{"id":"W2912630397","doi":"10.1109/mwscas.2018.8623949","title":"Multi-sensor Attitude and Heading Reference System using Genetically Optimized Kalman Filter","year":2018,"lang":"en","type":"article","venue":"","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Attitude and heading reference system; Gyroscope; Extended Kalman filter; Heading (navigation); Accelerometer; Kalman filter; Inertial navigation system; Control theory (sociology); Computer science; Inertial measurement unit; Noise (video); Covariance; Orientation (vector space); Engineering; Artificial intelligence; Mathematics","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":[0.0006819239,0.0009003535,0.0008269031,0.0005353461,0.0004830002,0.0007783519,0.0008533217,0.0008093112,0.0006827247],"category_scores_gemma":[0.001214952,0.0003665321,0.000700846,0.0005429012,0.0004032973,0.0006308312,0.0005601859,0.0006422358,0.0003099648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007458975,"about_ca_system_score_gemma":0.001178385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01676205,"about_ca_topic_score_gemma":0.01165084,"domain_scores_codex":[0.9995136,0.00009084207,0.00002890857,0.0001556016,0.0001612293,0.00004977502],"domain_scores_gemma":[0.999545,0.0001027068,0.0001125071,0.00004171916,0.000181474,0.00001652294],"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":[0.00003467459,0.0000261277,0.0008352753,0.00003428746,0.00004205802,0.00003715304,0.00005234125,0.9410769,0.003822055,0.001722604,0.0003718614,0.0519447],"study_design_scores_gemma":[0.00000912186,0.00003580598,0.0003767684,0.000005661418,0.00001422881,0.00001337904,0.000006503296,0.9976584,0.0009396626,0.0004855666,0.0004456451,0.000009288095],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01631519,0.0001310836,0.9814431,0.00005167483,0.00003311765,0.00003987778,0.00003291218,0.0004943387,0.001458741],"genre_scores_gemma":[0.6916817,0.0002155261,0.3046196,0.00007062715,0.00003355728,0.0003153131,0.0002457705,0.00005462538,0.002763254],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01676205,"threshold_uncertainty_score":0.03332895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04119432844888295,"score_gpt":0.270884065723885,"score_spread":0.229689737275002,"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."}}