Sparse Representation and Blind Deconvolution of Dynamical Systems
نویسندگان
چکیده
In this paper, we discuss blind deconvolution of dynamical systems, described by the state space model. First we formulate blind deconvolution problem in the framework of the state space model. The blind deconvolution is fulfilled in two stages: internal representation and signal separation. We employ two different learning strategies for training the parameters in the two stages. A sparse representation approach is presented based on the independent decomposition. Some properties of the sparse representation approach are discussed. The natural gradient algorithm is used to train the external parameters in the stage of signal separation. The two-stage approach provides a new insight into blind deconvolution in the state-space framework. Finally a computer simulation is given to show the validity and effectiveness of the state-space approach.
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