{"ID":2899840,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.01230","arxiv_id":"2507.01230","title":"Numerical Techniques for the Maximum Likelihood Toeplitz Covariance Matrix Estimation: Part I. Symmetric Toeplitz Matrices","abstract":"In several applications, one must estimate a real-valued (symmetric) Toeplitz covariance matrix, typically shifted by the conjugated diagonal matrices of phase progression and phase \"calibration\" errors. Unlike the Hermitian Toeplitz covariance matrices, these symmetric matrices have a unique potential capability of being estimated regardless of these beam-steering phase progression and/or phase \"calibration\" errors. This unique capability is the primary motivation of this paper.","short_abstract":"In several applications, one must estimate a real-valued (symmetric) Toeplitz covariance matrix, typically shifted by the conjugated diagonal matrices of phase progression and phase \"calibration\" errors. Unlike the Hermitian Toeplitz covariance matrices, these symmetric matrices have a unique potential capability of be...","url_abs":"https://arxiv.org/abs/2507.01230","url_pdf":"https://arxiv.org/pdf/2507.01230v1","authors":"[\"Yuri Abramovich\",\"Victor Abramovich\",\"Tanit Pongsiri\"]","published":"2025-07-01T23:07:22Z","proceeding":"eess.SP","tasks":"[\"eess.SP\",\"cs.IT\"]","methods":"[]","has_code":false}