{"id":"W2272228970","doi":"10.1139/tcsme-2010-0001","title":"MODELING AND NUMERICAL SIMULATION OF AN ALGORITHM FOR THE INERTIAL SENSORS ERRORS REDUCTION AND FOR THE INCREASE OF THE STRAP-DOWN NAVIGATOR REDUNDANCY DEGREE IN A LOW COST ARCHITECTURE","year":2010,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Accelerometer; Redundancy (engineering); Inertial measurement unit; Inertial frame of reference; Acceleration; Inertial navigation system; Computer science; Algorithm; Reduction (mathematics); Real-time computing; Engineering; Simulation; Artificial intelligence; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0003926166,0.0004212144,0.0003259348,0.0003303713,0.0003286554,0.0005957045,0.0005636638,0.0006207716,0.003349274],"category_scores_gemma":[0.001316322,0.0001960966,0.0004166764,0.0002546864,0.0003620238,0.0005941568,0.0002863297,0.0005081931,0.0005382639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005516912,"about_ca_system_score_gemma":0.0008981541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004681871,"about_ca_topic_score_gemma":0.002627238,"domain_scores_codex":[0.9997935,0.00003677578,0.00001015177,0.00003290918,0.0001065137,0.00002020943],"domain_scores_gemma":[0.9996766,0.0001220373,0.0000403248,0.00003326278,0.0001187754,0.000009023587],"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":[0.00003918042,0.00002113454,0.0004528449,0.00008400781,0.00001352676,0.00004194832,0.00006530641,0.96131,0.006420346,0.01284832,0.0004384157,0.01826496],"study_design_scores_gemma":[0.000007731107,0.00001458254,0.00006859275,0.000004714802,0.000004217094,0.00001258418,0.000004887419,0.9967908,0.001474842,0.0006600871,0.0009541956,0.000002690783],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0136943,0.0001251638,0.981719,0.00007842253,0.00003631075,0.00004273884,0.00002669391,0.0004255156,0.003851819],"genre_scores_gemma":[0.5324836,0.0003290507,0.4598085,0.00004212805,0.000027076,0.000388219,0.000123358,0.0001355929,0.006662388],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004681871,"threshold_uncertainty_score":0.01120442,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01124873406127126,"score_gpt":0.2273678021683955,"score_spread":0.2161190681071243,"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."}}