Abstract
In traditional compressed sensing theory, the authors have to adopt a discretisation procedure to reduce the continuous parameter space to a finite set of grid points. The discretisation procedure will cause the off-grid effect, which result in the reconstruction result unstable. To solve this problem, an alternating iteration algorithm is proposed which is built on the greedy-like algorithms. Firstly, a multilayer first-order approximation model is proposed to approximate the true observation model in the off-grid model. Then, an alternating iteration algorithm is proposed to estimate the sparse signal and mismatch factor. Moreover, the computational complexities of the proposed algorithm with different greedy algorithms are discussed. Finally, simulations demonstrate the good performance of the proposed algorithm. Meanwhile, the performance of the proposed method will not be influenced by the correlation of dictionary matrix and mismatch matrix.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
