Rajib Lochan Das

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—The generalized Orthogonal Matching Pursuit (gOMP) is a recently proposed compressive sensing greedy recovery algorithm which generalizes the OMP algorithm by selecting atoms in each iteration. In this letter, we demonstrate that the gOMP can successfully reconstruct a-sparse signal from a compressed measurement by a maximum of iterations if the sensing(More)
—In this paper, a new convergence analysis is presented for a well-known sparse adaptive filter family, namely, the proportionate type normalized least mean square (PtNLMS) algorithms, where, unlike all the existing approaches, no assumption of whiteness is made on the input. The analysis relies on a " transform " domain based model of the PtNLMS algorithms(More)
The proportionate normalized least mean square (PNLMS) algorithm and its variants are by far the most popular adaptive filters that are used to identify sparse systems. The convergence speed of the PNLMS algorithm, though very high initially, however, slows down at a later stage, even becoming worse than sparsity agnostic adaptive filters like the NLMS. In(More)
— In this paper, we present a new algorithm for sparse adaptive filtering, drawing from the ideas of a greedy compressed sensing recovery technique called the iterative hard thresholding (IHT) and the concepts of affine projection. While usage of affine projection makes it robust against colored input, the use of IHT provides a remarkable improvement in(More)
—Generalized Orthogonal Matching Pursuit (gOMP) is a natural extension of OMP algorithm where unlike OMP, it may select N (≥ 1) atoms in each iteration. In this paper, we demonstrate that gOMP can successfully reconstruct a K-sparse signal from a compressed measurement y = Φx by K th iteration if the sensing matrix Φ satisfies restricted isometry property(More)
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