{"id":"W2090691464","doi":"10.1109/icra.2012.6224562","title":"Invariant Momentum-tracking Kalman Filter for attitude estimation","year":2012,"lang":"en","type":"article","venue":"","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Invariant extended Kalman filter; Extended Kalman filter; Alpha beta filter; Fast Kalman filter; Control theory (sociology); Kalman filter; Ensemble Kalman filter; Mathematics; Computer science; Unscented transform; Artificial intelligence; Moving horizon estimation","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008359332,0.00006080723,0.00005370054,0.00002753502,0.00003588219,0.00001804652,0.00002998909,0.00003644984,0.0001054305],"category_scores_gemma":[0.00001300358,0.00005513195,0.00002474517,0.00004113824,0.000003606405,0.0002769042,0.00000476606,0.00003337454,0.0000646331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003445501,"about_ca_system_score_gemma":0.000001256964,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001148336,"about_ca_topic_score_gemma":0.00000430719,"domain_scores_codex":[0.9996248,0.000003844448,0.000102225,0.00004473641,0.00005581485,0.0001685998],"domain_scores_gemma":[0.9998486,0.00002293156,0.000009681351,0.00006407648,0.00001631137,0.00003840623],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005458093,0.0002130334,0.007436309,0.0006532581,0.0002122539,0.000001812906,0.004837336,0.2524521,0.5297554,0.07446662,0.0306398,0.09927744],"study_design_scores_gemma":[0.0004954411,0.00003021473,0.01153895,0.00003002537,0.00003381025,0.00000501567,0.00004536151,0.6987606,0.2804624,0.00063469,0.007659314,0.0003041377],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3519463,0.00009936605,0.6328058,0.0001146521,0.0008925792,0.000380584,0.000006445732,0.0004411595,0.01331312],"genre_scores_gemma":[0.9911529,0.000002387384,0.008319318,0.00003856142,0.0002265661,0.00002180481,0.00003808431,0.00001508527,0.0001853101],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6392066,"threshold_uncertainty_score":0.2248216,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02245127481457426,"score_gpt":0.2562791732815884,"score_spread":0.2338278984670142,"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."}}