{"id":"W1579773993","doi":"10.1002/acs.2471","title":"Least‐squares‐based adaptive target localization by mobile distance measurement sensors","year":2014,"lang":"en","type":"article","venue":"International Journal of Adaptive Control and Signal Processing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Convergence (economics); Algorithm; Recursive least squares filter; Noise (video); Computer science; Least-squares function approximation; Forgetting; Stability (learning theory); Control theory (sociology); Mathematics; Adaptive filter; Artificial intelligence; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003859415,0.0001768567,0.0002456203,0.0001654267,0.00008724858,0.00009973916,0.0002009639,0.00008166867,0.000021104],"category_scores_gemma":[0.00007482858,0.0001526581,0.0000691876,0.0001002381,0.0001011193,0.0003411099,0.00001190295,0.000198699,0.000001269712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00016003,"about_ca_system_score_gemma":0.00005281856,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003184578,"about_ca_topic_score_gemma":0.000002252449,"domain_scores_codex":[0.9985328,0.00005691755,0.0004268809,0.0001321591,0.000684174,0.0001671035],"domain_scores_gemma":[0.9983748,0.00005688589,0.0002355471,0.00004654716,0.001214829,0.00007134149],"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":[0.0005660826,0.0000864641,0.0005010174,0.00004767581,0.0002427887,0.0000230037,0.0003058398,0.8813429,0.009935766,0.001189587,0.001379239,0.1043796],"study_design_scores_gemma":[0.001709142,0.0002642955,0.00009220255,0.0002161631,0.00003163975,0.00001650674,0.0004519773,0.977939,0.01188928,0.0007863246,0.006409797,0.0001937467],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002265978,0.0022899,0.9944448,0.0001869465,0.000172147,0.0000995737,0.00002518603,0.00007660577,0.0004388297],"genre_scores_gemma":[0.9981915,0.00003446162,0.001379359,0.0001990607,0.0001495186,0.000008638401,0.000004881874,0.00002190676,0.00001064063],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9959255,"threshold_uncertainty_score":0.6225217,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008628018849444856,"score_gpt":0.2070578682701627,"score_spread":0.1984298494207179,"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."}}