{"id":"W3084797474","doi":"10.1109/tim.2020.3023213","title":"A Novel Multiple-Model Adaptive Kalman Filter for an Unknown Measurement Loss Probability","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Ministry of Land, Infrastructure, Transport and Tourism","keywords":"Kalman filter; Bernoulli's principle; Computer science; Algorithm; Bernoulli distribution; Bayesian probability; Filter (signal processing); Control theory (sociology); Random variable; Adaptive filter; Mathematics; Artificial intelligence; Statistics; Engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001341901,0.0008787573,0.001127744,0.0006458763,0.0006353708,0.0008999668,0.001949706,0.001259346,0.001698323],"category_scores_gemma":[0.003459729,0.0005235384,0.001073776,0.0008260856,0.0005613065,0.002434904,0.00101718,0.001915393,0.0007831264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007712003,"about_ca_system_score_gemma":0.001719258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009572396,"about_ca_topic_score_gemma":0.007244177,"domain_scores_codex":[0.9989333,0.0001424745,0.00005664867,0.0003574733,0.0004331078,0.00007701825],"domain_scores_gemma":[0.9993588,0.0002404698,0.00008010232,0.00007005383,0.0002293278,0.00002128183],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001449706,0.00007816604,0.00220223,0.0003044117,0.0001682728,0.0001768695,0.0002266309,0.4435407,0.01600166,0.04573458,0.004712894,0.4867087],"study_design_scores_gemma":[0.00001406006,0.00003306795,0.0002937375,0.00001568876,0.0000257182,0.00008166682,0.000007782019,0.9900025,0.001735495,0.003882122,0.003886089,0.00002209398],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006587169,0.00009793871,0.998765,0.00003197198,0.00002705915,0.000007763902,0.00001282104,0.0001371314,0.0002615612],"genre_scores_gemma":[0.2096311,0.001021174,0.7833772,0.000221586,0.0002117072,0.0002201752,0.0003813215,0.0001503495,0.004785465],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009572396,"threshold_uncertainty_score":0.01903331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1641456160188497,"score_gpt":0.2784194326160126,"score_spread":0.1142738165971629,"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."}}