Abstract
In this paper, we suggest a neural network signal detector using radial basis function network for detecting a known signal in presence of Gaussian and non-Gaussian noise. We employ this RBF neural detector to detect the presence or absence of a known signal corrupted by different Gaussian and non-Gaussian noise components. In case of non-Gaussian noise, computer simulation results show that RBF network signal detector has significant improvement in performance characteristics. Detection capability is better than to those obtained with multilayer perceptrons and optimum matched filter detector
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
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.