{"id":"W2153532012","doi":"10.1109/ccece.2008.4564615","title":"Alternate amplitude weighting approach for passive source localization using the energy-based grid search algorithm","year":2008,"lang":"en","type":"article","venue":"Conference proceedings - Canadian Conference on Electrical and Computer Engineering","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Weighting; Estimator; Algorithm; Monte Carlo method; Energy (signal processing); White noise; Computer science; Gaussian; Amplitude; Additive white Gaussian noise; Grid; Cramér–Rao bound; Mathematical optimization; Mathematics; Estimation theory; Statistics; Telecommunications; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0004375811,0.0003329171,0.0004430143,0.0004755752,0.0001919987,0.0005054934,0.0007076222,0.000427682,0.002021927],"category_scores_gemma":[0.001781196,0.0001792187,0.0002332975,0.0005774111,0.000302485,0.0008558183,0.0006261583,0.0003367924,0.0006608422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000214474,"about_ca_system_score_gemma":0.0004532762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007684441,"about_ca_topic_score_gemma":0.001051695,"domain_scores_codex":[0.9996884,0.00009156112,0.0000172019,0.00003873982,0.0001480832,0.00001599062],"domain_scores_gemma":[0.9996532,0.0001550487,0.00002784367,0.00005324927,0.00009862736,0.00001195816],"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":[0.0002389922,0.00009492435,0.001689505,0.0001444472,0.00004946082,0.0001155975,0.0001325463,0.2790542,0.0353541,0.04679808,0.001719758,0.6346084],"study_design_scores_gemma":[0.00002799567,0.00007199252,0.0003021604,0.000008771428,0.000011451,0.0001157648,0.000015365,0.9811865,0.006458311,0.009058119,0.002732318,0.0000111845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002726578,0.00003862647,0.9965847,0.00002088779,0.000009933368,0.000007952783,0.000004335042,0.00008604624,0.0005209948],"genre_scores_gemma":[0.200244,0.0001692954,0.7974018,0.0000561246,0.00002967425,0.00009353319,0.00005463594,0.00006470019,0.001886159],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002021927,"threshold_uncertainty_score":0.006764054,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02107659298071593,"score_gpt":0.199108521171722,"score_spread":0.1780319281910061,"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."}}