Dynamic Iterative Pursuit

TL;DR

This paper introduces a dynamic iterative pursuit algorithm for compressive sensing of time-varying sparse signals, improving recovery performance by leveraging sequential predictions with low complexity.

Contribution

The paper presents a novel iterative pursuit algorithm that effectively incorporates sequential predictions for dynamic sparse signals, enhancing recovery performance without high computational costs.

Findings

Algorithm shows graceful degradation under poor signal conditions.

Significant performance gains observed with improved signal conditions.

Low complexity compared to existing methods.

Abstract

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 deteriorating signal conditions while capable of yielding substantial performance gains as conditions improve.