Zero-point attracting projection algorithm for sequential compressive sensing

TL;DR

This paper introduces a new online algorithm for sequential compressive sensing that improves sparse signal recovery by using techniques like warm start, fast iteration, and variable step size, outperforming existing methods.

Contribution

It proposes an innovative online recovery algorithm with enhanced performance for sequential compressive sensing, incorporating multiple optimization techniques.

Findings

Demonstrates superior recovery accuracy in simulations

Achieves faster convergence compared to existing algorithms

Effective in real-time sparse signal reconstruction

Abstract

Sequential Compressive Sensing, which may be widely used in sensing devices, is a popular topic of recent research. This paper proposes an online recovery algorithm for sparse approximation of sequential compressive sensing. Several techniques including warm start, fast iteration, and variable step size are adopted in the proposed algorithm to improve its online performance. Finally, numerical simulations demonstrate its better performance than the relative art.