{"id":"W3038849551","doi":"10.1109/lcomm.2020.3007191","title":"Robust Recursive RSSD Based Source Localization in Gaussian Mixture Channels","year":2020,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Cramér–Rao bound; Estimator; Gaussian noise; Algorithm; Gaussian; Mathematics; Noise (video); Upper and lower bounds; Noise measurement; Benchmark (surveying); Signal-to-noise ratio (imaging); Mathematical optimization; Computer science; Applied mathematics; Estimation theory; Noise reduction; Statistics; Artificial intelligence; Mathematical analysis","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007390611,0.0001582624,0.0001598578,0.0001770402,0.0001189558,0.00004705626,0.0007923348,0.0001318774,0.00002251721],"category_scores_gemma":[0.00007005374,0.0001775421,0.00004896002,0.0008015437,0.0001214668,0.0001328919,0.00005638319,0.0003464293,0.00005090749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009648752,"about_ca_system_score_gemma":0.00001184273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001951918,"about_ca_topic_score_gemma":0.00003883205,"domain_scores_codex":[0.9991715,0.00007467794,0.0002640822,0.0001593834,0.0001135532,0.0002168093],"domain_scores_gemma":[0.99899,0.00007173298,0.00004449806,0.0008002753,0.00003657803,0.0000569363],"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":[0.000004080946,0.00001686184,0.0003791912,0.00003665413,0.00001149422,0.000002253279,0.001793426,0.9794188,0.002381139,0.0002893033,0.01466246,0.001004304],"study_design_scores_gemma":[0.0004101647,0.00001514163,0.00006456211,0.00006612571,0.00001224226,0.000001248481,0.0004813199,0.9391788,0.01211901,0.00005310777,0.04731911,0.0002791805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007303919,0.000360424,0.9513705,0.03874461,0.0002038535,0.0003014554,0.00001216003,0.001050766,0.0006523255],"genre_scores_gemma":[0.9876933,0.0001162605,0.004639439,0.0072852,0.00004938434,0.00006298142,0.0001026189,0.00004360277,0.000007243834],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9803894,"threshold_uncertainty_score":0.7239956,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03282035298952111,"score_gpt":0.2222320983360919,"score_spread":0.1894117453465708,"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."}}