Feedback Acquisition And Reconstruction Of Spectrum-Sparse Signals By Predictive Level Comparisons

Keywords

1-Bit compressive sensing (CS); binary-sparse error correction; level comparison (LC) sign measurements; sparse signal acquisition

Abstract

In this letter, we propose a sparsity promoting feedback acquisition and reconstruction scheme for sensing, encoding and subsequent reconstruction of spectrally sparse signals. In the proposed scheme, the spectral components are estimated utilizing a sparsity-promoting, sliding-window algorithm in a feedback loop. Utilizing the estimated spectral components, a level signal is predicted and sign measurements of the prediction error are acquired. The sparsity promoting algorithm can then estimate the spectral components iteratively from the sign measurements. Unlike many batch-based compressive sensing algorithms, our proposed algorithm gradually estimates and follows slow changes in the sparse components utilizing a sliding-window technique. We also consider the scenario in which possible flipping errors in the sign bits propagate along iterations (due to the feedback loop) during reconstruction. We propose an iterative error correction algorithm to cope with this error propagation phenomenon considering a binary-sparse occurrence model on the error sequence. Simulation results show effective performance of the proposed scheme in comparison with the literature.

Publication Date

4-1-2018

Publication Title

IEEE Signal Processing Letters

Volume

25

Issue

4

Number of Pages

496-500

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/LSP.2018.2801836

Socpus ID

85041673386 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85041673386

This document is currently not available here.

Share

COinS