{"id":"W2143070938","doi":"10.1109/mwscas.2005.1594407","title":"Fuzzy logic based particle filter for tracking a maneuverable target","year":2005,"lang":"en","type":"article","venue":"","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Control theory (sociology); Fuzzy logic; Tracking (education); Particle filter; Trajectory; Nonlinear system; Filter (signal processing); Computer science; Constant (computer programming); Hidden Markov model; Algorithm; Mathematics; Artificial intelligence; Computer vision; Physics","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":[0.0003412629,0.0001472294,0.0001533027,0.00004370202,0.0002176742,0.0002583616,0.0006344148,0.00007388255,0.0003114667],"category_scores_gemma":[0.00004663479,0.000122633,0.00009867588,0.0002055513,0.00002439585,0.0005869424,0.00008771933,0.0001101142,0.0001846736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002566916,"about_ca_system_score_gemma":0.00002821475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008396273,"about_ca_topic_score_gemma":0.000009057421,"domain_scores_codex":[0.9985747,0.00004040924,0.0002593289,0.0004416041,0.0001983038,0.0004856571],"domain_scores_gemma":[0.9989098,0.0002361542,0.00005756206,0.0005940273,0.00007959982,0.0001228243],"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":[0.00009045743,0.0007164451,0.002145868,0.00005970283,0.00003683969,0.00003435818,0.0005237305,0.1441083,0.004067475,0.3127276,0.2920074,0.2434818],"study_design_scores_gemma":[0.0006516174,0.00007290317,0.0002655537,0.00001108362,0.000004380209,0.00000737401,0.000009945359,0.8164999,0.01224991,0.00577058,0.1642246,0.0002321799],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002779774,0.000161244,0.9846404,0.004920331,0.0003580704,0.0002220583,0.00001021175,0.0005011307,0.006406759],"genre_scores_gemma":[0.5539926,0.000002932941,0.4406069,0.004030463,0.0001986821,0.00002812978,0.00001124809,0.000009876711,0.001119125],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6723917,"threshold_uncertainty_score":0.500083,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03702491213815411,"score_gpt":0.2644267103678011,"score_spread":0.227401798229647,"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."}}