{"id":"W3005649591","doi":"10.1016/j.isatra.2020.02.004","title":"Distributed fusion estimation for multisensor systems with non-Gaussian but heavy-tailed noises","year":2020,"lang":"en","type":"article","venue":"ISA Transactions","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China","keywords":"Filter (signal processing); Fusion; Kalman filter; Sensor fusion; Computer science; Algorithm; Gaussian; Generalization; Computation; Tracking (education); Multivariate normal distribution; Multivariate t-distribution; Bayesian probability; Extended Kalman filter; Mathematics; Artificial intelligence; Multivariate statistics; Machine learning; Computer vision","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008358747,0.0002089821,0.0002460032,0.00006207753,0.0004978423,0.0002689027,0.0003844802,0.0001073856,0.00002160831],"category_scores_gemma":[0.0000222538,0.0001748574,0.00009313595,0.0004610319,0.00004682149,0.0005073329,0.00001209765,0.0001891405,0.0000429755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003768816,"about_ca_system_score_gemma":0.00005586099,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001042239,"about_ca_topic_score_gemma":0.0000286224,"domain_scores_codex":[0.9985458,0.0000473198,0.0003153122,0.0005013085,0.000265507,0.0003247653],"domain_scores_gemma":[0.9989032,0.0002025222,0.000106439,0.0004139416,0.0001307324,0.0002431555],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002978625,0.0002469938,0.0001098887,0.0001844229,0.00008718228,0.00002836444,0.001311042,0.9617853,0.002459141,0.001201215,0.004628522,0.02766009],"study_design_scores_gemma":[0.001042131,0.0002117649,0.0002926112,0.00008691048,0.00004468829,0.00002789586,0.0001426219,0.9910085,0.001229197,0.00002161716,0.005632832,0.0002592133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003713887,0.00004619375,0.9909654,0.002932584,0.0005884882,0.0006497446,0.0005145826,0.0005269853,0.0000621342],"genre_scores_gemma":[0.9165594,0.00001620201,0.08272956,0.0001687598,0.0001302122,0.0001195872,0.0001825329,0.00002188499,0.00007184158],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9128456,"threshold_uncertainty_score":0.7130477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02065149463742155,"score_gpt":0.23596802034447,"score_spread":0.2153165257070485,"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."}}