{"id":"W4320925821","doi":"10.18280/rces.090401","title":"LPI Radar Signal Detection Based on Autocorrelation Function and Wigner-Ville Distribution","year":2022,"lang":"en","type":"article","venue":"Review of Computer Engineering Studies","topic":"Radar Systems and Signal Processing","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Autocorrelation; Wigner distribution function; Radar; Autocorrelation technique; SIGNAL (programming language); Radar signal processing; Distribution (mathematics); Statistical physics; Statistics; Mathematics; Computer science; Physics; Signal processing; Mathematical analysis; Telecommunications; Quantum mechanics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0004820905,0.0005285723,0.0005282605,0.00141974,0.0002132012,0.0006779998,0.0005347996,0.000538425,0.0005081533],"category_scores_gemma":[0.002111514,0.0001982761,0.0003850978,0.0009003129,0.0006575991,0.0009888841,0.0003942889,0.0005685025,0.0003176293],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004147306,"about_ca_system_score_gemma":0.0004360675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001086497,"about_ca_topic_score_gemma":0.0008320422,"domain_scores_codex":[0.999455,0.00009848797,0.00002629487,0.0001310713,0.0002452702,0.00004371148],"domain_scores_gemma":[0.9992648,0.00037162,0.0001145198,0.00004805391,0.0001809945,0.0000201405],"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":[0.0002979527,0.00009817817,0.005837704,0.0004481591,0.000127529,0.0005161875,0.0002260652,0.09281042,0.1536037,0.06683615,0.002048455,0.6771495],"study_design_scores_gemma":[0.00001490521,0.0001853547,0.003922618,0.00003646311,0.00004249822,0.001430128,0.000043211,0.9324851,0.04886492,0.009346243,0.003558066,0.00007061295],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02494361,0.001328513,0.9712186,0.00009337462,0.00004599193,0.00003051089,0.00003439676,0.0003750465,0.001929853],"genre_scores_gemma":[0.5925925,0.002578211,0.4015929,0.0001009436,0.0001391011,0.00005810087,0.0001685774,0.00006292791,0.002706838],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00141974,"threshold_uncertainty_score":0.003009081,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008059400114741265,"score_gpt":0.205659936672267,"score_spread":0.1976005365575257,"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."}}