{"id":"W2118603200","doi":"10.1109/jproc.2003.823149","title":"Probabilistic Data Association Techniques for Target Tracking in Clutter","year":2004,"lang":"en","type":"article","venue":"Proceedings of the IEEE","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":254,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Clutter; Estimator; Data association; Tracking (education); Probabilistic logic; Radar; Radar tracker; Artificial intelligence; Low probability of intercept radar; Tracking system; Sonar; Statistical model; Computer vision; Kalman filter; Mathematics; Statistics; Radar engineering details","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":[0.0009473453,0.0001006791,0.0001527105,0.00006288003,0.00008613885,0.0001196289,0.001978226,0.00009243759,0.000001016661],"category_scores_gemma":[0.0004880124,0.00007627122,0.00004518831,0.0003328099,0.0000248816,0.000771841,0.0003222643,0.0001691863,0.000001343077],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001183401,"about_ca_system_score_gemma":0.00003575825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002280626,"about_ca_topic_score_gemma":0.00001118828,"domain_scores_codex":[0.9988365,0.000005487727,0.0002930937,0.0003503644,0.0002692241,0.0002453901],"domain_scores_gemma":[0.9990703,0.0001034153,0.0002700768,0.0003387265,0.0001927617,0.00002468033],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002680146,0.00240091,0.1173963,0.002196582,0.0002268497,0.00000526855,0.01055763,0.009000164,0.1386664,0.2518583,0.3554347,0.1119889],"study_design_scores_gemma":[0.002457744,0.0002852016,0.01521501,0.00158729,0.00006949213,0.00002985121,0.0001678616,0.07993346,0.3947716,0.4668357,0.03753464,0.001112082],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5705777,0.0003028238,0.3873944,0.02578607,0.004499203,0.004817843,0.0002790042,0.001579689,0.004763272],"genre_scores_gemma":[0.8474545,0.000007669017,0.1519423,0.0002693394,0.0001835867,0.00003626741,0.000005124887,0.00001265688,0.00008852653],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3179,"threshold_uncertainty_score":0.3676068,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03054598795343635,"score_gpt":0.2737829069069535,"score_spread":0.2432369189535172,"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."}}