{"ID":2844370,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.06383","arxiv_id":"2511.06383","title":"Performance Bounds for Near-Field Velocity Estimation With Modular Linear Array","abstract":"Velocity estimation is a cornerstone of the recently introduced near-field predictive beamforming. This paper derives the Cramer-Rao bounds (CRBs) for joint radial and transverse velocity estimation within a predictive beamforming framework employing a modular linear array (MLA). We obtain closed-form expressions that characterize the interplay between array geometry and estimation accuracy, showing that increasing the inter-module separation enlarges the effective aperture and reduces the transverse-velocity CRB, while the radial-velocity CRB remains largely insensitive to this separation. Furthermore, we show that an MLA can achieve the same accuracy as a collocated ULA with fewer antennas and quantify the relation between inter-module spacing and antenna savings. The derived expressions are validated through simulations by comparing them with the mean-squared error (MSE) of the maximum likelihood estimator (MLE) reported in the literature.","short_abstract":"Velocity estimation is a cornerstone of the recently introduced near-field predictive beamforming. This paper derives the Cramer-Rao bounds (CRBs) for joint radial and transverse velocity estimation within a predictive beamforming framework employing a modular linear array (MLA). We obtain closed-form expressions that...","url_abs":"https://arxiv.org/abs/2511.06383","url_pdf":"https://arxiv.org/pdf/2511.06383v4","authors":"[\"Khalid A. Alshumayri\",\"Mudassir Masood\",\"Ali. A. Nasir\"]","published":"2025-11-09T13:40:46Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[]","has_code":false}