Abstract
At present, most of the detection algorithms used in our country take the iteration process of feature as the research object. This detection method is only suitable for the presence of perceptual signals, but not for all the signal measurement work. This paper introduces the basic principle of signal compression sensing, the construction of measurement matrix and the orthogonal matching pursuit algorithm. The orthogonal matching pursuit algorithm is applied to compressed sensing reconstruction of sparse signals in one-dimensional time domain and transform domain, and the reconstruction performance of the orthogonal matching pursuit algorithm is analyzed. Compared with the detection algorithm based on matching pursuit, this algorithm based on the idea of orthogonal matching pursuit corrects the feature quantities as the basis of decision. When the signal of interest exists, the feature quantities with smaller fluctuations are obtained, and better detection results are obtained. The experimental results show that the OMP detection algorithm proposed in this paper has better performance in improving detection success rate, sampling points required, noise suppression and so on compared with MP detection algorithm.
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 callMore From: International Journal of Wireless Information Networks
Disclaimer: 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.
