{"ID":2862976,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.17823","arxiv_id":"2510.17823","title":"Covariance Matrix Construction with Preprocessing-Based Spatial Sampling for Robust Adaptive Beamforming","abstract":"This work proposes an efficient, robust adaptive beamforming technique to deal with steering vector (SV) estimation mismatches and data covariance matrix reconstruction problems. In particular, the direction-of-arrival(DoA) of interfering sources is estimated with available snapshots in which the angular sectors of the interfering signals are computed adaptively. Then, we utilize the well-known general linear combination algorithm to reconstruct the interference-plus-noise covariance (IPNC) matrix using preprocessing-based spatial sampling (PPBSS). We demonstrate that the preprocessing matrix can be replaced by the sample covariance matrix (SCM) in the shrinkage method. A power spectrum sampling strategy is then devised based on a preprocessing matrix computed with the estimated angular sectors' information. Moreover, the covariance matrix for the signal is formed for the angular sector of the signal-of-interest (SOI), which allows for calculating an SV for the SOI using the power method. An analysis of the array beampattern in the proposed PPBSS technique is carried out, and a study of the computational cost of competing approaches is conducted. Simulation results show the proposed method's effectiveness compared to existing approaches.","short_abstract":"This work proposes an efficient, robust adaptive beamforming technique to deal with steering vector (SV) estimation mismatches and data covariance matrix reconstruction problems. In particular, the direction-of-arrival(DoA) of interfering sources is estimated with available snapshots in which the angular sectors of the...","url_abs":"https://arxiv.org/abs/2510.17823","url_pdf":"https://arxiv.org/pdf/2510.17823v1","authors":"[\"Saeed Mohammadzadeh\",\"Rodrigo C. de Lamare\",\"Yuriy Zakharov\"]","published":"2025-09-30T17:46:44Z","proceeding":"eess.SP","tasks":"[\"eess.SP\",\"cs.IT\",\"cs.LG\"]","methods":"[]","has_code":false}