{"id":"W3014081040","doi":"10.1109/icnc47757.2020.9049799","title":"Linear FMCW Radar System for Accurate Indoor Localization and Trajectory Detection","year":2020,"lang":"en","type":"article","venue":"2020 International Conference on Computing, Networking and Communications (ICNC)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Constant false alarm rate; Computer science; Continuous-wave radar; Radar; Kalman filter; Detector; Trajectory; Position (finance); Doppler effect; Doppler frequency; Computer vision; Doppler radar; Pulse-Doppler radar; Radar imaging; Artificial intelligence; Telecommunications; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004348346,0.0004371633,0.0004008706,0.0004074265,0.0001971504,0.0003295427,0.000596524,0.0006246018,0.00217756],"category_scores_gemma":[0.0007845612,0.0001757707,0.0002125464,0.0006119381,0.0002154468,0.0007743023,0.0005137657,0.0005455113,0.001794453],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000280258,"about_ca_system_score_gemma":0.0003054631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006232888,"about_ca_topic_score_gemma":0.0005610099,"domain_scores_codex":[0.9994503,0.0001298833,0.00002045487,0.0001162258,0.0002333469,0.00004972637],"domain_scores_gemma":[0.9996386,0.00008973494,0.00006312624,0.00006083559,0.0001322269,0.00001543678],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002032184,0.0001118626,0.00181751,0.0004661536,0.00005078483,0.0002141463,0.0001709385,0.01897815,0.4168113,0.007325773,0.006160186,0.5476899],"study_design_scores_gemma":[0.0001327058,0.00142218,0.007204941,0.00009630337,0.0001214735,0.002062324,0.0001138303,0.5784729,0.3469499,0.003243685,0.06002993,0.0001498911],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01555385,0.0008807987,0.978343,0.0001838693,0.00008432104,0.000035444,0.00006464304,0.001903987,0.002950147],"genre_scores_gemma":[0.4901045,0.001031851,0.4991369,0.0005736822,0.0002218066,0.0001188414,0.0003570274,0.00008361934,0.008371756],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00217756,"threshold_uncertainty_score":0.007284701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05047648642916045,"score_gpt":0.2681436908502404,"score_spread":0.21766720442108,"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."}}