{"id":"W3028365456","doi":"10.1016/j.sigpro.2020.107659","title":"Robust sensor fusion with heavy-tailed noises","year":2020,"lang":"en","type":"article","venue":"Signal Processing","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Fundamental Research Funds for the Key Research Program of Chongqing Science and Technology Commission; Chongqing Science and Technology Commission; National Natural Science Foundation of China","keywords":"Outlier; Sensor fusion; State space; Fusion; Bernoulli's principle; Gaussian; Bayesian probability; State (computer science); Computer science; Algorithm; Artificial intelligence; Gaussian process; State-space representation; Data mining; Pattern recognition (psychology); Mathematics; Engineering; Statistics; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003996298,0.001599847,0.002082327,0.0009135951,0.0007250475,0.001575844,0.001458062,0.002616328,0.0008387332],"category_scores_gemma":[0.01570375,0.0009988234,0.0008663124,0.00199245,0.002296228,0.003567991,0.002704966,0.002541321,0.0006537374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008406478,"about_ca_system_score_gemma":0.001144742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00177765,"about_ca_topic_score_gemma":0.002018705,"domain_scores_codex":[0.99575,0.001005917,0.0002197782,0.001017535,0.00166634,0.0003404381],"domain_scores_gemma":[0.9940934,0.003442207,0.0005997702,0.0009143498,0.000843127,0.0001071551],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008155478,0.0001114724,0.001088802,0.0004004862,0.0002921112,0.0003554276,0.0002039132,0.8032276,0.02762132,0.04066256,0.002768605,0.1224521],"study_design_scores_gemma":[0.0000122489,0.00004548731,0.0003789496,0.00001199964,0.00002256499,0.00008134224,0.00001399469,0.9794781,0.004536991,0.01483028,0.0005671412,0.00002092537],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00773389,0.0004583603,0.990677,0.0001613607,0.00009809875,0.00001377621,0.00004656752,0.0001874262,0.0006235438],"genre_scores_gemma":[0.8255683,0.002026229,0.1627462,0.0004690652,0.000650265,0.0001110536,0.0005580385,0.0001756762,0.007695215],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003996298,"threshold_uncertainty_score":0.02113467,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03703339537655507,"score_gpt":0.2252119263826936,"score_spread":0.1881785310061385,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}