{"id":"W2421965999","doi":"10.1109/radar.2016.7485243","title":"Reduced time-on-target in pulse Doppler radar: Slow time domain compressed sensing","year":2016,"lang":"en","type":"article","venue":"","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Azrieli Foundation; European Commission","keywords":"Computer science; Nyquist rate; Doppler effect; Pulse-Doppler radar; Time domain; Radar; Bandwidth (computing); Nyquist–Shannon sampling theorem; Doppler radar; Continuous-wave radar; SIGNAL (programming language); Compressed sensing; Electronic engineering; Sampling (signal processing); Pulse repetition frequency; Real-time computing; Radar imaging; Algorithm; Telecommunications; Physics; Filter (signal processing); Computer vision; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001331569,0.000248033,0.0002986084,0.0001829816,0.00003942372,0.00003598154,0.0001568781,0.0001200181,0.000621656],"category_scores_gemma":[0.00002176142,0.0001830436,0.00006519273,0.0001628065,0.00004317155,0.0001206341,0.00004469553,0.0001372398,0.0006785842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000900642,"about_ca_system_score_gemma":0.00001265208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002355844,"about_ca_topic_score_gemma":0.000003274146,"domain_scores_codex":[0.9988303,0.00006052385,0.0002733755,0.0002693823,0.0001825705,0.0003838532],"domain_scores_gemma":[0.9992507,0.000154834,0.00003060855,0.0004468339,0.00003461912,0.00008241965],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002602323,0.0000203543,0.00001437171,0.000003672177,0.00002299719,0.00004328353,0.00005210907,0.0004040349,0.9371973,0.0001053457,0.05673981,0.005370661],"study_design_scores_gemma":[0.0008172907,0.00004591466,0.0002601377,0.0002957516,0.000006325299,0.00003035736,0.000008478296,0.03554367,0.9516736,0.00357178,0.007265256,0.0004813896],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.847519,0.0001026938,0.0320755,0.0009199292,0.0002932284,0.0005273286,0.00001852953,0.003371768,0.115172],"genre_scores_gemma":[0.9553291,0.00001371936,0.04225229,0.0001897407,0.0001144992,0.000004055995,0.000009246081,0.00007463349,0.002012676],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1131594,"threshold_uncertainty_score":0.8722053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008788441656116985,"score_gpt":0.2057687470924242,"score_spread":0.1969803054363072,"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."}}