{"id":"W2328617575","doi":"10.2514/6.2008-7317","title":"An Adaptive Kalman Filter for Motion Esitmation/Prediction of a Free-Falling Space Object Using Laser-Vision Data with Uncertain Inertial and Noise Characteristics","year":2008,"lang":"en","type":"article","venue":"AIAA Guidance, Navigation and Control Conference and Exhibit","topic":"Space Satellite Systems and Control","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Space Agency","funders":"","keywords":"Kalman filter; Computer vision; Noise (video); Computer science; Object (grammar); Artificial intelligence; Trajectory; Motion (physics); Falling (accident); Inertial frame of reference; Space (punctuation); Filter (signal processing); Control theory (sociology); Physics; Image (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":[0.0006581447,0.0005291591,0.0005719545,0.000432475,0.0004153993,0.0005571491,0.000910849,0.0007239549,0.0009225405],"category_scores_gemma":[0.001948087,0.0003887191,0.0004485411,0.000446862,0.0003289309,0.001155445,0.0004840243,0.0008541991,0.0003941232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006064614,"about_ca_system_score_gemma":0.001440147,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01498342,"about_ca_topic_score_gemma":0.01520287,"domain_scores_codex":[0.9996291,0.00004479951,0.00002551548,0.00008188526,0.0001880359,0.00003065506],"domain_scores_gemma":[0.9996009,0.0001735227,0.00005064228,0.00003109284,0.0001316289,0.00001215147],"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":[0.0001762914,0.00006400807,0.001815519,0.0002169803,0.0000977606,0.000138782,0.0001504133,0.6030547,0.02635821,0.01537332,0.002381185,0.3501728],"study_design_scores_gemma":[0.00001128794,0.0000387067,0.0003438299,0.000007947194,0.00001857028,0.00002584288,0.000004905593,0.9945802,0.002168104,0.001141018,0.001649503,0.00001010704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002955176,0.00009058334,0.9964008,0.00002389269,0.00002097627,0.00001239439,0.00002262231,0.0002342818,0.0002392695],"genre_scores_gemma":[0.4125231,0.0007585816,0.5820761,0.00009832811,0.0001257091,0.0002984609,0.0004333928,0.00008473002,0.003601479],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01498342,"threshold_uncertainty_score":0.02979243,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02883172840542435,"score_gpt":0.243189499276063,"score_spread":0.2143577708706386,"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."}}