/W2963121817","https://openalex.org/W3136696980","https://openalex.org/W3183282730","https://openalex.org/W3194973558","https://openalex.org/W4288281758","https://openalex.org/W4292566877","https://openalex.org/W4383751510","https://openalex.org/W4386295352"],"related_works":[],"abstract_inverted_index":{"We":[0,37],"analyze":[1],"the":[2,40,46,70,73,76,88,149,158],"use":[3],"of":[4,75,87,161],"Long":[5],"Short-Term":[6],"Memory":[7],"(LSTM)":[8],"networks":[9,13,145],"and":[10,18,32,64,72,95,103,106,134],"Gaussian":[11,35,94,116,143],"LSTM":[12],"(G-LSTMs)":[14],"for":[15,45,52,170],"object":[16],"localization":[17],"tracking":[19],"based":[20],"on":[21,59],"bistatic":[22],"sonar":[23],"measurements":[24],"in":[25,140],"scenarios":[26],"involving":[27],"moving":[28],"receivers,":[29],"missing":[30,101],"detections,":[31],"potentially":[33],"biased":[34],"measurements.":[36],"formally":[38],"derive":[39],"Cram\u00e9r-Rao":[41],"Lower":[42],"Bound":[43],"(CRLB)":[44],"situation":[47],"including":[48],"incorporating":[49],"past":[50],"information":[51],"noisy":[53],"nonlinear":[54],"models.":[55,92],"Performance":[56],"is":[57,122],"analyzed":[58],"decaying":[60],"coordinated":[61],"turn":[62],"tracks":[63],"found":[65],"to":[66,69,109,151,168],"be":[67],"close":[68],"CRLB":[71],"performance":[74,121],"Extended":[77],"Kalman":[78],"Filter":[79],"(EKF),":[80],"despite":[81],"having":[82],"no":[83],"explicit":[84],"prior":[85],"knowledge":[86],"movement":[89],"or":[90],"measurement":[91,104],"The":[93,142],"non-Gaussian":[96],"LSTMs":[97],"exhibit":[98],"robustness":[99],"against":[100],"detections":[102],"bias":[105],"are":[107,165],"able":[108],"quantify":[110],"such":[111],"biases":[112],"(if":[113],"present).":[114],"This":[115],"architecture":[117],"shows":[118],"promise,":[119],"as":[120,155],"slightly":[123],"worse":[124],"with":[125],"their":[126],"nonGaussian":[127],"counterparts":[128],"but":[129],"offers":[130],"a":[131,137],"covariance":[132],"estimate":[133],"only":[135],"exhibits":[136],"slight":[138],"underconfidence":[139],"predictions.":[141],"neural":[144],"considered":[146],"here":[147],"show":[148],"ability":[150],"capture":[152],"environmental":[153],"uncertainty":[154],"given":[156],"by":[157],"dynamic":[159],"probability":[160],"detection.":[162],"Inference":[163],"times":[164],"small":[166],"enough":[167],"allow":[169],"real-time":[171],"tracking.":[172]},"counts_by_year":[],"updated_date":"2026-07-02T09:51:11.867554","created_date":"2026-01-14T00:00:00"}
