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1Sparse Recovery of Streaming Signals Using `1Homotopy
"... Most of the existing methods for sparse signal recovery assume a static system: the unknown signal is a finitelength vector for which a fixed set of linear measurements and a sparse representation basis are available and an `1norm minimization program is solved for the reconstruction. However, the ..."
Abstract

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Most of the existing methods for sparse signal recovery assume a static system: the unknown signal is a finitelength vector for which a fixed set of linear measurements and a sparse representation basis are available and an `1norm minimization program is solved for the reconstruction. However, the same representation and reconstruction framework is not readily applicable in a streaming system: the unknown signal changes over time, and it is measured and reconstructed sequentially over small time intervals. A streaming framework for the reconstruction is particularly desired when dividing a streaming signal into disjoint blocks and processing each block independently is either infeasible or inefficient. In this paper, we discuss two such streaming systems and a homotopybased algorithm for quickly solving the associated weighted `1norm minimization programs: 1) Recovery of a smooth, timevarying signal for which, instead of using block transforms, we use lapped orthogonal transforms for sparse representation. 2) Recovery of a sparse, timevarying signal that follows a linear dynamic model. For both the systems, we iteratively process measurements over a sliding interval and solve a weighted `1norm minimization problem for estimating sparse coefficients. Since we estimate overlapping portions of the streaming signal while adding and removing measurements, instead of solving a new `1 program