{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002934372,0.0006349682,0.0005595629,0.0003565086,0.0001867795,0.0003837993,0.0005206143,0.0005098206,0.0005759386],"category_scores_gemma":[0.0008482622,0.0002680588,0.0003065249,0.0004714473,0.0002420283,0.000717868,0.0007034027,0.0003842803,0.0003911166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000173838,"about_ca_system_score_gemma":0.0002130703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000736104,"about_ca_topic_score_gemma":0.00130368,"domain_scores_codex":[0.9996651,0.00005478171,0.00001714599,0.00007408863,0.0001598356,0.00002901809],"domain_scores_gemma":[0.9996669,0.0000591266,0.00008756296,0.00007028063,0.0001016883,0.0000144571],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008178749,0.0001415627,0.01440752,0.0004209423,0.0001352595,0.0003481836,0.0003059548,0.2053194,0.2947792,0.006103876,0.003698544,0.4735218],"study_design_scores_gemma":[0.00002882274,0.0002697338,0.009221625,0.00001853815,0.00004140157,0.0001706119,0.00005773393,0.9181908,0.06685835,0.001336293,0.003761867,0.00004417264],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1113054,0.0004071878,0.8846544,0.0001126043,0.00007651964,0.00003297594,0.0001044009,0.001042508,0.002263949],"genre_scores_gemma":[0.9109498,0.0001341694,0.08732312,0.00004943526,0.00003798548,0.00003335808,0.0001679841,0.00002892915,0.001275233],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.000736104,"threshold_uncertainty_score":0.001926661,"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."}}