{"id":"W3197404650","doi":"10.1109/access.2021.3105594","title":"Target Detection Through Riemannian Geometric Approach With Application to Drone Detection","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Radar Systems and Signal Processing","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Department of National Defence; Defence Research and Development Canada; Carleton University","funders":"Defence Research and Development Canada","keywords":"Riemannian geometry; Mathematics; Information geometry; Statistical manifold; Riemannian manifold; Clutter; Algorithm; Mathematical analysis; Radar; Computer science; Geometry","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":[0.0004524267,0.0006925045,0.0006559935,0.001064381,0.0002902037,0.0007794663,0.0008458803,0.0006863807,0.0009141197],"category_scores_gemma":[0.002466577,0.0003067623,0.0006156217,0.0008251801,0.0007506601,0.0009888649,0.001049243,0.0007780134,0.0004499251],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005155946,"about_ca_system_score_gemma":0.0004439752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002180763,"about_ca_topic_score_gemma":0.001222518,"domain_scores_codex":[0.9995931,0.000107461,0.00001848458,0.0001090793,0.0001387674,0.00003305632],"domain_scores_gemma":[0.9993074,0.0002612604,0.000105753,0.00007731052,0.0002009781,0.00004726014],"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":[0.0001668872,0.00005854507,0.004347805,0.0002453634,0.0001338856,0.0005403394,0.0003854473,0.519893,0.03276505,0.1410404,0.002848387,0.297575],"study_design_scores_gemma":[0.000002812798,0.00003180833,0.0005334431,0.000004682048,0.000005530265,0.0001300382,0.00001426412,0.9879138,0.001759936,0.00857373,0.001015532,0.00001438274],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01543943,0.0003856461,0.9826402,0.0001071264,0.00002529334,0.00001397116,0.00002584933,0.0002152736,0.001147093],"genre_scores_gemma":[0.6147588,0.001315411,0.3787354,0.000164642,0.0001485073,0.00006689754,0.0002337874,0.0001422167,0.004434221],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002180763,"threshold_uncertainty_score":0.004336119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01291134111083823,"score_gpt":0.2358819269433639,"score_spread":0.2229705858325257,"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."}}