{"id":"W4403182703","doi":"10.1109/lwc.2024.3474774","title":"Novel Iterative Approach for Time-of-Arrivals-Based Localization With Maximum Likelihood Estimation","year":2024,"lang":"en","type":"article","venue":"IEEE Wireless Communications Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Engineering and Physical Sciences Research Council; Chongqing Municipal Education Commission; Natural Science Foundation of Chongqing; Chongqing Science and Technology Commission; National Natural Science Foundation of China","keywords":"Maximum likelihood; Computer science; Iterative method; Estimation; Maximum likelihood sequence estimation; Mathematical optimization; Algorithm; Estimation theory; Mathematics; Statistics","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":[0.001008972,0.001085363,0.001110988,0.0008336274,0.0004888978,0.0008884832,0.001796462,0.00113809,0.001680282],"category_scores_gemma":[0.003244767,0.0006212696,0.000915186,0.001200168,0.000682209,0.001506646,0.001932043,0.00142972,0.0008865889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006472885,"about_ca_system_score_gemma":0.001545043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003246112,"about_ca_topic_score_gemma":0.003086232,"domain_scores_codex":[0.9991713,0.0002710892,0.00004334532,0.0001706514,0.0002672691,0.00007636852],"domain_scores_gemma":[0.9991131,0.0004547143,0.0001003718,0.00006868892,0.0002276799,0.00003537111],"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":[0.000127158,0.00007483509,0.0008913034,0.0002282935,0.0001078333,0.0002558387,0.0003334742,0.7191978,0.01142681,0.03154977,0.003234555,0.2325725],"study_design_scores_gemma":[0.000009324806,0.00002095662,0.00006422395,0.000005605049,0.000008555128,0.00004780266,0.00001161158,0.9942526,0.001077971,0.003557166,0.000934817,0.000009348651],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006079771,0.00005015371,0.9988489,0.00003199754,0.000008446273,0.000007015059,0.000005249989,0.0001051534,0.0003350841],"genre_scores_gemma":[0.1310466,0.0003161742,0.8649135,0.0001339657,0.00008990708,0.0002008547,0.0001381482,0.0001532042,0.003007657],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003246112,"threshold_uncertainty_score":0.006454408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01447823644093594,"score_gpt":0.2373539005855004,"score_spread":0.2228756641445645,"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."}}