{"id":"W4400646697","doi":"10.1109/lcomm.2024.3428440","title":"Insights Into Visible Light Positioning: Range Tracking and Bayesian Cramér-Rao Lower Bound Analysis","year":2024,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Optical Wireless Communication Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cramér–Rao bound; Visible light communication; Bayesian probability; Computer science; Range (aeronautics); Tracking (education); Upper and lower bounds; Telecommunications; Artificial intelligence; Mathematics; Algorithm; Light-emitting diode; Estimation theory; Physics; Optics; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003178425,0.001000903,0.001045858,0.001875811,0.0005823958,0.001968863,0.001405308,0.00157958,0.001998487],"category_scores_gemma":[0.02274762,0.0008612654,0.0007105322,0.001663194,0.002253477,0.003492656,0.00172836,0.002006836,0.0007016085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001448131,"about_ca_system_score_gemma":0.002102182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007856695,"about_ca_topic_score_gemma":0.006831343,"domain_scores_codex":[0.9971199,0.0007451445,0.0001101498,0.0004650216,0.001367409,0.0001923449],"domain_scores_gemma":[0.9900631,0.006657694,0.001006257,0.0007809444,0.001370179,0.0001218398],"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":[0.000089967,0.00005703515,0.003387223,0.0003105856,0.0001042713,0.0001788013,0.0002618965,0.7217033,0.007347303,0.1928101,0.002144275,0.07160517],"study_design_scores_gemma":[0.000009392726,0.00007145401,0.001962095,0.0001031849,0.00002760615,0.0002160517,0.00006058582,0.9355249,0.003130958,0.05565056,0.003180346,0.00006287156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00621518,0.001498318,0.986892,0.0004685305,0.00003775716,0.00001520348,0.00006517882,0.0001047875,0.004703073],"genre_scores_gemma":[0.7433429,0.006649561,0.2415461,0.0006712952,0.0003949805,0.0001604093,0.0003657511,0.0002294096,0.006639635],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007856695,"threshold_uncertainty_score":0.01680928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01431197746454977,"score_gpt":0.2556707148934809,"score_spread":0.2413587374289312,"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."}}