Abstract
We propose a technique of multisensor signal reconstruction based on the assumption, that source signals are spatially sparse, as well as have sparse [wavelet-type] representation in time domain. This leads to a large scale convex optimization problem, which involves l 1 norm minimization. The optimization is carried by the Truncated Newton method, using preconditioned Conjugate Gradients in inner iterations. The byproduct of reconstruction is the estimation of source locations.
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Model, D., Zibulevsky, M. (2004). Signal Reconstruction in Sensor Arrays Using Temporal-Spatial Sparsity Regularization. In: Puntonet, C.G., Prieto, A. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2004. Lecture Notes in Computer Science, vol 3195. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30110-3_41
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DOI: https://doi.org/10.1007/978-3-540-30110-3_41
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