Abstract
Subspace pursuit (SP) algorithm provides the two most important features of greedy algorithms. It has lowest computational complexity in comparison to other greedy algorithms such as OMP and ROMP. It provides the reconstruction quality of same order as that of linear programming techniques for very sparse signals. SP algorithm estimates the sparse signal step by step, in an iterative fashion. The presented analysis shows that SP exactly recovers the sparse signals for temporally correlated data collected during real-time experimentation using NI WSN platform. The efficacy of recovered signal is analyzed using the parameters of peak signal to noise ratio and root mean square error.
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.
