{"id":"W43427029","doi":"","title":"A sequential tracking filter without requirement of measurement decorrelation","year":2012,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Decorrelation; Covariance; Sequential estimation; Computer science; Filter (signal processing); Covariance matrix; Algorithm; Measurement uncertainty; Tracking (education); Nonlinear system; Observational error; Correlation coefficient; Mathematics; Statistics; Computer vision; Machine learning","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":[0.0009067363,0.0007937957,0.0008795275,0.0006207009,0.0006076738,0.000641894,0.0007899887,0.001274498,0.002357206],"category_scores_gemma":[0.001666509,0.0004564075,0.0007648002,0.0009504058,0.0003516261,0.001160134,0.0008016011,0.0009498753,0.001292986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003755034,"about_ca_system_score_gemma":0.001265718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00288922,"about_ca_topic_score_gemma":0.003501462,"domain_scores_codex":[0.9990294,0.00008862852,0.00005630606,0.0002978264,0.0004648947,0.00006290291],"domain_scores_gemma":[0.999422,0.000136758,0.00006689622,0.000121773,0.0002262816,0.00002644176],"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":[0.0005637407,0.0001658989,0.001090438,0.0003085556,0.0001781245,0.0002033925,0.0001347577,0.05403088,0.1725806,0.01615055,0.004970234,0.7496228],"study_design_scores_gemma":[0.0001082932,0.0004550466,0.001853559,0.00003072011,0.0001484071,0.0008335332,0.00001997342,0.9121127,0.0601111,0.004683248,0.01955982,0.00008366511],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002902996,0.00009660592,0.9959312,0.00003783976,0.0000824165,0.00002375219,0.00003112617,0.0002903663,0.0006037862],"genre_scores_gemma":[0.1339298,0.0003641535,0.8591283,0.0002037574,0.0001961907,0.0002019147,0.0002526517,0.00007182461,0.005651375],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00288922,"threshold_uncertainty_score":0.007885695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09508718448114455,"score_gpt":0.3025976529917184,"score_spread":0.2075104685105738,"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."}}