{"id":"W2902715633","doi":"10.23919/ecc.2018.8550441","title":"Constrained Extended Kalman Filter based on Kullback-Leibler (KL) Divergence","year":2018,"lang":"en","type":"article","venue":"","topic":"Distributed Sensor Networks and Detection Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Kalman filter; Kullback–Leibler divergence; Divergence (linguistics); Extended Kalman filter; Fast Kalman filter; Ensemble Kalman filter; Computer science; Invariant extended Kalman filter; Mathematics; Artificial intelligence","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":["insufficient_payload"],"category_scores_codex":[0.0001752829,0.0001892037,0.0001520479,0.0001089606,0.0002397169,0.0001883574,0.000615694,0.00008469122,0.002076394],"category_scores_gemma":[0.00003205691,0.000154626,0.00009316466,0.0005731519,0.0001664622,0.0002234447,0.0001201371,0.0001314776,0.001108006],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003167167,"about_ca_system_score_gemma":0.00004901959,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001938675,"about_ca_topic_score_gemma":0.00001227193,"domain_scores_codex":[0.9984365,0.00006022705,0.0002251038,0.000526665,0.0003561225,0.0003954475],"domain_scores_gemma":[0.9987583,0.00008562428,0.00006759969,0.0007105565,0.0001876398,0.0001902695],"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":[0.0002024269,0.001017979,0.0006357728,0.0000219304,0.0001127111,0.0002074504,0.0002788006,0.004064688,0.002028424,0.1622833,0.2740581,0.5550885],"study_design_scores_gemma":[0.0006727377,0.0005320599,0.001768177,0.00001796557,0.000004891614,0.00001366417,0.00001540203,0.9475897,0.006192095,0.001593905,0.04126417,0.0003352339],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001679054,0.000006070283,0.9282761,0.0008384362,0.001310917,0.000138084,0.00001178384,0.0004657375,0.06727389],"genre_scores_gemma":[0.9474159,0.000002876307,0.04573271,0.003849308,0.0002968521,0.00001062601,0.000008432545,0.000010821,0.002672503],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9457368,"threshold_uncertainty_score":0.9996697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01618010005125496,"score_gpt":0.2403860738107049,"score_spread":0.22420597375945,"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."}}