{"id":"W4416846309","doi":"10.3390/aerospace12121065","title":"Vision-Based Geolocation of Moving Ground Targets Using Kalman Filtering with a Gimbal Camera on Board a UAV","year":2025,"lang":"en","type":"article","venue":"Aerospace","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nexen (Canada)","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea; Korea Aerospace University; National Research Foundation","keywords":"Gimbal; Kalman filter; Geolocation; Inertial measurement unit; Extended Kalman filter; Position (finance); Software deployment; Object detection","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":[0.00007551333,0.0001532686,0.0001779918,0.0001278099,0.00007747359,0.00003770826,0.00007735662,0.00006792865,0.00001015498],"category_scores_gemma":[0.000022169,0.0001488678,0.00003655465,0.0003115789,0.00003421851,0.00008078823,0.00001370905,0.00009931494,0.000003651641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001337174,"about_ca_system_score_gemma":0.00003972346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003102478,"about_ca_topic_score_gemma":0.00009855605,"domain_scores_codex":[0.9992639,0.00002026403,0.0001815255,0.0001787963,0.0001628444,0.000192653],"domain_scores_gemma":[0.9995732,0.00006145976,0.00004951522,0.0002044593,0.00007212468,0.00003922888],"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":[0.00002946891,0.00002618752,0.001346331,0.0001227637,0.00002549027,0.000003273597,0.0001109174,0.9656431,0.03157502,0.0005159964,0.00010062,0.0005008422],"study_design_scores_gemma":[0.0004498844,0.0001219207,0.005681569,0.0003797281,0.00002609653,6.86132e-7,0.00007371805,0.9701567,0.02279794,0.00002498452,0.0001240194,0.0001627933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6076346,0.0000804279,0.3912764,0.00009763754,0.0001130738,0.000133154,0.000002259872,0.0001054364,0.000556988],"genre_scores_gemma":[0.9898604,0.000005573565,0.009879916,0.0001001083,0.00002267381,0.000005507766,0.00001213307,0.00002896733,0.00008475225],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3822258,"threshold_uncertainty_score":0.6070651,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007036081373734399,"score_gpt":0.2273427026546946,"score_spread":0.2203066212809602,"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."}}