Dynamic Filtering of Time-Varying Sparse Signals via Minimization
AS Charles, A Balavoine… - IEEE Transactions on …, 2016 - ieeexplore.ieee.org
Despite the importance of sparsity signal models and the increasing prevalence of high-
dimensional streaming data, there are relatively few algorithms for dynamic filtering of …
dimensional streaming data, there are relatively few algorithms for dynamic filtering of …
Distributed greedy pursuit algorithms
For compressed sensing over arbitrarily connected networks, we consider the problem of
estimating underlying sparse signals in a distributed manner. We introduce a new signal …
estimating underlying sparse signals in a distributed manner. We introduce a new signal …
Sequential Bayesian sparse signal reconstruction using array data
In this paper, the sequential reconstruction of source waveforms under a sparsity constraint
is considered from a Bayesian perspective. Let the wave field, which is observed by a …
is considered from a Bayesian perspective. Let the wave field, which is observed by a …
Centralized and distributed online learning for sparse time-varying optimization
SM Fosson - IEEE Transactions on Automatic Control, 2020 - ieeexplore.ieee.org
The development of online algorithms to track time-varying systems has drawn a lot of
attention in the last years, in particular in the framework of online convex optimization …
attention in the last years, in particular in the framework of online convex optimization …
Design and analysis of a greedy pursuit for distributed compressed sensing
We consider a distributed compressed sensing scenario where many sensors measure
correlated sparse signals and the sensors are connected through a network. Correlation …
correlated sparse signals and the sensors are connected through a network. Correlation …
Iterated Extended Kalman Smoother-Based Variable Splitting for -Regularized State Estimation
In this paper, we propose a new framework for solving state estimation problems with an
additional sparsity-promoting L 1-regularizer term. We first formulate such problems as …
additional sparsity-promoting L 1-regularizer term. We first formulate such problems as …
Sparse Bayesian learning with dynamic filtering for inference of time-varying sparse signals
Many signal processing applications require estimation of time-varying sparse signals,
potentially with the knowledge of an imperfect dynamics model. In this paper, we propose an …
potentially with the knowledge of an imperfect dynamics model. In this paper, we propose an …
Methods for distributed compressed sensing
Compressed sensing is a thriving research field covering a class of problems where a large
sparse signal is reconstructed from a few random measurements. In the presence of several …
sparse signal is reconstructed from a few random measurements. In the presence of several …
Online optimization in dynamic environments: a regret analysis for sparse problems
SM Fosson - 2018 IEEE Conference on Decision and Control …, 2018 - ieeexplore.ieee.org
Time-varying systems are a challenge in many scientific and engineering areas. Usually,
estimation of time-varying parameters or signals must be performed online, which calls for …
estimation of time-varying parameters or signals must be performed online, which calls for …
Adaptive equalization based on dynamic compressive sensing for single-carrier multiple-input multiple-output underwater acoustic communications
The sparse property of a direct adaptive equalizer (DAE) for single-carrier underwater
acoustic communications is well recognized. It has been used to improve the performance …
acoustic communications is well recognized. It has been used to improve the performance …