{"id":"W2982681549","doi":"10.1109/taes.2019.2948517","title":"Comprehensive Time-Offset Estimation for Multisensor Target Tracking","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Aerospace and Electronic Systems","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; General Dynamics (Canada)","funders":"National Natural Science Foundation of China","keywords":"UTC offset; Offset (computer science); Estimator; Computer science; Observability; Preprocessor; Algorithm; Sensor fusion; Cramér–Rao bound; Control theory (sociology); Estimation theory; Mathematics; Computer vision; Artificial intelligence; Global Positioning System; Statistics; Telecommunications","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":[0.0002088184,0.0002465984,0.0003274419,0.0001230693,0.0003194376,0.0002706135,0.0002824545,0.0001535233,0.00001913189],"category_scores_gemma":[0.000003777612,0.0002346214,0.0001058514,0.0002456165,0.0000300926,0.0003494149,0.000002867085,0.0002934977,0.0001962473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009538854,"about_ca_system_score_gemma":0.00005803375,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004736184,"about_ca_topic_score_gemma":0.000006038505,"domain_scores_codex":[0.9982294,0.00008719968,0.0002925826,0.0005747742,0.0002476847,0.0005683059],"domain_scores_gemma":[0.9987629,0.0003731373,0.0001241076,0.0005119247,0.000121198,0.0001067376],"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":[0.0002138931,0.0002928498,0.00006476409,0.0003307299,0.000220448,0.000004870516,0.0009936367,0.8994372,0.02844205,0.006102249,0.005388646,0.05850868],"study_design_scores_gemma":[0.001063834,0.0003923838,0.00004128441,0.00009998689,0.00001929714,0.00006063109,0.00007511071,0.979203,0.004381986,0.0001194457,0.01422541,0.0003175554],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04302793,0.0005258535,0.9536027,0.0002821713,0.00130878,0.0008765709,0.00004366979,0.0002604649,0.00007189767],"genre_scores_gemma":[0.9894735,0.0001179839,0.008406269,0.000130036,0.00007128017,0.00008940406,0.0000173197,0.00002983594,0.001664325],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9464456,"threshold_uncertainty_score":0.9567583,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.010781431199475,"score_gpt":0.2342413313527211,"score_spread":0.2234599001532461,"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."}}