{"id":"W2588988797","doi":"","title":"Real-Time Implementation of INS/GPS Data Fusion Utilizing Adaptive Neuro-Fuzzy Inference system","year":2005,"lang":"en","type":"article","venue":"Proceedings of the 2005 National Technical Meeting of The Institute of Navigation","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Global Positioning System; Adaptive neuro fuzzy inference system; GPS/INS; Computer science; Inertial navigation system; Kalman filter; Fuzzy logic; Mean squared error; Position (finance); Sensor fusion; Artificial intelligence; Control theory (sociology); Real-time computing; Computer vision; Assisted GPS; Fuzzy control system; Orientation (vector space); Mathematics; 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":[0.0003245302,0.0003573355,0.0003294026,0.0003070028,0.0003141566,0.0004058271,0.0005586096,0.0004046174,0.001128123],"category_scores_gemma":[0.0005507255,0.0001677242,0.000310564,0.0001901803,0.0001497981,0.0004187188,0.0003892627,0.0003591406,0.000389164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002846332,"about_ca_system_score_gemma":0.0004962286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005285098,"about_ca_topic_score_gemma":0.003675747,"domain_scores_codex":[0.9997364,0.00002657955,0.00002505531,0.0000564446,0.0001257313,0.00002987148],"domain_scores_gemma":[0.9998605,0.00002183078,0.00001485067,0.00001749728,0.00007788323,0.00000741522],"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":[0.0006223522,0.0002047063,0.004005777,0.0002720871,0.00009350006,0.0005453143,0.0004661719,0.2791023,0.160286,0.004792562,0.002638869,0.5469704],"study_design_scores_gemma":[0.00002300939,0.0001366489,0.001534626,0.00001532507,0.00002708636,0.0000983602,0.00004065004,0.9616091,0.0325746,0.0007083035,0.003211546,0.00002067083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0777463,0.0002783487,0.9124413,0.0001243828,0.0001550094,0.00008920237,0.00007266045,0.002976671,0.006116223],"genre_scores_gemma":[0.8977558,0.0001565667,0.09937999,0.00004193738,0.00002434438,0.00006218928,0.0001017699,0.00002608254,0.002451309],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005285098,"threshold_uncertainty_score":0.01050866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02818264755276251,"score_gpt":0.2887157628373018,"score_spread":0.2605331152845393,"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."}}