{"id":"W4224128918","doi":"10.11591/eei.v11i2.3695","title":"Controlling the degree of observability in GPS/INS integration land-vehicle navigation based on extended Kalman filter","year":2022,"lang":"en","type":"article","venue":"Bulletin of Electrical Engineering and Informatics","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Inertial measurement unit; Global Positioning System; Extended Kalman filter; GPS/INS; Observability; Kalman filter; Computer science; Inertial navigation system; GNSS applications; Robustness (evolution); Assisted GPS; Computer vision; Artificial intelligence; Mathematics; Orientation (vector space)","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":[0.000540984,0.0005173136,0.0003637558,0.0002733013,0.0002908804,0.0004445653,0.0004198744,0.0003111514,0.001382479],"category_scores_gemma":[0.001731512,0.0001905268,0.0001932032,0.0002103926,0.0003157134,0.0005911029,0.00054539,0.0003241564,0.0001887762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003236126,"about_ca_system_score_gemma":0.000505975,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004790343,"about_ca_topic_score_gemma":0.005576775,"domain_scores_codex":[0.9994488,0.00008133181,0.00003397461,0.0001419009,0.000189397,0.0001045876],"domain_scores_gemma":[0.9992093,0.0002258719,0.00009950706,0.0001299647,0.0002843562,0.00005100376],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002794345,0.0007980381,0.03729299,0.0006008208,0.0001818436,0.0002798152,0.0007940798,0.3522311,0.3612061,0.003027456,0.001242238,0.2395511],"study_design_scores_gemma":[0.00009034011,0.00100078,0.02731347,0.00002515079,0.00008662219,0.00008348117,0.0001300617,0.8216029,0.1470457,0.0008236814,0.001752299,0.00004558287],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6680416,0.0001025389,0.3274026,0.00005561695,0.00006107547,0.0000951126,0.0001807228,0.001494383,0.002566295],"genre_scores_gemma":[0.9827754,0.00002312008,0.01653038,0.000007724259,0.000002475437,0.00004166057,0.00008379604,0.0000161346,0.0005193603],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004790343,"threshold_uncertainty_score":0.009524882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0116981867453571,"score_gpt":0.1924853985049804,"score_spread":0.1807872117596233,"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."}}