{"id":"W2338595933","doi":"10.1109/tac.2016.2557999","title":"Linear Optimal Unbiased Filter for Time-Variant Systems Without Apriori Information on Initial Conditions","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Automatic Control","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Education and Child Care; University of Alberta","funders":"","keywords":"Kalman filter; A priori and a posteriori; Mathematics; Realization (probability); Control theory (sociology); Computation; Filter (signal processing); State (computer science); Transient (computer programming); Mathematical optimization; Computer science; Algorithm; Statistics; Artificial intelligence; Control (management)","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003443226,0.0002538102,0.0003398691,0.0002676824,0.0003770426,0.0002376504,0.0004541258,0.0001521836,0.0001847552],"category_scores_gemma":[0.00004347124,0.0001833349,0.0001721836,0.0001984201,0.00006009743,0.0009401804,0.000002400978,0.0001374886,0.0009383885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008696396,"about_ca_system_score_gemma":0.0000998259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001206952,"about_ca_topic_score_gemma":0.00000180852,"domain_scores_codex":[0.9981683,0.0001406961,0.000610475,0.000316287,0.0003837575,0.0003804677],"domain_scores_gemma":[0.9975649,0.001185325,0.00021204,0.0007021255,0.0001794205,0.0001561582],"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":[0.0009997536,0.001496785,0.000008798395,0.0002683309,0.0008496959,0.00005065395,0.001695555,0.4894413,0.005886411,0.01665524,0.02559949,0.4570479],"study_design_scores_gemma":[0.004243092,0.0004102891,0.00004091559,0.0002453255,0.00005939422,0.00003822888,0.00001480464,0.9900361,0.001102334,0.00008258146,0.003446993,0.0002799214],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003447261,0.000003653707,0.9907224,0.001137094,0.001884022,0.001026796,0.0008483496,0.000731422,0.000199029],"genre_scores_gemma":[0.9842238,0.000002845202,0.01404912,0.0007622737,0.000161963,0.0004045388,0.00002586298,0.0000202929,0.0003492439],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9807766,"threshold_uncertainty_score":0.9998395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01406799202381938,"score_gpt":0.2546531407507468,"score_spread":0.2405851487269274,"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."}}