{"id":"W3120403041","doi":"10.2514/6.2021-1398","title":"A Geometric Model for Estimating Time Difference of Arrival (TDOA) Performance","year":2021,"lang":"en","type":"article","venue":"AIAA Scitech 2021 Forum","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Multilateration; Cramér–Rao bound; Hyperbola; Computer science; Intersection (aeronautics); FDOA; Ranging; Dwell time; Time of arrival; Common emitter; Dilation (metric space); Algorithm; Statistics; Mathematics; Estimation theory; Geometry; Electronic engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001201015,0.001724938,0.0008151283,0.001959567,0.0003988647,0.001647975,0.002101508,0.001344176,0.004690427],"category_scores_gemma":[0.006839899,0.0006407535,0.00111451,0.002427942,0.0009722483,0.002277765,0.001236248,0.001521101,0.004975521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001434009,"about_ca_system_score_gemma":0.001020242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005730393,"about_ca_topic_score_gemma":0.003794025,"domain_scores_codex":[0.9983801,0.0003234193,0.00006362474,0.0003977631,0.0006882694,0.0001468569],"domain_scores_gemma":[0.9979512,0.0007283963,0.0002837091,0.0002883605,0.0007045056,0.00004384634],"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":[0.00006636106,0.00003578447,0.00209329,0.0001048734,0.00004049798,0.0001254509,0.0001138146,0.887454,0.007057564,0.03663292,0.003653709,0.06262162],"study_design_scores_gemma":[0.000006848019,0.00007086909,0.000628754,0.00002106421,0.00001880948,0.0002272667,0.00002400704,0.9828284,0.002216085,0.008602502,0.005325487,0.00002993931],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002600143,0.0001335366,0.993588,0.0001076495,0.00004568018,0.0000353469,0.0001688107,0.0004939899,0.002826898],"genre_scores_gemma":[0.4956049,0.001971459,0.4821115,0.0003209478,0.0002545328,0.000682754,0.001988184,0.0009411265,0.01612461],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005730393,"threshold_uncertainty_score":0.0156911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01114045078252387,"score_gpt":0.2144674473376576,"score_spread":0.2033269965551337,"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."}}