{"id":"W2739112826","doi":"10.1109/adconip.2017.7983842","title":"On initialization of the Kalman filter","year":2017,"lang":"en","type":"article","venue":"","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Initialization; Kalman filter; A priori and a posteriori; Variance (accounting); Computer science; Fast Kalman filter; Extended Kalman filter; Control theory (sociology); Filter (signal processing); Algorithm; Artificial intelligence; 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":[0.002058492,0.0008724906,0.0008448899,0.0006953219,0.0007221761,0.00155382,0.0008798728,0.001301458,0.002097544],"category_scores_gemma":[0.009541749,0.0005865736,0.0007243379,0.0007497608,0.001484522,0.002550243,0.00154003,0.002218147,0.001324292],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001108028,"about_ca_system_score_gemma":0.001250685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00292355,"about_ca_topic_score_gemma":0.001552338,"domain_scores_codex":[0.9982383,0.0006083232,0.00009237627,0.0004705918,0.0004750722,0.0001154178],"domain_scores_gemma":[0.9977196,0.001230766,0.0001843397,0.000347529,0.00047413,0.00004368455],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002215509,0.00003147372,0.001248491,0.0002320179,0.0000500338,0.0001689292,0.0003547884,0.4466345,0.009881771,0.3903501,0.002920562,0.1479058],"study_design_scores_gemma":[0.00001739078,0.00006500504,0.0004813477,0.00009969164,0.0000231728,0.0001095996,0.00002995856,0.9168833,0.007301161,0.06670466,0.008229083,0.00005553328],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001293014,0.0002171021,0.9966582,0.00006748326,0.00004873247,0.000009244046,0.00001360221,0.0001062211,0.001586427],"genre_scores_gemma":[0.4487669,0.00266691,0.5392699,0.0003213475,0.0004633957,0.0002442636,0.0003598413,0.0003780355,0.00752941],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00292355,"threshold_uncertainty_score":0.01088649,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02825980417880534,"score_gpt":0.2665393315360026,"score_spread":0.2382795273571972,"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."}}