Blind Source Separation With Compressively Sensed Linear Mixtures
نویسندگان
چکیده
منابع مشابه
Blind Source Separation of Compressively Sensed Signals
We present an approach to simultaneously separate and reconstruct signals from a compressively sensed linear mixture. We assume that the signals have a common sparse representation. The approach combines classical Compressive Sensing (CS) theory with a linear mixing model. Since Blind Source Separation (BSS) from a linear mixture is only possible up to permutation and scaling, factoring out the...
متن کاملBlind Source Separation of Linear Mixtures with Singular Matrices
Abstract. We consider the Blind Source Separation problem of linear mixtures with singular matrices and show that it can be solved if the sources are sufficiently sparse. More generally, we consider the problem of identifying the source matrix S ∈ IR if a linear mixture X = AS is known only, where A ∈ IR, m 6 n and the rank of A is less than m. A sufficient condition for solving this problem is...
متن کاملBlind source separation with time-dependent mixtures
We address the problem of blind source separation in the case of a time dependent mixture matrix. For a slowly and smoothly varying mixture matrix, we propose a systematic expansion which leads to a practical algebraic solution when stationary and ergodic properties hold for the sources. Resum e Nous consid erons le probl eme de la s eparation aveugle de sources dans le cas d'une matrice de m e...
متن کاملBlind Source Separation with Pure Delay Mixtures
We address the problem of blind separation of mixtures consisting of pure unknown delays in addition to scalar mixing coefficients. Such a mixture is a hybrid situation resembling both static and convolutive mixtures, but essentially different from both: On one hand, static-mixture approaches cannot be readily applied in this context; On the other hand, conventional convolutive-mixture approach...
متن کاملNeural Network Based Blind Source Separation of Non-linear Mixtures
In this paper we present a novel neural network topology capable of separating simultaneous signals transferred through a memoryless non-linear path. The employed neural network is a two-layer perceptron that uses parametric non-linearities in the hidden neurons. The non-linearities are formed using a mixture of sigmoidal non-linear functions and present greater adaptation towards separating co...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Signal Processing Letters
سال: 2012
ISSN: 1070-9908,1558-2361
DOI: 10.1109/lsp.2011.2181945