{"id":"W2114457006","doi":"10.1109/taes.2004.1292138","title":"GPS navigation with increased immunity to modeling errors","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Aerospace and Electronic Systems","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Extended Kalman filter; Global Positioning System; Kalman filter; Computer science; Control theory (sociology); Invariant extended Kalman filter; Robustness (evolution); Artificial intelligence; Telecommunications; Control (management)","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":[0.0004633474,0.0005456515,0.0005529968,0.0002650319,0.0001860969,0.0004927877,0.0005923903,0.0009508722,0.0005502892],"category_scores_gemma":[0.003194819,0.0003037523,0.0005779169,0.0003719821,0.0002893575,0.001015478,0.001000874,0.0008898278,0.0004910954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002022927,"about_ca_system_score_gemma":0.0003898957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001972731,"about_ca_topic_score_gemma":0.0013546,"domain_scores_codex":[0.9995232,0.0001261641,0.00002503043,0.00009433216,0.0001867816,0.00004446448],"domain_scores_gemma":[0.9992068,0.0002836394,0.00009764697,0.0001860927,0.0002053072,0.00002047389],"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":[0.0002448005,0.00004238023,0.002331469,0.000131178,0.00009138683,0.0003389863,0.00009740925,0.7654541,0.04113647,0.01636924,0.001832584,0.1719301],"study_design_scores_gemma":[0.00002822953,0.0001453504,0.00075898,0.00001122848,0.00003461486,0.0001900956,0.000006745951,0.9868649,0.006350754,0.002580265,0.003004561,0.00002430935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02143,0.000252474,0.9759404,0.000146357,0.0000863213,0.00001243602,0.00003857583,0.0006681143,0.001425273],"genre_scores_gemma":[0.8694775,0.0004085223,0.1270086,0.0000989252,0.0001453588,0.00004936395,0.0001962049,0.00006958266,0.002545997],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001972731,"threshold_uncertainty_score":0.003922522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01110825911333453,"score_gpt":0.2227621730893657,"score_spread":0.2116539139760311,"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."}}