A novel aliasing-free subband information fusion approach for wideband sparse spectral estimation

Ji-An Luo; Xiao-Ping Zhang; Zhi Wang

doi:10.1186/s13634-017-0494-8

A novel aliasing-free subband information fusion approach for wideband sparse spectral estimation

EURASIP J Adv Signal Process. 2017;2017(1):61. doi: 10.1186/s13634-017-0494-8. Epub 2017 Aug 23.

Authors

Ji-An Luo¹, Xiao-Ping Zhang², Zhi Wang³

Affiliations

¹ 1Key Lab for IOT and Information Fusion Technology of Zhejiang, Hangzhou Dianzi University, Hangzhou, 310018 China.
² 2Department of Electrical & Computer Engineering, Ryerson University, 350 Victoria Street, Toronto, Ontario, M5B 2K3 Canada.
³ 3State Key Lab of Industrial Control Technology, Zhejiang University, Hangzhou, 310027 China.

Abstract

Wideband sparse spectral estimation is generally formulated as a multi-dictionary/multi-measurement (MD/MM) problem which can be solved by using group sparsity techniques. In this paper, the MD/MM problem is reformulated as a single sparse indicative vector (SIV) recovery problem at the cost of introducing an additional system error. Thus, the number of unknowns is reduced greatly. We show that the system error can be neglected under certain conditions. We then present a new subband information fusion (SIF) method to estimate the SIV by jointly utilizing all the frequency bins. With orthogonal matching pursuit (OMP) leveraging the binary property of SIV's components, we develop a SIF-OMP algorithm to reconstruct the SIV. The numerical simulations demonstrate the performance of the proposed method.