{"id":"W2530014764","doi":"10.5539/mas.v11n1p62","title":"GPS/INS/Odometer Data Fusion for Land Vehicle Localization in GPS Denied Environment","year":2016,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Odometer; Global Positioning System; GPS/INS; GPS signals; Computer science; Inertial navigation system; Kalman filter; Real-time computing; Sensor fusion; Inertial measurement unit; Precision Lightweight GPS Receiver; Assisted GPS; Remote sensing; Artificial intelligence; Inertial frame of reference; Geography; Telecommunications","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003203404,0.0003358662,0.0003807754,0.0003250774,0.0002354753,0.0004035065,0.0002533172,0.0003214234,0.0007274006],"category_scores_gemma":[0.0006495444,0.0001447013,0.0002245041,0.0004904199,0.0002801124,0.0007504323,0.0004874246,0.0003315639,0.0002640195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002624222,"about_ca_system_score_gemma":0.0003297249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003229667,"about_ca_topic_score_gemma":0.002193175,"domain_scores_codex":[0.9997875,0.00007188984,0.00001041736,0.00002771214,0.00007920442,0.00002326164],"domain_scores_gemma":[0.999843,0.00005332789,0.00001812063,0.00002617376,0.00005273381,0.000006667071],"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":[0.0005213314,0.00007478596,0.005438999,0.0002935384,0.00009881289,0.000317544,0.0002283573,0.6629614,0.07355058,0.006666319,0.001272757,0.2485755],"study_design_scores_gemma":[0.00001346604,0.0000942613,0.001603869,0.00001073574,0.00002745526,0.00006470821,0.00004945761,0.980647,0.0141231,0.001740237,0.001610002,0.00001576404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09524401,0.000731879,0.9015794,0.0001556346,0.00006534834,0.00002265411,0.00007472758,0.0008564129,0.001269876],"genre_scores_gemma":[0.942075,0.000316631,0.05686537,0.00001765387,0.00001893977,0.00001612968,0.0001118016,0.00002474689,0.0005537029],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003229667,"threshold_uncertainty_score":0.006421745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02221823061029459,"score_gpt":0.2256718574259433,"score_spread":0.2034536268156487,"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."}}