A Dynamical System With Fixed Convergence Time for Sparse Recovery

The sparse recovery (SR) algorithm, under the premise that signals are sparse, can be divided into two categories. One is a digital discrete method implemented via lots of iterative computations and the other is a continuous method implemented via analog circuits, which is usually faster. In this pa...

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Bibliographic Details
Main Authors: Junying Ren, Lei Yu, Yulun Jiang, Jean-Pierre Barbot, Hong Sun
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/8638783/
Description
Summary:The sparse recovery (SR) algorithm, under the premise that signals are sparse, can be divided into two categories. One is a digital discrete method implemented via lots of iterative computations and the other is a continuous method implemented via analog circuits, which is usually faster. In this paper, we focus on the continuous method and propose a fixed-time convergence dynamical system. Compared with the existing system, it dynamically allocates the exponent according to time-varying elements of the system state, avoiding possible mismatches between the fixed exponent and some elements.
ISSN:2169-3536