{"id":"W2167414424","doi":"10.1080/00207720701669446","title":"An alternative Kalman innovation filter approach for receiver position estimation based on GPS measurements","year":2007,"lang":"en","type":"article","venue":"International Journal of Systems Science","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"National Aeronautics and Space Administration","keywords":"Pseudorange; Kalman filter; Extended Kalman filter; Position (finance); Fast Kalman filter; Control theory (sociology); Alpha beta filter; Computer science; Invariant extended Kalman filter; Noise (video); GPS/INS; Global Positioning System; Assisted GPS; Telecommunications; Artificial intelligence; Moving horizon estimation; GNSS applications","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":[0.0008879467,0.0007986287,0.001024904,0.001121429,0.0003391364,0.0008088825,0.001184491,0.001253949,0.001872912],"category_scores_gemma":[0.002623115,0.0004939587,0.0009560685,0.001136293,0.0004329679,0.00178106,0.0007534145,0.001125763,0.0009135107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006959235,"about_ca_system_score_gemma":0.001153519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006806133,"about_ca_topic_score_gemma":0.00614504,"domain_scores_codex":[0.9987661,0.0002474361,0.00006884203,0.0002725564,0.0005695001,0.00007564258],"domain_scores_gemma":[0.9993485,0.0002748343,0.00006769186,0.00006389184,0.0002303075,0.00001486342],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002094594,0.00009107209,0.002262482,0.0005368503,0.0003265463,0.0002651532,0.0002904323,0.3183728,0.02427829,0.06264395,0.003663549,0.5870594],"study_design_scores_gemma":[0.00003234305,0.00008432334,0.0007743681,0.00002798353,0.00007259352,0.0001347146,0.00001816285,0.9788362,0.004794102,0.006990788,0.008173788,0.00006054122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001458212,0.000145515,0.9974601,0.00003540758,0.00004612918,0.000008757633,0.00002280755,0.0002261061,0.0005969286],"genre_scores_gemma":[0.2407949,0.001301809,0.7504578,0.0001811402,0.0003387808,0.0001827534,0.0004009358,0.0001301355,0.006211662],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006806133,"threshold_uncertainty_score":0.01353306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04092328143170371,"score_gpt":0.3079419184984817,"score_spread":0.267018637066778,"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."}}