Artículo
Strict separability and identifiability of a class of ICA models
Autor/es | Murillo Fuentes, Juan José
Boloix Tortosa, Rafael |
Departamento | Universidad de Sevilla. Departamento de Teoría de la Señal y Comunicaciones |
Fecha de publicación | 2010-03 |
Fecha de depósito | 2024-09-30 |
Publicado en |
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Resumen | In this letter we focus on the application of independent component analysis (ICA) to a class of overdetermined blind source separation (BSS) problems. The mixing matrix in the BSS model is the product of an unknown square ... In this letter we focus on the application of independent component analysis (ICA) to a class of overdetermined blind source separation (BSS) problems. The mixing matrix in the BSS model is the product of an unknown square diagonal matrix and a projection matrix. The last matrix performs a known projection to the same or larger dimensional space. We demonstrate the conditions for the model to be strictly separable and identifiable under the statistical independence condition, paying attention to permutations and relative scalings. These results find application, e.g., in the channel estimation of ZP-OFDM and Precoded-OFDM systems |
Agencias financiadoras | Ministerio de Ciencia e Innovación (MICIN). España |
Identificador del proyecto | TEC2009-14504-C02-02/TCM, COMONSENS |
Cita | Murillo Fuentes, J.J. y Boloix Tortosa, R. (2010). Strict separability and identifiability of a class of ICA models. IEEE Signal Processing Letters, 17 (3), 285-288. https://doi.org/10.1109/LSP.2009.2038955. |
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