{"id":"W1977213604","doi":"10.1117/12.488024","title":"&lt;title&gt;Nonrecursive and recursive methods for parameter estimation in filtering problems&lt;/title&gt;","year":2003,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Nonlinear system; Particle filter; Recursive least squares filter; Computer science; Recursive Bayesian estimation; Estimation theory; Recursive filter; Algorithm; Filter (signal processing); State (computer science); Identification (biology); Mathematical optimization; Mathematics; Applied mathematics; Control theory (sociology); Adaptive filter; Digital filter; Artificial intelligence; Bayesian probability","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":[0.001112239,0.001270975,0.00103689,0.0008719857,0.0003400524,0.001478702,0.001217182,0.001551404,0.03333135],"category_scores_gemma":[0.003493403,0.000348587,0.0006429943,0.001613391,0.001135889,0.001717501,0.0006173418,0.002147811,0.02575201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008863892,"about_ca_system_score_gemma":0.0004866479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002367811,"about_ca_topic_score_gemma":0.002277514,"domain_scores_codex":[0.9991623,0.0002356413,0.0000488909,0.000142446,0.000359489,0.00005133298],"domain_scores_gemma":[0.9984844,0.0006958061,0.0001130309,0.0002302262,0.0004320527,0.0000443935],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002594868,0.00009694623,0.0004260833,0.0006850228,0.00008845662,0.0003326653,0.0001394416,0.0533079,0.009057369,0.223532,0.2260587,0.486016],"study_design_scores_gemma":[0.00004545389,0.0001169161,0.0005828739,0.000206691,0.0000330344,0.0002932926,0.00003003783,0.5285336,0.008840388,0.1134029,0.3478263,0.00008851455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001253011,0.007405965,0.9616607,0.001564545,0.006542404,0.0001061648,0.0002355699,0.001482936,0.01974864],"genre_scores_gemma":[0.08352344,0.01811298,0.615609,0.002105989,0.009780247,0.0005497681,0.002135425,0.003677462,0.2645056],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03333135,"threshold_uncertainty_score":0.1115045,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01701685953948916,"score_gpt":0.2675750370779729,"score_spread":0.2505581775384838,"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."}}