{"ID":2867580,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.17511","arxiv_id":"2509.17511","title":"Single-Snapshot Localization Using Sparse Extremely Large Aperture Arrays","abstract":"This paper investigates single-snapshot direction-of-arrival (DOA) estimation and target localization with coherent sparse extremely large aperture arrays (ELAAs) in automotive radar applications. Far-field and near-field signal models are formulated for distributed bistatic configurations. To enable noncoherent processing, a single-snapshot MUSIC (SS-MUSIC) algorithm is proposed to fuse local spectra from individual subarrays and extended to near-field localization via geometric intersection. For coherent processing, a single-snapshot ESPRIT (SS-ESPRIT) method with ambiguity dealiasing is developed to fully exploit the aperture of sparse ELAAs for high-resolution angle estimation. Simulation results demonstrate that SS-ESPRIT provides superior angular resolution for closely spaced far-field targets, while SS-MUSIC offers robustness in near-field localization and flexibility in hybrid scenarios.","short_abstract":"This paper investigates single-snapshot direction-of-arrival (DOA) estimation and target localization with coherent sparse extremely large aperture arrays (ELAAs) in automotive radar applications. Far-field and near-field signal models are formulated for distributed bistatic configurations. To enable noncoherent proces...","url_abs":"https://arxiv.org/abs/2509.17511","url_pdf":"https://arxiv.org/pdf/2509.17511v1","authors":"[\"Yunqiao Hu\",\"Xuesu Xiao\",\"Steven Jones\",\"Shunqiao Sun\"]","published":"2025-09-22T08:37:20Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[]","has_code":false}