{"id":"W3195042091","doi":"10.1109/tim.2021.3104395","title":"Attitude Estimation Using Low-Cost MARG Sensors With Disturbances Reduction","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council; University of Calgary","keywords":"Robustness (evolution); Control theory (sociology); Quaternion; Kalman filter; Attitude and heading reference system; Rotation matrix; Gyroscope; Attitude control; Accelerometer; Acceleration; Computer science; Covariance matrix; Angular acceleration; Angular velocity; Algorithm; Engineering; Mathematics; Artificial intelligence; Control engineering; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008912755,0.0001319179,0.00009922555,0.0000726615,0.0001820686,0.00005573024,0.00001902032,0.00004451855,0.00004248978],"category_scores_gemma":[0.000001518705,0.000128483,0.00002893777,0.0001905391,0.00002813198,0.0002436167,2.462365e-7,0.0001029399,0.000006425546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000258403,"about_ca_system_score_gemma":0.00002661564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002858201,"about_ca_topic_score_gemma":0.0000629679,"domain_scores_codex":[0.9991431,0.00003413268,0.0001846342,0.0001674922,0.0003415547,0.0001290353],"domain_scores_gemma":[0.999697,0.000005640687,0.0000323838,0.00009094625,0.0001155503,0.00005843757],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000742105,0.00008194464,0.00005483139,0.00009704078,0.00008124865,0.000003151334,0.0004856088,0.7674114,0.1438888,0.00002674464,0.00002290623,0.08777212],"study_design_scores_gemma":[0.00104855,0.00004619506,0.001212049,0.0001887589,0.0001019871,0.00005842599,0.0005115152,0.1127374,0.8837122,0.00002009779,0.0001248652,0.0002379456],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6264292,0.00003442082,0.3724161,0.00005557284,0.0005546515,0.0002043769,0.000008354315,0.00009746998,0.0001997899],"genre_scores_gemma":[0.9970931,0.00008821852,0.002664028,0.00002295102,0.00003580991,0.00003166527,0.00001469188,0.00001821724,0.00003128439],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7398234,"threshold_uncertainty_score":0.5239385,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02663702325505321,"score_gpt":0.2386991184975139,"score_spread":0.2120620952424607,"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."}}