{"id":"W2064968522","doi":"10.1117/12.542259","title":"&lt;title&gt;Geolocation of multiple emitters in the presence of clutter&lt;/title&gt;","year":2004,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Multilateration; Common emitter; Geolocation; Clutter; FDOA; Estimator; Computer science; Algorithm; Nonlinear system; Set (abstract data type); Acoustics; Mathematics; Electronic engineering; Physics; Telecommunications; Radar; Engineering; 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":[0.0005710473,0.0006603458,0.0008193435,0.0008418358,0.0004579317,0.001358393,0.0009886873,0.001018039,0.04633969],"category_scores_gemma":[0.0009585306,0.0002874366,0.0004666245,0.001312162,0.0006055843,0.001757383,0.0007146996,0.0006615421,0.03415122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006158495,"about_ca_system_score_gemma":0.0005341394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001983219,"about_ca_topic_score_gemma":0.003861896,"domain_scores_codex":[0.9996312,0.00004954959,0.00002236668,0.0001054437,0.0001614856,0.00002994123],"domain_scores_gemma":[0.9992907,0.0001543402,0.00007054569,0.0001626285,0.0002775688,0.00004422685],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005235318,0.00008227075,0.0008584386,0.0006185941,0.00004102543,0.0006932016,0.0001103597,0.01103391,0.05374959,0.0320681,0.1841534,0.7160676],"study_design_scores_gemma":[0.00006492621,0.000406047,0.002087113,0.0002134905,0.00005519609,0.001506389,0.000103126,0.1116055,0.0929874,0.01657792,0.7742778,0.0001151683],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01323127,0.01070074,0.8433185,0.003262405,0.009578804,0.0003033806,0.00161377,0.00914029,0.1088509],"genre_scores_gemma":[0.1712967,0.01215034,0.3630203,0.001524681,0.003771648,0.0002422581,0.005179354,0.003319316,0.4394954],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04633969,"threshold_uncertainty_score":0.1550217,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01110534453435697,"score_gpt":0.2210356571820597,"score_spread":0.2099303126477027,"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."}}