{"id":"W2606276298","doi":"10.1186/s13638-017-0851-1","title":"Localization algorithms for asynchronous time difference of arrival positioning systems","year":2017,"lang":"en","type":"article","venue":"EURASIP Journal on Wireless Communications and Networking","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Cramér–Rao bound; Asynchronous communication; Algorithm; Convergence (economics); Multilateration; Semidefinite programming; Upper and lower bounds; Synchronization (alternating current); Time of arrival; Variance (accounting); Channel (broadcasting); Mathematical optimization; Estimation theory; Telecommunications; Node (physics); Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009533337,0.0008888498,0.0006277466,0.0007169651,0.0005201352,0.001018092,0.001255553,0.0009775725,0.003573304],"category_scores_gemma":[0.00319132,0.0004041425,0.0005093985,0.001189478,0.0005280225,0.001372632,0.001242458,0.001423298,0.001521514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007145392,"about_ca_system_score_gemma":0.0007508257,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002330191,"about_ca_topic_score_gemma":0.001971229,"domain_scores_codex":[0.9990676,0.0002531608,0.00005238652,0.0002187195,0.0003437239,0.00006441691],"domain_scores_gemma":[0.9989716,0.0004619195,0.0001347515,0.00007664436,0.0003287449,0.00002632367],"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":[0.00006270205,0.00003372717,0.0004745064,0.0002121598,0.0000334416,0.00009120841,0.0001781702,0.6599997,0.006208332,0.1161048,0.0060568,0.2105443],"study_design_scores_gemma":[0.00001251962,0.00002588477,0.00009049608,0.00001286442,0.000005870247,0.00005165791,0.0000173566,0.9775047,0.0007846425,0.01684477,0.004638868,0.00001044738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006999524,0.0001144874,0.9980818,0.00005585036,0.00002145618,0.000009712419,0.00001465841,0.00008272154,0.0009193054],"genre_scores_gemma":[0.2405338,0.00170859,0.7434822,0.0003035881,0.0002577802,0.0003863975,0.0003547533,0.0002043471,0.01276855],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003573304,"threshold_uncertainty_score":0.01195389,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03066008911659349,"score_gpt":0.2691121223503867,"score_spread":0.2384520332337932,"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."}}