{"ID":2823344,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2601.00999","arxiv_id":"2601.00999","title":"Dynamic Accuracy Estimation in a Wi-Fi-based Positioning System","abstract":"The paper presents a concept of a dynamic accuracy estimation method, in which the localization errors are derived based on the measurement results used by the positioning algorithm. The concept was verified experimentally in a Wi\\nobreakdash-Fi based indoor positioning system, where several regression methods were tested (linear regression, random forest, k-nearest neighbors, and neural networks). The highest positioning error estimation accuracy was achieved for random forest regression, with a mean absolute error of 0.72 m.","short_abstract":"The paper presents a concept of a dynamic accuracy estimation method, in which the localization errors are derived based on the measurement results used by the positioning algorithm. The concept was verified experimentally in a Wi\\nobreakdash-Fi based indoor positioning system, where several regression methods were tes...","url_abs":"https://arxiv.org/abs/2601.00999","url_pdf":"https://arxiv.org/pdf/2601.00999v1","authors":"[\"Marcin Kolakowski\",\"Vitomir Djaja-Josko\"]","published":"2026-01-02T22:46:41Z","proceeding":"eess.SP","tasks":"[\"eess.SP\",\"cs.LG\"]","methods":"[]","has_code":false}