{"id":"W1988995727","doi":"10.1016/j.adhoc.2014.12.001","title":"TOA-based joint synchronization and source localization with random errors in sensor positions and sensor clock biases","year":2014,"lang":"en","type":"article","venue":"Ad Hoc Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"National Natural Science Foundation of China","keywords":"Cramér–Rao bound; Synchronization (alternating current); Position (finance); Computer science; Clock synchronization; Noise (video); Joint (building); Algorithm; Upper and lower bounds; Time of arrival; Wireless sensor network; Observational error; Real-time computing; Estimation theory; Mathematics; Statistics; Artificial intelligence; Telecommunications; Engineering; Wireless","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":[0.0001084302,0.0001700486,0.0002033871,0.0001538922,0.0001120674,0.00005335385,0.00003855372,0.0001571565,0.000008502796],"category_scores_gemma":[0.00009758059,0.0001545264,0.00001739396,0.000338899,0.00009884299,0.00009342433,0.00001630333,0.0001369413,0.000001798766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004101913,"about_ca_system_score_gemma":0.000008053215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001128893,"about_ca_topic_score_gemma":0.0001320621,"domain_scores_codex":[0.9992329,0.00005133182,0.0002095025,0.0001936041,0.00009193517,0.0002207175],"domain_scores_gemma":[0.9995664,0.0001223486,0.00004088757,0.0001522929,0.00006864865,0.0000494012],"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":[0.00003367675,0.00001021628,0.003011035,0.00003871724,0.00001056304,0.000002366408,0.0001051396,0.9870771,0.00003007121,0.0001002467,0.00020676,0.009374079],"study_design_scores_gemma":[0.001578609,0.00008355777,0.001571404,0.0001690563,0.00002675526,0.000008820256,0.0001991059,0.9942953,0.0006919764,0.00003274348,0.001130416,0.000212241],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1396306,0.001142455,0.8582813,0.0000939386,0.00007277892,0.0002392396,0.000003075093,0.0004486265,0.00008800095],"genre_scores_gemma":[0.9974136,0.0004369466,0.001789918,0.000153631,0.00005390635,0.0000213467,0.00006835449,0.0000400588,0.00002226954],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.857783,"threshold_uncertainty_score":0.6301403,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006884454109065977,"score_gpt":0.1886045590287481,"score_spread":0.1817201049196821,"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."}}