# Dynamic Iterative Pursuit

@article{Zachariah2012DynamicIP, title={Dynamic Iterative Pursuit}, author={Dave Zachariah and Saikat Chatterjee and M. Jansson}, journal={IEEE Transactions on Signal Processing}, year={2012}, volume={60}, pages={4967-4972} }

For compressive sensing of dynamic sparse signals, we develop an iterative pursuit algorithm. A dynamic sparse signal process is characterized by varying sparsity patterns over time/space. For such signals, the developed algorithm is able to incorporate sequential predictions, thereby providing better compressive sensing recovery performance, but not at the cost of high complexity. Through experimental evaluations, we observe that the new algorithm exhibits a graceful degradation at…

## 24 Citations

Distributed predictive subspace pursuit

- Computer Science2013 IEEE International Conference on Acoustics, Speech and Signal Processing
- 2013

In a compressed sensing setup with jointly sparse, correlated data, a distributed greedy algorithm called distributed predictive subspace pursuit is developed, based on estimates from neighboring sensor nodes, which provides better performance than current state-of-the-art algorithms.

Variational Bayesian dynamic compressive sensing

- Computer Science2016 IEEE International Symposium on Information Theory (ISIT)
- 2016

This paper proposes a computationally efficient mean-field variational Bayes algorithm to learn the dynamic compressed sensing model without parameter tuning, and considers both the online and offline scenarios.

Online convex optimization meets sparsity

- Computer Science
- 2017

A new perspective on tracking time-varying sparse signals is proposed, based on the theory on online convex optimization, which has been developed in the machine learning community, and a strongly convex model is exploited.

Methods for Distributed Compressed Sensing

- Computer ScienceJ. Sens. Actuator Networks
- 2014

An overview of the current literature regarding distributed compressed sensing is provided, in particular, aspects of network topologies, signal models and recovery algorithms are discussed.

Dynamic Filtering of Time-Varying Sparse Signals via $\ell _1$ Minimization

- Computer ScienceIEEE Transactions on Signal Processing
- 2016

Two algorithms for dynamic filtering of sparse signals that are based on efficient ℓ1 optimization methods that represent the first strong performance analysis of dynamic filtering algorithms for time-varying sparse signals as well as state-of-the-art performance in this emerging application.

Re-Weighted l_1 Dynamic Filtering for Time-Varying Sparse Signal Estimation

- Computer Science
- 2012

This work presents a re-weighted l_1 dynamic filtering scheme for causal signal estimation that utilizes both sparsity assumptions and dynamic structure and incorporates both dynamic and sparsity priors in the estimation procedure in a robust and efficient algorithm.

Design and Analysis of a Greedy Pursuit for Distributed Compressed Sensing

- Computer ScienceIEEE Transactions on Signal Processing
- 2016

This paper develops a distributed parallel pursuit (dipp) algorithm based on exchange of information about estimated support-sets at sensors, which converges to a performance level that depends on a scaled additive measurement noise power (convergence in theory) where the scaling coefficient is a function of RIP parameters and information processing quality parameters.

Greedy Algorithms for Distributed Compressed Sensing

- Computer Science
- 2014

Compressed sensing (CS) is a recently invented sub-sampling technique that utilizes sparsity in full signals. Most natural signals possess this sparsity property. From a sub-sampled vector, some CS…

Sparse Bayesian Learning With Dynamic Filtering for Inference of Time-Varying Sparse Signals

- Computer ScienceIEEE Transactions on Signal Processing
- 2020

Numerical simulations show that SBL-DF converges much faster and to more accurate solutions than standard SBL and other dynamical filtering algorithms, and outperforms state of the art algorithms when the dictionary contains the challenging coherence and column scaling structure found in many practical applications.

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