Compressed sensing for wireless communications: Useful tips and tricks
As a paradigm to recover the sparse signal from a small set of linear measurements,
compressed sensing (CS) has stimulated a great deal of interest in recent years. In order to …
compressed sensing (CS) has stimulated a great deal of interest in recent years. In order to …
Recursive recovery of sparse signal sequences from compressive measurements: A review
In this overview article, we review the literature on design and analysis of recursive
algorithms for reconstructing a time sequence of sparse signals from compressive …
algorithms for reconstructing a time sequence of sparse signals from compressive …
Sparse signal recovery with temporally correlated source vectors using sparse Bayesian learning
We address the sparse signal recovery problem in the context of multiple measurement
vectors (MMV) when elements in each nonzero row of the solution matrix are temporally …
vectors (MMV) when elements in each nonzero row of the solution matrix are temporally …
Unbiased finite impluse response filtering: An iterative alternative to Kalman filtering ignoring noise and initial conditions
If a system and its observation are both represented in state space with linear equations, the
system noise and the measurement noise are white, Gaussian, and mutually uncorrelated …
system noise and the measurement noise are white, Gaussian, and mutually uncorrelated …
Modified-CS: Modifying compressive sensing for problems with partially known support
We study the problem of reconstructing a sparse signal from a limited number of its linear
projections when a part of its support is known, although the known part may contain some …
projections when a part of its support is known, although the known part may contain some …
P2C2: Programmable pixel compressive camera for high speed imaging
We describe an imaging architecture for compressive video sensing termed programmable
pixel compressive camera (P2C2). P2C2 allows us to capture fast phenomena at frame rates …
pixel compressive camera (P2C2). P2C2 allows us to capture fast phenomena at frame rates …
Online Adaptive Estimation of Sparse Signals: Where RLS Meets the -Norm
Using the ℓ 1-norm to regularize the least-squares criterion, the batch least-absolute
shrinkage and selection operator (Lasso) has well-documented merits for estimating sparse …
shrinkage and selection operator (Lasso) has well-documented merits for estimating sparse …
Block-based compressed sensing of images and video
A number of techniques for the compressed sensing of imagery are surveyed. Various
imaging media are considered, including still images, motion video, as well as multiview …
imaging media are considered, including still images, motion video, as well as multiview …
Dynamic compressive sensing of time-varying signals via approximate message passing
In this work the dynamic compressive sensing (CS) problem of recovering sparse,
correlated, time-varying signals from sub-Nyquist, non-adaptive, linear measurements is …
correlated, time-varying signals from sub-Nyquist, non-adaptive, linear measurements is …
An iterative Kalman-like algorithm ignoring noise and initial conditions
YS Shmaliy - IEEE Transactions on Signal Processing, 2011 - ieeexplore.ieee.org
We address a p-shift finite impulse response (FIR) unbiased estimator (UE) for linear
discrete time-varying filtering (p= 0), p-step prediction (p>; 0), and p-lag smoothing (p<; 0) in …
discrete time-varying filtering (p= 0), p-step prediction (p>; 0), and p-lag smoothing (p<; 0) in …