A recursive way for sparse reconstruction of parametric spaces

Date
2015-11
Advisor
Instructor
Source Title
Conference Record - Asilomar Conference on Signals, Systems and Computers
Print ISSN
Electronic ISSN
Publisher
IEEE
Volume
Issue
Pages
637 - 641
Language
English
Type
Conference Paper
Journal Title
Journal ISSN
Volume Title
Abstract

A novel recursive framework for sparse reconstruction of continuous parameter spaces is proposed by adaptive partitioning and discretization of the parameter space together with expectation maximization type iterations. Any sparse solver or reconstruction technique can be used within the proposed recursive framework. Experimental results show that proposed technique improves the parameter estimation performance of classical sparse solvers while achieving Cramér-Rao lower bound on the tested frequency estimation problem. © 2014 IEEE.

Course
Other identifiers
Book Title
Keywords
Basis mismatch, Compressive sensing, Off-grid targets, Recursive solver, Parse reconstruction, Channel estimation, Compressed sensing, Maximum principle, Sparse reconstruction, Frequency estimation
Citation
Published Version (Please cite this version)