{"id":"W4323697349","doi":"10.48550/arxiv.2303.03739","title":"Path Planning Under Uncertainty to Localize mmWave Sources","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Standards and Technology; York University; National Science Foundation","keywords":"Computer science; Estimator; Solver; Motion planning; Kalman filter; SIGNAL (programming language); Mathematical optimization; Real-time computing; Wireless; Artificial intelligence; Computer vision; Robot; Telecommunications; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005172939,0.0006476519,0.0005512213,0.0004110221,0.0003355503,0.0004376814,0.0006304894,0.0006299117,0.001415481],"category_scores_gemma":[0.002824221,0.0004555664,0.0004025909,0.000482966,0.000607985,0.001132752,0.0009536244,0.0008839618,0.0002597088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005235453,"about_ca_system_score_gemma":0.0008195654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005551107,"about_ca_topic_score_gemma":0.0037948,"domain_scores_codex":[0.999719,0.00008297592,0.000011955,0.00006673098,0.00008684581,0.00003250785],"domain_scores_gemma":[0.9990968,0.0005905048,0.0001037334,0.00005796483,0.0001236601,0.00002738279],"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":[0.00002453879,0.00000909856,0.0002090873,0.00003049676,0.00001153075,0.00002981417,0.00005297695,0.9773102,0.001007956,0.004480686,0.0003653754,0.01646832],"study_design_scores_gemma":[0.000004774435,0.00001339309,0.00005907682,0.00000345202,0.000002712707,0.000008674348,0.000009273728,0.9945945,0.0005416427,0.00435868,0.0004006465,0.000003137679],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007275893,0.0001210141,0.9918523,0.00007799725,0.00001254186,0.00001199026,0.00002436528,0.0001534623,0.0004704228],"genre_scores_gemma":[0.5893072,0.0004824883,0.4067816,0.0001024078,0.00005197637,0.0001746864,0.0002488498,0.0001753316,0.002675393],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005551107,"threshold_uncertainty_score":0.01103759,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08406414356367173,"score_gpt":0.1938488270061745,"score_spread":0.1097846834425028,"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."}}