{"id":"W3148883980","doi":"10.1007/s10291-021-01119-w","title":"Enhanced fault detection and exclusion based on Kalman filter with colored measurement noise and application to RTK","year":2021,"lang":"en","type":"article","venue":"GPS Solutions","topic":"GNSS positioning and interference","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Chinese Government Scholarship; Natural Sciences and Engineering Research Council of Canada","keywords":"GNSS applications; Kalman filter; Fault detection and isolation; Computer science; False alarm; Algorithm; Real-time computing; Noise (video); Engineering; Control theory (sociology); Global Positioning System; Artificial intelligence; Telecommunications","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":[0.00005858016,0.00007809563,0.00006392897,0.00003931459,0.0001775537,0.00003000915,0.00002271232,0.00003647576,0.000007684358],"category_scores_gemma":[0.00001730565,0.0000763295,0.00001040225,0.0001173524,0.00001716868,0.0000446114,0.00001518327,0.00007025557,0.00001210192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008942128,"about_ca_system_score_gemma":0.00001305562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001995903,"about_ca_topic_score_gemma":0.0002855856,"domain_scores_codex":[0.9995112,0.00001601691,0.00007785233,0.000159106,0.0001171309,0.0001186293],"domain_scores_gemma":[0.9996899,0.00001258268,0.00001113567,0.0001268717,0.00009677892,0.00006273993],"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":[0.00003404101,0.00004562686,0.00003713687,0.00003125684,0.00001316479,6.611906e-7,0.0002895314,0.06787348,0.9230199,0.000147123,0.0002000807,0.008308038],"study_design_scores_gemma":[0.0006995046,0.0003469253,0.03171037,0.0002503494,0.00004147388,0.00001179438,0.000124582,0.5557552,0.4095525,0.00009109712,0.001134856,0.0002813648],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3119352,0.00005954682,0.6853009,0.0002128984,0.00005514782,0.0001589951,0.000007376708,0.0001156329,0.002154295],"genre_scores_gemma":[0.9985541,0.000009213748,0.001159213,0.00008392688,0.00002068214,0.0001205187,0.00001054251,0.000009729523,0.00003213737],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6866189,"threshold_uncertainty_score":0.3112627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01442360206326978,"score_gpt":0.2087549026100382,"score_spread":0.1943313005467684,"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."}}