{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007814656,0.0001415195,0.000126786,0.0002565137,0.0001048151,0.0001786418,0.0005523406,0.00008258103,0.0004653464],"category_scores_gemma":[0.0001148998,0.0001271129,0.0000661676,0.0001438595,0.00002662276,0.003470102,0.0001685292,0.0001519913,0.0002769468],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001352452,"about_ca_system_score_gemma":0.00006327626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002530839,"about_ca_topic_score_gemma":0.00000294152,"domain_scores_codex":[0.997778,0.00006005769,0.0005966375,0.0001363331,0.001211072,0.0002179139],"domain_scores_gemma":[0.9983744,0.00003098597,0.0004208086,0.0003168039,0.0007665701,0.00009044649],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001770475,0.0003189337,0.01065991,0.00004223313,0.00007168962,8.79301e-7,0.005174254,0.0023402,0.008646609,0.6944252,0.005894667,0.2722484],"study_design_scores_gemma":[0.003371421,0.0004536963,0.05759568,0.001200217,0.00004174355,0.00005784376,0.0006793031,0.7774408,0.03431902,0.004560416,0.1191139,0.001165857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02851077,0.00002082263,0.925096,0.0008208498,0.004069298,0.0002523101,0.00002600505,0.000149602,0.04105433],"genre_scores_gemma":[0.9910389,0.00003024259,0.008296973,0.0003118771,0.000147462,0.00001464834,0.0001118104,0.000004424756,0.00004366619],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9625281,"threshold_uncertainty_score":0.5183515,"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."}}