{"id":"W2981753322","doi":"10.1109/taes.2019.2948451","title":"Expanding Window Dynamic-Programming-Based Track-Before-Detect With Order Statistics in Weibull Distributed Clutter","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Aerospace and Electronic Systems","topic":"Radar Systems and Signal Processing","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Clutter; Track-before-detect; Weibull distribution; Computer science; Sliding window protocol; Radar; Radar tracker; Window (computing); Constant false alarm rate; Algorithm; Dynamic programming; Track (disk drive); Artificial intelligence; Mathematics; Statistics","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":[0.0003569337,0.000565725,0.0006933325,0.0003057387,0.0002696372,0.0005576597,0.0007830607,0.0003818827,0.0006302546],"category_scores_gemma":[0.0009346374,0.000403962,0.0003908132,0.0005216959,0.0003270055,0.000716625,0.0004920803,0.0008190254,0.0001591645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003279212,"about_ca_system_score_gemma":0.0006603575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001784179,"about_ca_topic_score_gemma":0.001660271,"domain_scores_codex":[0.9997361,0.00004707763,0.00001183354,0.00007954105,0.00009198245,0.00003343411],"domain_scores_gemma":[0.9995566,0.0002999174,0.0000431663,0.00002467897,0.00005394795,0.00002167793],"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":[0.0003059114,0.0001227625,0.001461098,0.0001182422,0.0000695724,0.000206931,0.0001209963,0.5854731,0.03586178,0.008664984,0.00130979,0.3662848],"study_design_scores_gemma":[0.000004402095,0.00004792177,0.0001147783,0.000001557187,0.000005541648,0.00003971476,0.000005158856,0.9956108,0.003065898,0.0008543762,0.0002444476,0.000005460063],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01360689,0.0001276387,0.9856129,0.0000302826,0.00001034599,0.00001507057,0.00001230049,0.0001585436,0.0004259413],"genre_scores_gemma":[0.4459143,0.0003145115,0.5506277,0.0001125545,0.00003895021,0.0001070882,0.0001011407,0.00008288048,0.002700786],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001784179,"threshold_uncertainty_score":0.003547668,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003153403595961805,"score_gpt":0.1978653651410541,"score_spread":0.1947119615450923,"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."}}