{"id":"W2102540630","doi":"10.1109/acc.2005.1469955","title":"An adaptive GLR estimator for state estimation of a maneuvering target","year":2005,"lang":"en","type":"article","venue":"","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Estimator; Adaptive estimator; Covariance; Control theory (sociology); Probabilistic logic; Computer science; Estimation theory; State (computer science); Mathematics; Algorithm; Mathematical optimization; Statistics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008509438,0.000533686,0.0006861858,0.000419106,0.0001712342,0.0003899271,0.0009733518,0.0006595496,0.001289477],"category_scores_gemma":[0.003586926,0.0002874638,0.0005282913,0.0004217586,0.0004103415,0.001111114,0.0008522872,0.0009029988,0.001085798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000208038,"about_ca_system_score_gemma":0.0004087045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008664862,"about_ca_topic_score_gemma":0.0009386377,"domain_scores_codex":[0.999324,0.0002067725,0.00003313009,0.0001468231,0.0002565006,0.00003288491],"domain_scores_gemma":[0.9992182,0.0003718388,0.0001061235,0.0001168585,0.0001672359,0.00001965435],"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":[0.0002109454,0.00009231725,0.002202624,0.0002250113,0.000121531,0.0001925654,0.0001376056,0.3015229,0.06228946,0.01574172,0.002960561,0.6143026],"study_design_scores_gemma":[0.00001683025,0.0001234421,0.0005734719,0.000009033636,0.00002448852,0.0001579912,0.00001027951,0.9837041,0.008832522,0.003491637,0.003030555,0.00002561099],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003026898,0.0000930705,0.9960338,0.00002975447,0.00001763783,0.000009531882,0.00001565215,0.0004716195,0.0003018891],"genre_scores_gemma":[0.253959,0.0003131323,0.7431441,0.0001772694,0.0001058596,0.0000759153,0.0002362159,0.0002080148,0.001780509],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001289477,"threshold_uncertainty_score":0.00450027,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01594285130945756,"score_gpt":0.2676208596088835,"score_spread":0.251678008299426,"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."}}