{"paper":{"title":"A Fast Robust Adaptive filter using Improved Data-Reuse Method","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"The RTGA-IDROC algorithm merges total least squares with robust adaptation and improved data reuse to handle input noise while speeding early convergence.","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Haiquan Zhao, Jinhui Hu, Yi Peng","submitted_at":"2026-05-18T13:52:57Z","abstract_excerpt":"Adaptive filter in complex scenarios demands algorithms that integrate fast convergence, low complexity, and robust performance under diverse noise conditions. To address this challenge, we propose a online censoring robust total generalized adaptive filter using improved data-reused method (RTGA-IDROC) algorithm. The proposed RTGA variant possesses the advantages of both the total least squares (TLS) strategy and the robust generalized adaptive (RGA) function. This algorithm not only effectively handles input noise under the errors-in-variables (EIV) model but also achieves excellent performa"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"The proposed RTGA-IDROC algorithm not only effectively handles input noise under the errors-in-variables (EIV) model but also achieves excellent performance across diverse noise environments, with faster convergence enabled by the improved data reuse method without compromising steady-state performance.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The local stability analysis and theoretical steady-state mean-square deviation derivation rest on modeling assumptions about the noise and input statistics that are typical in adaptive filtering but may not hold uniformly across the diverse real-world noise conditions claimed.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Introduces RTGA-IDROC adaptive filter that integrates TLS and RGA advantages with IDR for speed and OC for efficiency, plus local stability analysis and MSD derivation, validated in simulations.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"The RTGA-IDROC algorithm merges total least squares with robust adaptation and improved data reuse to handle input noise while speeding early convergence.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"d7e220d653b1dcdcebb7eaee2ca8628df6e53413b4106076dbf9a9fa95df14cf"},"source":{"id":"2605.18417","kind":"arxiv","version":1},"verdict":{"id":"de467a25-b12a-4c49-a1b9-2139996c725a","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T23:58:39.649576Z","strongest_claim":"The proposed RTGA-IDROC algorithm not only effectively handles input noise under the errors-in-variables (EIV) model but also achieves excellent performance across diverse noise environments, with faster convergence enabled by the improved data reuse method without compromising steady-state performance.","one_line_summary":"Introduces RTGA-IDROC adaptive filter that integrates TLS and RGA advantages with IDR for speed and OC for efficiency, plus local stability analysis and MSD derivation, validated in simulations.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The local stability analysis and theoretical steady-state mean-square deviation derivation rest on modeling assumptions about the noise and input statistics that are typical in adaptive filtering but may not hold uniformly across the diverse real-world noise conditions claimed.","pith_extraction_headline":"The RTGA-IDROC algorithm merges total least squares with robust adaptation and improved data reuse to handle input noise while speeding early convergence."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.18417/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"citation_quote_validity","ran_at":"2026-05-19T23:49:57.082435Z","status":"skipped","version":"0.1.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T23:33:27.594614Z","status":"skipped","version":"1.0.0","findings_count":0},{"name":"external_links","ran_at":"2026-05-19T23:31:35.619085Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"cited_work_retraction","ran_at":"2026-05-19T23:22:00.463249Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T23:21:58.689802Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"d5ff787eb200ad9a6303844bfa44e3c4e15412fe9e0c60c226ce33c5ae027067"},"references":{"count":50,"sample":[{"doi":"","year":2002,"title":"S. 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