{"id":"W2173157718","doi":"10.1049/iet-rsn.2015.0169","title":"Improved multi‐target multi‐Bernoulli filter with modelling of spurious targets","year":2015,"lang":"en","type":"article","venue":"IET Radar Sonar & Navigation","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada; McMaster University","funders":"","keywords":"Spurious relationship; Filter (signal processing); Cardinality (data modeling); Bernoulli's principle; Algorithm; Computer science; Set (abstract data type); Process (computing); Bernoulli distribution; Joint probability distribution; Mathematics; Random variable; Data mining; Statistics; Engineering; Machine learning; 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":[0.002790133,0.0006599689,0.000983722,0.0007068382,0.0004521133,0.0008160305,0.001337247,0.00139933,0.001046168],"category_scores_gemma":[0.006967005,0.0004592547,0.0007730541,0.0007389526,0.0005818723,0.001781849,0.001062221,0.001293029,0.0004900427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006957173,"about_ca_system_score_gemma":0.001345356,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003176587,"about_ca_topic_score_gemma":0.002797689,"domain_scores_codex":[0.9986469,0.0003237372,0.00006828793,0.0002757547,0.0005615181,0.0001237318],"domain_scores_gemma":[0.997632,0.001178896,0.0002552586,0.0002920206,0.0005670769,0.0000747481],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005620327,0.000110197,0.003578678,0.0002239572,0.0001584479,0.000175096,0.0002711591,0.6223696,0.04164323,0.04096103,0.001685082,0.2882614],"study_design_scores_gemma":[0.00001053834,0.00004101075,0.0005213126,0.000008630763,0.00001456736,0.00006107856,0.000005272378,0.9920492,0.00436099,0.002026296,0.0008836,0.00001758003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006987907,0.0001064194,0.9923393,0.00006004484,0.00002906222,0.000009551439,0.000019874,0.0001045227,0.0003433598],"genre_scores_gemma":[0.322489,0.0004214544,0.6720179,0.0002371995,0.0001189741,0.0001395983,0.0002988944,0.00009348621,0.004183525],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003176587,"threshold_uncertainty_score":0.01475585,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04556271080110263,"score_gpt":0.2516237068758672,"score_spread":0.2060609960747646,"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."}}