{"id":"W4388126712","doi":"10.3390/electronics12214501","title":"Robust Adaptive Beamforming for Interference Suppression Based on SNR","year":2023,"lang":"en","type":"article","venue":"Electronics","topic":"Direction-of-Arrival Estimation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"National Natural Science Foundation of China; Qingdao Agricultural University","keywords":"Adaptive beamformer; Covariance matrix; Beamforming; Interference (communication); Signal-to-interference-plus-noise ratio; Algorithm; Control theory (sociology); Noise (video); Noise power; Subspace topology; Zero-forcing precoding; Quadratic programming; Computer science; Mathematics; Power (physics); Mathematical optimization; Telecommunications; MIMO; Precoding; Artificial intelligence","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":[0.0007373524,0.001113521,0.0005827247,0.0004774862,0.0002902297,0.0005113717,0.0007763107,0.0005325246,0.001723449],"category_scores_gemma":[0.002826641,0.000366992,0.0005520649,0.0006593645,0.0006049192,0.00109909,0.0009326017,0.0008817465,0.0006915876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000392339,"about_ca_system_score_gemma":0.000701134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001141874,"about_ca_topic_score_gemma":0.001478457,"domain_scores_codex":[0.9992681,0.0001742194,0.00003585093,0.0001393691,0.0003352476,0.00004724323],"domain_scores_gemma":[0.9991663,0.0004136944,0.0000850874,0.00006816605,0.0002471571,0.00001965622],"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":[0.0002785597,0.00007187324,0.001130869,0.0002736216,0.0001223626,0.0001478251,0.0001476329,0.4618662,0.0953369,0.05547285,0.002804206,0.3823471],"study_design_scores_gemma":[0.0000132848,0.00005735321,0.0002502151,0.00001463865,0.00001767531,0.00009453465,0.00001203165,0.9769379,0.01439691,0.005720793,0.00246476,0.00001985201],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001687828,0.0001154408,0.9970312,0.00003284982,0.00001239386,0.000008368843,0.000009330644,0.0001204878,0.0009820228],"genre_scores_gemma":[0.2787263,0.0008426907,0.7159396,0.0001797693,0.00009422467,0.0001568735,0.0002233257,0.0002048795,0.003632319],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001723449,"threshold_uncertainty_score":0.005765498,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04036966231173403,"score_gpt":0.2828123081585482,"score_spread":0.2424426458468142,"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."}}