Teke, OğuzhanGürbüz, A. C.Arıkan, Orhan2016-02-082016-02-082015-11http://hdl.handle.net/11693/28485Date of Conference: 2-5 Nov. 2014Conference name: 48th Asilomar Conference on Signals, Systems and Computers, 2014A 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.EnglishBasis mismatchCompressive sensingOff-grid targetsRecursive solverParse reconstructionChannel estimationCompressed sensingMaximum principleSparse reconstructionFrequency estimationConference Paper10.1109/ACSSC.2014.7094524