{"paper":{"title":"Array Zooming Optimization for Near-Field Localization With Movable Antennas","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Movable-antenna array zooming fuses multiple measurements to suppress aliasing and outperform fixed arrays in near-field localization accuracy.","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Boyu Teng, Rui Wang, Xiaojun Yuan, Yuxin Duan","submitted_at":"2026-04-30T03:06:24Z","abstract_excerpt":"The emergence of movable antenna (MA) technology provides a promising way to enhance wireless sensing and communication by introducing spatial degrees of freedom through dynamic array reconfiguration. In near-field localization, achieving high resolution at low cost necessitates the adoption of sparse arrays. However, such sparsity tends to introduce spatial ambiguity due to aliasing effects. To resolve this resolution-ambiguity dilemma, this paper proposes an MA-enabled array zooming (AZ) system. First, we design a multi-measurement array zooming system that dynamically adjusts antenna spacin"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"the proposed AZ strategy adaptively optimizes array configurations under varying signal-to-noise ratios (SNRs), substantially outperforming both conventional fixed-spacing arrays and Cramer-Rao bound (CRB)-based AZ benchmarks in localization accuracy.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That fusing multiple measurements from different array spacings can reliably eliminate spatial aliasing without introducing new errors from movement or calibration, and that the derived false-peak distribution accurately models practical multi-modal likelihoods.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"The paper introduces an array zooming optimization using movable antennas that fuses multi-measurement observations to suppress false peaks and achieve better near-field localization accuracy than fixed arrays or CRB-based methods.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Movable-antenna array zooming fuses multiple measurements to suppress aliasing and outperform fixed arrays in near-field localization accuracy.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"2879aad483a00773e1aec4a1cf4b720be69e26b68eb138f2c522376586002be0"},"source":{"id":"2604.27352","kind":"arxiv","version":2},"verdict":{"id":"5a24c229-510d-4cd5-accf-a9c5a85555d1","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-07T08:34:34.237368Z","strongest_claim":"the proposed AZ strategy adaptively optimizes array configurations under varying signal-to-noise ratios (SNRs), substantially outperforming both conventional fixed-spacing arrays and Cramer-Rao bound (CRB)-based AZ benchmarks in localization accuracy.","one_line_summary":"The paper introduces an array zooming optimization using movable antennas that fuses multi-measurement observations to suppress false peaks and achieve better near-field localization accuracy than fixed arrays or CRB-based methods.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That fusing multiple measurements from different array spacings can reliably eliminate spatial aliasing without introducing new errors from movement or calibration, and that the derived false-peak distribution accurately models practical multi-modal likelihoods.","pith_extraction_headline":"Movable-antenna array zooming fuses multiple measurements to suppress aliasing and outperform fixed arrays in near-field localization accuracy."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.27352/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-20T22:39:28.121785Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T19:21:55.427838Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"70cb41ebc69f52610d4a6f0197c7c3072616bac674ef2f1883412850d9be153d"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}